══════════════════════════════════════════════════════════════════════════════╗ ⚔️ ORFEAS AI 2D→3D STUDIO - COPILOT PROTOCOL ⚔️ â•' â•' â•' PROJECT: ORFEAS AI 3D Studio â•' â•' STACK: Python + Flask + Hunyuan3D-2.1 + WebGL + Docker â•' PURPOSE: Production AI-Powered 2D→3D Model Generation Platform â•' DESIGN: Secure, Efficient, Scalable, Maintainable â•' â•' ══════════════════════════════════════════════════════════════════════════════

ORFEAS AI 2D→3D STUDIO - GITHUB COPILOT INSTRUCTIONS

⚠️ CRITICAL FILE ORGANIZATION RULES

ALL .md FILES MUST BE PLACED IN THE

md\

DIRECTORY

✅ CORRECT:
```
md\SESSION_REPORT.md
```
❌ WRONG:
```
SESSION_REPORT.md
```
(root directory)

ALL .txt FILES MUST BE PLACED IN THE

txt\

DIRECTORY

✅ CORRECT:
```
txt\VISUAL_GUIDE.txt
```
❌ WRONG:
```
VISUAL_GUIDE.txt
```
(root directory)

[1] PROJECT IDENTITY

PROJECT: ORFEAS AI 2D→3D Studio PURPOSE: Enterprise-grade AI-powered multimedia platform for comprehensive AI content generation including 2D→3D model conversion, AI image creation from text prompts, advanced text-to-image synthesis, real-time speech-to-text transcription, cinematic video composition, intelligent code development, and automated DevOps with enterprise HTML5 interfaces, zero-trust security, and cloud-native technologies REPOSITORY: Tencent-Hunyuan/Hunyuan3D-2.1 (upstream), enterprise ORFEAS integration with Sora-class video generation

CORE ARCHITECTURE: This is an enterprise-grade Flask backend + cloud-native HTML5/JavaScript frontend + zero-trust SSL/HTTPS + PWA system that integrates Tencent's Hunyuan3D-2.1 AI model for professional 3D asset generation from 2D images or text prompts, DALL-E 3 and Stable Diffusion XL for high-quality AI image creation from text descriptions, Whisper and Azure Speech Services for real-time speech-to-text transcription and advanced voice processing, plus advanced AI video composition capabilities inspired by OpenAI's Sora for cinematic video creation, intelligent code development platforms, and automated DevOps workflows, all delivered through secure, scalable progressive web applications with enterprise-grade monitoring and observability.

TECHNOLOGY STACK:

Backend: Python 3.10+ with Flask 2.3.3, FastAPI, Gunicorn
Web Technologies: HTML5, CSS3, JavaScript ES6+, PWA (Progressive Web App)
Security: SSL/TLS certificates, HTTPS enforcement, secure headers
Alternative Backend: PHP 8.0+ support for legacy integrations
AI Engine: Hunyuan3D-2.1 (shape + texture generation)
Video AI Engine: Sora-inspired video composition and generation
Code Development AI: GitHub Copilot, CodeT5+, StarCoder integration
Text-to-Image AI: DALL-E 3, Stable Diffusion XL, Midjourney API integration
Text-to-Speech AI: ElevenLabs, Azure Cognitive Speech, OpenAI TTS
Speech-to-Text AI: Whisper OpenAI, Azure Speech Services, Google Speech-to-Text
3D Processing: trimesh, Open3D, numpy-stl, PyMeshLab
Video Processing: OpenCV, FFmpeg, MediaPipe
GPU: PyTorch 2.0.1 with CUDA support (RTX 3090 optimized)
Frontend: HTML5 + Vanilla JS + WebGL/Three.js + BabylonJS
Web Server: Nginx with SSL termination, Apache mod_wsgi support
Container: Docker Compose with GPU acceleration + HTTPS, Kubernetes support
Cloud Platform: AWS, Azure, GCP compatible deployment
API: RESTful APIs, GraphQL, WebSocket real-time communication
Database: PostgreSQL, Redis, MongoDB support
Message Queue: RabbitMQ, Apache Kafka for enterprise workflows
Monitoring: Prometheus + Grafana + Redis + ELK Stack
CI/CD: GitHub Actions, Jenkins, GitLab CI integration
Testing: pytest with 26+ security tests, Selenium E2E testing

MULTI-ENCODING & INTERNATIONALIZATION STACK:

Character Encoding Support:
- UTF-8 (primary encoding with BOM detection)
- UTF-16 LE/BE (Windows and Unicode standard)
- UTF-32 LE/BE (full Unicode character set)
- ASCII (7-bit compatibility mode)
- Latin-1 (ISO-8859-1) for legacy systems
- CP1252 (Windows-1252) for Windows legacy
- GB2312 & GBK (Simplified Chinese)
- Big5 (Traditional Chinese)
- Shift-JIS (Japanese)
- EUC-KR (Korean)
- ISO-8859 series (European languages)
Encoding Detection & Conversion:
- chardet (automatic encoding detection)
- codecs (Python native encoding handlers)
- ftfy (fixes mojibake and encoding errors)
- unicodedata (Unicode normalization)
- charset-normalizer (advanced encoding detection)
Internationalization (i18n) Framework:
- Flask-Babel (internationalization for Flask)
- gettext (GNU internationalization utilities)
- babel (Python internationalization library)
- langdetect (language detection from text)
- polyglot (multilingual NLP toolkit)
- langcodes (language code standardization)

MULTI-CODE LANGUAGE PROGRAMMING STACK:

Primary Development Languages:
- Python 3.10+ (primary backend, AI/ML processing)
- JavaScript ES6+ (frontend, Node.js backend)
- TypeScript 4.9+ (type-safe JavaScript development)
- HTML5 + CSS3 (modern web interfaces)
- SQL (PostgreSQL, MySQL, SQLite)
Enterprise Programming Languages:
- PHP 8.0+ (legacy system integration)
- Java 17+ (enterprise backend services)
- C# .NET 6+ (Microsoft ecosystem integration)
- Go 1.19+ (high-performance microservices)
- Rust (system programming, WebAssembly)
- C++ (GPU computing, performance-critical code)
Specialized Programming Languages:
- R (statistical computing, data science)
- MATLAB (scientific computing, simulations)
- Swift (iOS/macOS native applications)
- Kotlin (Android native, JVM interop)
- Dart (Flutter cross-platform apps)
- WebAssembly (WASM) (high-performance web applications)
Infrastructure & DevOps Languages:
- Shell/Bash (Linux automation scripts)
- PowerShell (Windows automation)
- YAML (configuration files, CI/CD)
- JSON (data interchange, APIs)
- XML (enterprise data formats)
- TOML (configuration files)
Database Query Languages:
- SQL (ANSI SQL standard)
- NoSQL Query Languages (MongoDB, Cassandra)
- GraphQL (API query language)
- SPARQL (RDF query language)
Code Analysis & Cross-Language Integration:
- Language Server Protocol (LSP) integration
- Tree-sitter (universal syntax highlighting)
- ANTLR (cross-language parser generator)
- Protocol Buffers (cross-language serialization)
- gRPC (cross-language RPC framework)
- Apache Thrift (cross-language services)
Multi-Language AI Code Generation:
- GitHub Copilot (multi-language code completion)
- OpenAI Codex (cross-language code generation)
- CodeT5+ (code translation between languages)
- StarCoder (open-source multi-language model)
- DeepSeek-Coder (advanced multi-language understanding)
- Tabnine (AI-powered multi-language completion)

ENTERPRISE AI & ML ECOSYSTEM:

Primary AI Engine: Hunyuan3D-2.1 (Tencent's enterprise-grade 2D→3D generation)
Cinematic AI Engine: Sora-class enterprise video composition and cinematic generation
Intelligent Code Development Platform: Enterprise code writing, automated debugging, and performance optimization
Text-to-Image AI Engine: Enterprise-grade image generation from text prompts with DALL-E 3, Stable Diffusion XL, and Midjourney API
Text-to-Speech AI Engine: High-fidelity voice synthesis with ElevenLabs, Azure Cognitive Speech, and OpenAI TTS
Speech-to-Text AI Engine: Advanced speech recognition with Whisper OpenAI, Azure Speech Services, and Google Speech-to-Text

LARGE LANGUAGE MODEL (LLM) INTEGRATION:

Foundation Models Stack:
- GPT-4 Turbo & GPT-4o (OpenAI enterprise API integration)
- Claude 3.5 Sonnet & Claude 3 Opus (Anthropic enterprise models)
- Gemini Ultra & Gemini Pro (Google enterprise LLM suite)
- LLaMA 3.1 405B & 70B (Meta's open-source enterprise models)
- Mistral 8x22B & Mixtral 8x7B (Mistral AI enterprise deployment)
- Command R+ & Command (Cohere enterprise language models)
- PaLM 2 & Bard Enterprise (Google Cloud AI integration)
Specialized LLM Applications:
- Code Generation & Review (CodeLlama 34B, StarCoder2, DeepSeek-Coder V2)
- Technical Documentation (Claude 3 Haiku for rapid content generation)
- Multimodal Understanding (GPT-4 Vision, Gemini Pro Vision, Claude 3 Opus)
- Reasoning & Analysis (GPT-4o for complex problem solving)
- Conversational AI (ChatGPT Enterprise, Claude Enterprise)
LLM Optimization & Deployment:
- vLLM & Text Generation Inference (TGI) for high-throughput serving
- NVIDIA TensorRT-LLM for GPU-optimized inference
- Hugging Face Transformers with custom fine-tuning pipelines
- LoRA & QLoRA for efficient model adaptation
- Quantization (AWQ, GPTQ, SmoothQuant) for memory optimization
- Model parallelism & distributed inference across multiple GPUs
Enterprise LLM Features:
- Custom fine-tuning on proprietary datasets
- RAG (Retrieval-Augmented Generation) with vector databases
- Function calling & tool integration
- Chain-of-thought prompting & reasoning
- Multi-agent LLM orchestration
- Enterprise-grade safety & content filtering

ADVANCED MACHINE LEARNING STACK:

Deep Learning Frameworks:
- PyTorch 2.1+ Lightning (primary framework with advanced optimizations)
- TensorFlow Enterprise 2.15+ (production-grade deployment)
- JAX & Flax (Google's high-performance ML framework)
- Hugging Face Transformers 4.35+ (state-of-the-art NLP models)
- Diffusers Pro (advanced diffusion model implementations)
- Stable Baselines3 (reinforcement learning algorithms)
Computer Vision & Multimodal AI:
- OpenCV 4.8+ Professional (advanced computer vision operations)
- MediaPipe Pro (real-time perception pipelines)
- CLIP & ALIGN (vision-language understanding models)
- YOLO v8 & v9 (real-time object detection)
- Segment Anything Model (SAM) for universal segmentation
- REMBG Pro (advanced background removal)
- Detectron2 (Facebook's object detection platform)
Natural Language Processing:
- spaCy Enterprise (industrial-strength NLP)
- NLTK & TextBlob (comprehensive text processing)
- SentenceTransformers (semantic text embeddings)
- Named Entity Recognition (NER) with custom models
- Sentiment analysis & emotion detection
- Text summarization & generation pipelines
Audio & Speech Processing:
- Whisper Enterprise (OpenAI's speech recognition)
- Wav2Vec 2.0 (Facebook's speech representation learning)
- VALL-E X (voice synthesis and cloning)
- Real-time speech-to-text and text-to-speech
- Audio classification & sound event detection
- Music generation with MusicLM integration

ENTERPRISE AI AGENTS & AUTONOMOUS SYSTEMS:

Multi-Agent Frameworks:
- LangChain Enterprise (LLM application development framework)
- LlamaIndex Pro (data framework for LLM applications)
- AutoGen (Microsoft's multi-agent conversation framework)
- CrewAI (role-playing autonomous agent framework)
- Semantic Kernel (Microsoft's AI orchestration platform)
- LangGraph (advanced agent workflow orchestration)
- OpenAI Assistants API (enterprise assistant creation)
- Anthropic Claude Computer Use (computer interaction agents)
- Microsoft Copilot Studio (enterprise bot framework)
- Google Vertex AI Agent Builder (cloud-native agent platform)
Advanced Agent Capabilities:
- Autonomous Task Planning & Execution:
  - Dynamic goal decomposition and hierarchical planning
  - Multi-step workflow orchestration with conditional branching
  - Resource allocation and constraint satisfaction
  - Parallel task execution with dependency management
  - Real-time replanning and adaptation to changing conditions
  - Progress monitoring and milestone tracking
- Multi-Modal Perception & Reasoning:
  - Vision-language understanding and cross-modal reasoning
  - Audio processing and speech recognition/synthesis
  - Document analysis and structured data extraction
  - Real-time sensor data integration and fusion
  - Spatial reasoning and 3D environment understanding
  - Temporal pattern recognition and sequence analysis
- Advanced Tool Use & API Integration:
  - Dynamic tool discovery and capability assessment
  - API schema understanding and automatic integration
  - Function calling with parameter validation and error handling
  - Tool chaining and complex workflow automation
  - External system integration (databases, cloud services, IoT)
  - Custom tool creation and deployment
- Intelligent Memory Management & Knowledge Persistence:
  - Episodic memory for experience-based learning
  - Semantic memory with knowledge graph integration
  - Working memory optimization for real-time processing
  - Long-term memory consolidation and retrieval
  - Context-aware information retention and forgetting
  - Cross-session continuity and knowledge transfer
- Collaborative Problem-Solving & Team Coordination:
  - Multi-agent negotiation and consensus building
  - Role-based specialization and expertise sharing
  - Conflict resolution and decision arbitration
  - Load balancing and task distribution
  - Swarm intelligence and collective behavior
  - Inter-agent communication protocols
- Self-Improvement & Adaptive Learning:
  - Reinforcement learning from human feedback (RLHF)
  - Continuous learning from experience and performance metrics
  - Self-reflection and capability assessment
  - Automated hyperparameter optimization
  - Knowledge distillation and model compression
  - Meta-learning for rapid adaptation to new domains
- Enterprise Security & Compliance:
  - Role-based access control and permission management
  - Audit logging and compliance tracking
  - Data privacy and anonymization protocols
  - Secure communication and encryption
  - Threat detection and anomaly identification
  - Regulatory compliance automation (GDPR, HIPAA, SOX)
Specialized Enterprise Agent Types:
- Development & Engineering Agents:
  - Code review and optimization agents with security scanning
  - Automated testing and quality assurance agents
  - Documentation generation and maintenance agents
  - DevOps and CI/CD pipeline management agents
  - Architecture analysis and recommendation agents
  - Performance profiling and optimization agents
  - Dependency management and security vulnerability agents
- Business Intelligence & Analytics Agents:
  - Data analysis and insight generation agents
  - Report automation and dashboard creation agents
  - Predictive analytics and forecasting agents
  - Market research and competitive analysis agents
  - Customer behavior analysis and segmentation agents
  - Risk assessment and mitigation planning agents
- Operations & Infrastructure Agents:
  - System monitoring and alerting agents
  - Incident response and resolution agents
  - Capacity planning and resource optimization agents
  - Security monitoring and threat response agents
  - Backup and disaster recovery agents
  - Network optimization and troubleshooting agents
- Customer Experience & Support Agents:
  - Intelligent customer support and ticketing agents
  - Personalization and recommendation agents
  - Content creation and marketing automation agents
  - Voice of customer analysis and feedback agents
  - Multi-channel communication orchestration agents
  - Customer journey optimization agents
- Research & Innovation Agents:
  - Literature review and knowledge synthesis agents
  - Hypothesis generation and experimental design agents
  - Patent analysis and IP research agents
  - Technology trend analysis and forecasting agents
  - Innovation pipeline management agents
  - Scientific workflow automation agents
- Financial & Compliance Agents:
  - Financial analysis and reporting automation agents
  - Fraud detection and prevention agents
  - Regulatory compliance monitoring agents
  - Risk management and assessment agents
  - Audit trail and documentation agents
  - Contract analysis and management agents
Agent Orchestration Patterns:
- Hierarchical Agent Architecture:
  - Master agent coordinating specialized sub-agents
  - Chain of command with escalation protocols
  - Resource allocation and priority management
  - Cross-functional team coordination
- Peer-to-Peer Agent Networks:
  - Distributed problem-solving with consensus mechanisms
  - Expertise sharing and knowledge federation
  - Fault tolerance and redundancy management
  - Load balancing and workload distribution
- Event-Driven Agent Systems:
  - Reactive agents responding to system events
  - Event streaming and real-time processing
  - Complex event processing and pattern matching
  - Workflow triggers and automation chains
- Hybrid Human-AI Agent Teams:
  - Human-in-the-loop decision making
  - Augmented intelligence and collaborative workflows
  - Handoff protocols and escalation mechanisms
  - Human oversight and intervention capabilities
Agent Performance & Optimization:
- Performance Metrics & KPIs:
  - Task completion rate and accuracy metrics
  - Response time and throughput optimization
  - Resource utilization and efficiency tracking
  - User satisfaction and interaction quality
  - Learning curve and improvement trajectories
  - Cost-benefit analysis and ROI measurement
- Continuous Improvement Framework:
  - A/B testing for agent behavior optimization
  - Performance benchmarking against industry standards
  - Automated model retraining and deployment
  - Feedback loop integration and learning cycles
  - Version control and rollback capabilities
  - Quality gates and approval workflows

ENTERPRISE COMPUTER VISION STACK:

Core Vision Processing: REMBG, OpenCV 4.8+, PIL/Pillow, MediaPipe Pro
3D Vision & Reconstruction: Open3D, PyMeshLab, trimesh, Point Cloud Library (PCL)
Advanced Image Processing: Scikit-image, ImageIO, Albumentations (data augmentation)
Real-time Processing: NVIDIA DeepStream, Intel OpenVINO, TensorRT
Specialized Models: ESRGAN (super-resolution), Real-ESRGAN, EDSR, SwinIR

PRODUCTION ML FRAMEWORKS:

Core Frameworks: PyTorch 2.1+ Lightning, Transformers 4.35+, Diffusers Pro, TensorFlow Enterprise
Model Training: Accelerate (Hugging Face), DeepSpeed (Microsoft), FairScale (Facebook)
Distributed Training: Horovod, PyTorch Distributed, TensorFlow MultiWorkerMirroredStrategy
Experiment Tracking: Weights & Biases, MLflow, Neptune, TensorBoard Pro

ENTERPRISE AI AGENTS & MLOps:

Agent Infrastructure & Orchestration:
- Kubernetes-native agent orchestration with auto-scaling
- Docker containerized agent deployment
- Service mesh integration (Istio, Linkerd) for agent communication
- Enterprise authentication (OAuth2, SAML, Active Directory)
- Multi-cloud agent deployment (AWS EKS, Azure AKS, GCP GKE)
- Agent lifecycle management and versioning
- Health monitoring and failover mechanisms
- Resource quota management and cost optimization
Agent Communication & Coordination:
- Message bus integration (Apache Kafka, RabbitMQ, Azure Service Bus)
- Event-driven architecture with pub/sub patterns
- gRPC and REST API communication protocols
- GraphQL federation for distributed agent queries
- WebSocket real-time agent coordination
- Circuit breaker patterns for resilient communication
- Rate limiting and backpressure handling
- Distributed tracing and observability
Agent Development & Deployment:
- CI/CD for ML agents (GitHub Actions ML, Jenkins X, GitLab CI/CD, Azure DevOps)
- Automated agent testing and validation pipelines
- Blue-green and canary deployment strategies
- Configuration management and feature flags
- Environment promotion (dev → staging → production)
- Rollback and recovery mechanisms
- Performance testing and load validation
- Security scanning and vulnerability assessment
Agent Model Management:
- MLflow Enterprise for agent model lifecycle
- Kubeflow Pipelines for agent training workflows
- Model registry and versioning for agent brains
- A/B testing frameworks for agent behavior
- Model performance monitoring and drift detection
- Automated retraining and model updates
- Multi-model serving and routing
- Model compression and optimization for edge deployment
Agent Monitoring & Observability:
- Prometheus metrics collection for agent performance
- Grafana dashboards for agent health visualization
- ELK Stack (Elasticsearch, Logstash, Kibana) for log analysis
- Distributed tracing with Jaeger and Zipkin
- APM integration (Datadog, New Relic, AppDynamics)
- Custom agent KPIs and business metrics
- Alerting and incident management
- Performance profiling and bottleneck identification
Specialized Agent Monitoring Tools:
- Evidently AI for agent behavior drift detection
- WhyLabs for agent data quality monitoring
- Arize AI for agent performance observability
- Fiddler AI for agent explainability and fairness
- Weights & Biases for agent experiment tracking
- Neptune for agent metadata management
- TensorBoard for agent training visualization
- MLflow for agent artifact tracking
Agent Security & Compliance:
- Zero-trust security model for agent networks
- Mutual TLS (mTLS) for secure agent communication
- API gateway security and rate limiting
- Secret management (HashiCorp Vault, AWS Secrets Manager)
- RBAC (Role-Based Access Control) for agent permissions
- Audit logging and compliance reporting
- Data encryption at rest and in transit
- Vulnerability scanning and security patching
Agent Data Management:
- Vector databases for agent knowledge storage (Pinecone, Weaviate, Qdrant)
- Graph databases for agent relationship modeling (Neo4j, Amazon Neptune)
- Time-series databases for agent metrics (InfluxDB, TimescaleDB)
- Cache layers for agent response optimization (Redis, Memcached)
- Data lineage tracking for agent decisions
- GDPR and privacy compliance for agent data
- Data versioning and provenance tracking
- Distributed storage for large agent datasets
Agent Performance Optimization:
- GPU acceleration for compute-intensive agents
- Model quantization and pruning for efficiency
- Batching strategies for high-throughput agents
- Caching layers for frequently accessed data
- Load balancing across agent instances
- Horizontal and vertical scaling policies
- Resource allocation optimization
- Cost monitoring and optimization strategies
Enterprise Agent Patterns:
- Microservices architecture for agent components
- Event sourcing for agent state management
- CQRS (Command Query Responsibility Segregation) patterns
- Saga patterns for distributed agent transactions
- Bulkhead patterns for agent isolation
- Timeout and retry patterns for resilience
- Circuit breaker patterns for fault tolerance
- Strangler fig patterns for legacy system integration

MULTI-CLOUD AI ENGINES:

Stable Diffusion XL Enterprise (high-resolution text-to-image)
OpenAI CLIP Enterprise (multimodal understanding)
ControlNet Pro (precision-guided generation)
InstantMesh Enterprise (scalable 3D generation)
DALL-E Integration (premium image generation)
Midjourney API (artistic content generation)

ENTERPRISE CODE INTELLIGENCE PLATFORMS:

GitHub Copilot Enterprise (AI-powered development workflows)
CodeT5+ Enterprise (large-scale code generation and completion)
StarCoder Enterprise (enterprise open-source code generation)
DeepSeek-Coder Pro (advanced debugging and optimization)
Cursor AI Enterprise (intelligent enterprise code editing)
Amazon CodeWhisperer (AWS-native code intelligence)
Tabnine Enterprise (contextual code completion)

PRODUCTION ML OPTIMIZATION STACK:

NVIDIA TensorRT 8.6+ with DLA optimization
CUDA 12.0+ with multi-GPU acceleration and mixed precision (FP16/BF16/INT8)
Intel OpenVINO for CPU inference optimization
ONNX Runtime Enterprise for cross-platform deployment
Apache TVM for hardware-agnostic optimization
Google TPU integration for large-scale training

ENTERPRISE AI WORKFLOW MANAGEMENT:

Kubernetes-native batch inference pipelines with auto-scaling
MLflow Enterprise for model lifecycle management and A/B testing
Kubeflow Pipelines for enterprise ML workflows
Apache Airflow for complex AI workflow orchestration
Ray Serve for distributed model serving and scaling
NVIDIA Triton Inference Server for production model deployment
Automated model selection with reinforcement learning optimization
Enterprise model governance and compliance frameworks

VECTOR DATABASES & KNOWLEDGE MANAGEMENT:

Enterprise Vector Stores:
- Pinecone Enterprise (managed vector database service)
- Weaviate (open-source vector search engine)
- Qdrant (high-performance vector similarity search)
- Chroma (AI-native open-source embedding database)
- Milvus (cloud-native vector database)
- FAISS (Facebook AI Similarity Search) for local deployments
Knowledge Graph Integration:
- Neo4j Enterprise (graph database for connected data)
- Amazon Neptune (fully managed graph database)
- Microsoft Cosmos DB (multi-model database with graph support)
- Apache Jena (semantic web framework)
Retrieval-Augmented Generation (RAG):
- LangChain RAG pipelines with custom retrievers
- LlamaIndex advanced query engines
- Haystack (end-to-end NLP framework)
- Custom embedding models for domain-specific retrieval

REINFORCEMENT LEARNING & OPTIMIZATION:

RL Frameworks:
- Stable Baselines3 (high-quality RL algorithm implementations)
- Ray RLlib (scalable reinforcement learning library)
- OpenAI Gym & Gymnasium (RL environment standards)
- DeepMind Lab & dm_control (advanced RL environments)
Optimization Algorithms:
- Optuna (hyperparameter optimization framework)
- Ray Tune (distributed hyperparameter tuning)
- Weights & Biases Sweeps (experiment management)
- AutoML with Auto-sklearn & Auto-PyTorch

EDGE AI & MOBILE DEPLOYMENT:

Model Optimization:
- TensorFlow Lite (mobile and embedded deployment)
- ONNX Runtime Mobile (cross-platform inference)
- PyTorch Mobile (mobile-optimized PyTorch)
- OpenVINO (Intel's inference optimization toolkit)
Hardware Acceleration:
- NVIDIA Jetson integration (edge AI computing)
- Google Coral TPU (edge machine learning accelerator)
- Apple Core ML (iOS/macOS optimized inference)
- Qualcomm Neural Processing SDK

WEB TECHNOLOGIES & SECURITY STACK:

HTML5 Frontend Architecture:
- Progressive Web App (PWA) with offline support
- Responsive design with CSS Grid and Flexbox
- WebGL/Three.js for 3D model visualization
- BabylonJS for advanced 3D engine features
- Service Worker for caching and background sync
SSL/TLS Security:
- HTTPS enforcement with 301 redirects
- TLS 1.3 with perfect forward secrecy
- SSL certificate auto-renewal via Let's Encrypt
- HSTS headers and secure cookie settings
- CSP (Content Security Policy) implementation
PHP Integration (Legacy Support):
- PHP 8.0+ compatibility layer
- FastCGI/PHP-FPM integration
- Session management and authentication
- Database connectivity (MySQL/PostgreSQL)
- API endpoint compatibility
Web Server Configuration:
- Nginx as reverse proxy with SSL termination
- Apache mod_wsgi for Python WSGI applications
- Static file serving with compression
- Rate limiting and DDoS protection
- Health checks and monitoring endpoints
- Apache mod_wsgi for Python WSGI applications
- Static file serving with compression
- Rate limiting and DDoS protection
- Health checks and monitoring endpoints

âš¡ MULTI-AGENT SYSTEM OPTIMIZATION

AGENT ROLE SPECIALIZATION & COORDINATION:

The ORFEAS platform is optimized for multi-agent collaboration with intelligent task distribution and context sharing across specialized AI agents.

PRIMARY AGENT ROLES:

ORFEAS Platform Specialist: Expert in 3D generation, multimedia AI, and enterprise architecture
Frontend Development Agent: HTML5, JavaScript, CSS, PWA, and WebGL/Three.js specialist
Backend Development Agent: Python, Flask, PyTorch, GPU optimization, and API design expert
DevOps & Infrastructure Agent: Docker, Kubernetes, monitoring, and enterprise deployment specialist
Security & Compliance Agent: Zero-trust security, penetration testing, and regulatory compliance expert
Performance Optimization Agent: GPU memory management, model optimization, and benchmarking specialist
Quality Assurance Agent: TQM audits, testing frameworks, and continuous quality improvement expert

MULTI-AGENT COORDINATION PROTOCOLS:

# Agent communication standards
class AgentMessage:
    def __init__(self, sender_id: str, recipient_id: str, task_type: str, payload: Dict):
        self.message_id = generate_uuid()
        self.sender_id = sender_id
        self.recipient_id = recipient_id
        self.task_type = task_type
        self.payload = payload
        self.context_hash = self.calculate_context_hash()
        self.timestamp = datetime.utcnow()
        self.version = "1.0"

# Task distribution framework
AGENT_TASK_ROUTING = {
    'frontend_development': 'frontend_agent',
    'backend_api_design': 'backend_agent',
    'performance_optimization': 'performance_agent',
    'security_review': 'security_agent',
    'infrastructure_deployment': 'devops_agent',
    'quality_assurance': 'qa_agent',
    'multimedia_ai_integration': 'orfeas_specialist'
}

# Context sharing mechanism
class SharedContext:
    def __init__(self):
        self.project_state = {}
        self.active_tasks = {}
        self.agent_capabilities = {}
        self.performance_metrics = {}
        self.version_control = VersionController()

    async def synchronize_context(self, agent_id: str, local_context: Dict):
        """Synchronize context across all agents"""
        conflict_resolution = await self.detect_conflicts(local_context)
        merged_context = await self.merge_contexts(conflict_resolution)
        await self.broadcast_updates(merged_context, exclude=agent_id)

AGENT-SPECIFIC OPTIMIZATION RULES:

Code Generation Agents:
- Always include Python type hints and comprehensive docstrings
- Implement ORFEAS error handling patterns with automated problem detection
- Include GPU memory management for AI model operations
- Add Prometheus metrics and monitoring integration
- Follow enterprise security patterns with input validation
Testing & QA Agents:
- Generate pytest test suites with 80%+ code coverage
- Include security vulnerability tests and penetration testing scenarios
- Create performance benchmarks and load testing scripts
- Implement TQM audit procedures and quality gates
- Add integration tests for multi-agent coordination
Infrastructure & DevOps Agents:
- Use enterprise Kubernetes deployment patterns with auto-scaling
- Implement zero-downtime deployment strategies
- Configure comprehensive monitoring with Prometheus + Grafana
- Set up enterprise security with SSL/TLS and zero-trust architecture
- Enable disaster recovery and backup automation
Security & Compliance Agents:
- Implement comprehensive input validation and sanitization
- Add enterprise authentication (OAuth2, SAML, LDAP)
- Configure audit logging and compliance tracking
- Perform automated security scanning and vulnerability assessment
- Ensure GDPR, HIPAA, SOC2 compliance requirements

COLLABORATIVE WORKFLOW PATTERNS:

# Multi-agent feature development coordination
async def coordinate_feature_development(feature_spec: Dict) -> Dict:
    """Coordinate multi-agent development workflow"""

    # 1. Primary analysis and task decomposition
    analysis = await orfeas_specialist.analyze_feature_requirements(feature_spec)

    # 2. Intelligent task distribution
    task_assignments = {
        'backend_api': await backend_agent.estimate_effort(analysis['api_requirements']),
        'frontend_ui': await frontend_agent.estimate_effort(analysis['ui_requirements']),
        'security_review': await security_agent.assess_security_impact(analysis),
        'performance_impact': await performance_agent.analyze_performance_impact(analysis),
        'infrastructure_changes': await devops_agent.assess_deployment_impact(analysis)
    }

    # 3. Parallel execution with context sharing
    execution_results = await asyncio.gather(*[
        backend_agent.implement_api(task_assignments['backend_api'], shared_context),
        frontend_agent.implement_ui(task_assignments['frontend_ui'], shared_context),
        security_agent.perform_security_review(task_assignments['security_review'], shared_context),
        performance_agent.optimize_performance(task_assignments['performance_impact'], shared_context),
        devops_agent.prepare_deployment(task_assignments['infrastructure_changes'], shared_context)
    ])

    # 4. Integration and validation
    integrated_feature = await integration_agent.merge_implementations(execution_results)
    validation_results = await qa_agent.validate_feature(integrated_feature, feature_spec)

    # 5. Quality gates and deployment readiness
    if validation_results['quality_score'] >= 0.85:
        deployment_package = await devops_agent.prepare_deployment_package(integrated_feature)
        return {
            'status': 'ready_for_deployment',
            'feature': integrated_feature,
            'deployment_package': deployment_package,
            'quality_metrics': validation_results
        }
    else:
        improvement_plan = await qa_agent.generate_improvement_plan(validation_results)
        return {
            'status': 'requires_improvement',
            'improvement_plan': improvement_plan,
            'quality_issues': validation_results['issues']
        }

# Agent performance optimization
class MultiAgentPerformanceOptimizer:
    def __init__(self):
        self.agent_pool = AgentConnectionPool(max_connections=50)
        self.load_balancer = AgentLoadBalancer()
        self.circuit_breaker = AgentCircuitBreaker()
        self.cache_manager = AgentCacheManager()

    async def optimize_agent_communication(self):
        """Optimize inter-agent communication performance"""
        # Implement intelligent caching
        await self.cache_manager.enable_result_caching()

        # Use connection pooling
        await self.agent_pool.initialize_connections()

        # Apply load balancing
        await self.load_balancer.distribute_agent_load()

        # Enable circuit breakers for resilience
        await self.circuit_breaker.monitor_agent_health()

AGENT DEPLOYMENT & SCALING PATTERNS:

# Kubernetes multi-agent deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: orfeas-agent-cluster
spec:
  replicas: 5
  selector:
    matchLabels:
      app: orfeas-agents
  template:
    metadata:
      labels:
        app: orfeas-agents
    spec:
      containers:
        - name: orfeas-specialist
          image: orfeas/specialist-agent:latest
          resources:
            requests:
              memory: "2Gi"
              cpu: "1000m"
              nvidia.com/gpu: 1
            limits:
              memory: "4Gi"
              cpu: "2000m"
              nvidia.com/gpu: 1
        - name: backend-agent
          image: orfeas/backend-agent:latest
          resources:
            requests:
              memory: "1Gi"
              cpu: "500m"
        - name: frontend-agent
          image: orfeas/frontend-agent:latest
          resources:
            requests:
              memory: "512Mi"
              cpu: "250m"

PROJECT STRUCTURE:

orfeas/
â"œâ"€â"€ backend/                          # Python Flask API server
â"'   â"œâ"€â"€ main.py                       # Primary Flask application (2400+ lines)
â"'   â"œâ"€â"€ hunyuan_integration.py        # Hunyuan3D-2.1 model interface
â"'   â"œâ"€â"€ sora_video_integration.py     # Sora-inspired video composition AI
â"'   â"œâ"€â"€ video_processor.py            # AI video generation and processing
â"'   â"œâ"€â"€ cinematic_composer.py         # Cinematic video composition engine
â"'   â"œâ"€â"€ text_to_image_processor.py    # AI text-to-image generation (DALL-E, SDXL)
â"'   â"œâ"€â"€ text_to_speech_processor.py   # AI text-to-speech synthesis (ElevenLabs, Azure)
â"'   â"œâ"€â"€ speech_to_text_processor.py   # AI speech-to-text recognition (Whisper, Azure)
â"'   â"œâ"€â"€ multimedia_ai_manager.py      # Unified multimedia AI coordination
â"'   â"œâ"€â"€ audio_enhancement.py          # Audio quality enhancement and processing
â"'   â"œâ"€â"€ image_enhancement.py          # Image quality enhancement and upscaling
â"'   â"œâ"€â"€ code_writer.py                # AI-powered intelligent code generation
â"'   â"œâ"€â"€ code_debugger.py              # Automated code debugging and optimization
â"'   â"œâ"€â"€ code_analyzer.py              # Code quality analysis and suggestions
â"'   â"œâ"€â"€ agent_api.py                  # AI agent orchestration and workflows
â"'   â"œâ"€â"€ agent_auth.py                 # Authentication for autonomous agents
â"'   â"œâ"€â"€ gpu_manager.py                # GPU memory and resource management
â"'   â"œâ"€â"€ rtx_optimization.py           # RTX 3090 specific optimizations
â"'   â"œâ"€â"€ stl_processor.py              # Advanced STL mesh processing
â"'   â"œâ"€â"€ batch_processor.py            # Async job queue for concurrent tasks
â"'   â"œâ"€â"€ batch_inference_extension.py  # ML batch processing extensions
â"'   â"œâ"€â"€ huggingface_compat.py         # HuggingFace model compatibility layer
â"'   â"œâ"€â"€ ssl_manager.py                # SSL certificate management and HTTPS
â"'   â"œâ"€â"€ web_security.py               # Web security headers and CSP policies
â"'   â"œâ"€â"€ pwa_manager.py                # Progressive Web App functionality
â"'   â"œâ"€â"€ context_manager.py            # Intelligent context handling and analysis
â"'   â"œâ"€â"€ contextual_agent_coordinator.py # Multi-agent context coordination
â"'   â"œâ"€â"€ contextual_model_selector.py  # Context-aware model selection
â"'   â"œâ"€â"€ contextual_error_handler.py   # Context-based error recovery
â"'   â"œâ"€â"€ context_persistence.py        # Context learning and persistence
â"'   â"œâ"€â"€ tqm_audit_system.py          # Total Quality Management audit system
â"'   â"œâ"€â"€ continuous_quality_monitor.py # Real-time quality monitoring and improvement
â"'   â"œâ"€â"€ automated_audit_scheduler.py  # Automated quality audit scheduling
â"'   â"œâ"€â"€ quality_gateway_middleware.py # Quality gate enforcement middleware
â"'   â"œâ"€â"€ quality_metrics_collector.py  # Comprehensive quality metrics collection
â"'   â"œâ"€â"€ compliance_validator.py       # Standards compliance validation (ISO, SOC2, etc)
â"'   â"œâ"€â"€ quality_improvement_engine.py # Automated quality improvement actions
â"'   â"œâ"€â"€ llm_integration.py            # Enterprise LLM orchestration and management
â"'   â"œâ"€â"€ copilot_enterprise.py         # GitHub Copilot Enterprise integration
â"'   â"œâ"€â"€ llm_router.py                 # Intelligent LLM routing and selection
â"'   â"œâ"€â"€ multi_llm_orchestrator.py     # Multi-LLM task coordination
â"'   â"œâ"€â"€ rag_integration.py            # Retrieval-Augmented Generation system
â"'   â"œâ"€â"€ vector_database_manager.py    # Vector database operations (Pinecone, etc)
â"'   â"œâ"€â"€ knowledge_graph_manager.py    # Knowledge graph integration (Neo4j)
â"'   â"œâ"€â"€ prompt_engineering.py         # Advanced prompt optimization
â"'   â"œâ"€â"€ llm_performance_monitor.py    # LLM performance tracking and optimization
â"'   â"œâ"€â"€ llm_safety_filter.py          # Enterprise LLM safety and content filtering
â"'   â"œâ"€â"€ encoding_manager.py           # Multi-character encoding detection and conversion
â"'   â"œâ"€â"€ encoding_auto_detector.py     # Automatic encoding detection with fallback chains
â"'   â"œâ"€â"€ unicode_normalizer.py         # Unicode normalization and BOM handling
â"'   â"œâ"€â"€ i18n_manager.py               # Internationalization and localization manager
â"'   â"œâ"€â"€ language_detector.py          # Automatic language detection from text
â"'   â"œâ"€â"€ multilingual_processor.py     # Multi-language text processing and NLP
â"'   â"œâ"€â"€ polyglot_code_manager.py      # Multi-programming language code management
â"'   â"œâ"€â"€ code_language_detector.py     # Automatic programming language detection
â"'   â"œâ"€â"€ cross_language_translator.py  # Code translation between programming languages
â"'   â"œâ"€â"€ language_interop_engine.py    # Cross-language interoperability and integration
â"'   â"œâ"€â"€ syntax_validator_multi.py     # Multi-language syntax validation engine
â"'   â"œâ"€â"€ lsp_integration_manager.py    # Language Server Protocol integration
â"'   â"œâ"€â"€ tree_sitter_parser.py         # Universal syntax highlighting and parsing
â"'   â"œâ"€â"€ validation.py                 # Input validation and security
   ├ monitoring.py                 # Application metrics collection
   ├ prometheus_metrics.py         # Prometheus exporter
   ├ config.py                     # Configuration management
   ├ tests/                        # pytest test suite
   └ monitoring_stack/             # Docker monitoring infrastructure
       ├ docker-compose-monitoring.yml
       ├ prometheus.yml
       └ grafana-dashboard.json
├ Hunyuan3D-2.1/                    # Tencent's AI model (Git submodule)
   └ Hunyuan3D-2/
       └ hy3dgen/                  # Core generation pipelines
├ orfeas-studio.html                # Main web interface
├ orfeas-3d-engine-hybrid.js        # 3D viewer (Three.js/BabylonJS)
├ service-worker.js                 # PWA offline support
├ docker-compose.yml                # Multi-service orchestration
├ Dockerfile                        # Production container build
├ START_ORFEAS_AUTO.ps1             # Windows startup script
└ md/                               # Documentation directory

[2] CORE ARCHITECTURE PRINCIPLES

[2.1] HUNYUAN3D-2.1 INTEGRATION

CRITICAL MODEL LOADING:

First initialization: 30-36 seconds (loads 8GB+ models to GPU)
Singleton caching: Subsequent loads <1s (94% faster via
```
_model_cache
```
)
XFORMERS DISABLED:
```
os.environ["XFORMERS_DISABLED"] = "1"
```
prevents DLL crashes (0xc0000139)

Models:

shapegen_pipeline

texgen_pipeline

rembg

text2image_pipeline

CORRECT INITIALIZATION PATTERN:

# backend/hunyuan_integration.py
class Hunyuan3DProcessor:
    _model_cache = {'shapegen_pipeline': None, 'texgen_pipeline': None, 'initialized': False}
    _cache_lock = threading.Lock()

    def __init__(self, device=None):
        with Hunyuan3DProcessor._cache_lock:
            if Hunyuan3DProcessor._model_cache['initialized']:
                # 🔥 INSTANT: Use cached models (~1s)
                self.shapegen_pipeline = Hunyuan3DProcessor._model_cache['shapegen_pipeline']
                return
        # First time: Load models from Hunyuan3D-2.1 directory (30-36s)
        self.initialize_model()

GENERATION WORKFLOW:

# 1. Background removal (rembg)
processed_image = processor.rembg.remove_background(image)

# 2. Shape generation (Hunyuan3D-DiT-v2-1)
mesh = processor.shapegen_pipeline(image=processed_image, num_inference_steps=50)

# 3. Texture synthesis (Hunyuan3D-Paint-v2-1)
textured_mesh = processor.texgen_pipeline(mesh=mesh, image=processed_image)

# 4. Export to STL/OBJ/GLB
mesh.export('output.stl')

[2.1.1] AI VIDEO COMPOSITION (SORA-INSPIRED)

CINEMATIC VIDEO GENERATION:

The ORFEAS platform includes advanced AI video composition capabilities inspired by OpenAI's Sora, enabling the creation of cinematic video content from text prompts, images, or 3D models.

VIDEO PROCESSING PIPELINE:

# backend/sora_video_integration.py
class SoraVideoProcessor:
    """
    Sora-inspired video composition engine for cinematic content generation
    """

    def __init__(self, device=None):
        self.device = device or "cuda" if torch.cuda.is_available() else "cpu"
        self.video_models = {
            'text_to_video': None,
            'image_to_video': None,
            '3d_to_video': None,
            'style_transfer': None
        }
        self.motion_models = {
            'camera_motion': None,
            'object_motion': None,
            'lighting_dynamics': None
        }

    def generate_cinematic_video(self, input_data: Dict) -> Dict:
        """Generate cinematic video from various inputs"""

        # 1. Analyze input content
        content_analysis = self.analyze_content(input_data)

        # 2. Generate motion sequences
        motion_sequence = self.generate_motion_sequence(content_analysis)

        # 3. Apply cinematic techniques
        cinematic_frames = self.apply_cinematic_techniques(motion_sequence)

        # 4. Render final video
        video_output = self.render_video(cinematic_frames)

        return video_output

VIDEO GENERATION WORKFLOWS:

# 1. Text-to-Video Generation
video = processor.text_to_video(
    prompt="A futuristic city with flying cars at sunset",
    duration=10,  # seconds
    fps=24,
    resolution=(1920, 1080),
    style="cinematic"
)

# 2. 3D Model to Video Animation
video = processor.model_to_video(
    model_path="generated_model.stl",
    animation_type="turntable",
    lighting="studio",
    background="gradient"
)

# 3. Image to Video Expansion
video = processor.image_to_video(
    image="input.jpg",
    motion_type="camera_zoom",
    depth_estimation=True,
    parallax_effect=True
)

CINEMATIC FEATURES:

Camera Movements: Dolly, pan, tilt, zoom, orbit, tracking shots
Lighting Dynamics: Dynamic lighting, shadow animation, color grading
Motion Blur: Realistic motion blur and depth of field effects
Temporal Consistency: Frame-to-frame coherence and smooth transitions
Style Transfer: Apply artistic styles across video sequences
3D Integration: Seamless integration with generated 3D models

[2.1.2] AI CODE DEVELOPMENT & DEBUGGING

INTELLIGENT CODE GENERATION:

The ORFEAS platform includes advanced AI-powered code development capabilities for intelligent code writing, automated debugging, and code quality optimization.

CODE DEVELOPMENT PIPELINE:

# backend/code_writer.py
class IntelligentCodeWriter:
    """
    AI-powered code generation and development assistant
    """

    def __init__(self, model="github-copilot"):
        self.model = model
        self.code_models = {
            'github-copilot': None,
            'codeT5': None,
            'starcoder': None,
            'deepseek-coder': None
        }
        self.quality_analyzer = CodeQualityAnalyzer()

    def generate_code(self, prompt: str, language: str, context: Dict = None) -> Dict:
        """Generate code from natural language description"""

        # 1. Analyze requirements
        requirements = self.analyze_requirements(prompt, context)

        # 2. Generate code structure
        code_structure = self.generate_code_structure(requirements, language)

        # 3. Generate implementation
        code_implementation = self.generate_implementation(code_structure, requirements)

        # 4. Apply code optimization
        optimized_code = self.optimize_code(code_implementation, language)

        return {
            'code': optimized_code,
            'quality_score': self.quality_analyzer.analyze(optimized_code),
            'documentation': self.generate_documentation(optimized_code),
            'tests': self.generate_tests(optimized_code) if context.get('generate_tests') else None
        }

CODE DEBUGGING WORKFLOW:

# backend/code_debugger.py
class AutomatedCodeDebugger:
    """
    AI-powered code debugging and error resolution
    """

    def __init__(self):
        self.error_patterns = self.load_error_patterns()
        self.fix_strategies = self.load_fix_strategies()

    def debug_code(self, code: str, error_trace: str, language: str) -> Dict:
        """Automatically debug and fix code issues"""

        # 1. Analyze error
        error_analysis = self.analyze_error(error_trace, code)

        # 2. Identify root cause
        root_cause = self.identify_root_cause(error_analysis, code)

        # 3. Generate fix
        fix_suggestion = self.generate_fix(root_cause, code, language)

        # 4. Validate fix
        validated_fix = self.validate_fix(fix_suggestion, code)

        return {
            'error_type': error_analysis['type'],
            'root_cause': root_cause,
            'fix_suggestion': validated_fix,
            'confidence': error_analysis['confidence'],
            'explanation': self.generate_explanation(root_cause, validated_fix)
        }

CODE DEVELOPMENT FEATURES:

Natural Language to Code: Generate code from plain English descriptions
Code Completion: Intelligent autocomplete and suggestion system
Bug Detection: Automated identification of potential issues
Code Optimization: Performance and quality improvements
Test Generation: Automatic unit test creation
Documentation: Auto-generated code documentation
Code Review: AI-powered code review and suggestions
Refactoring: Intelligent code restructuring and improvement

[2.2] GPU MEMORY MANAGEMENT

RTX 3090 OPTIMIZATION (24GB VRAM):

Model cache: ~8GB (persistent)
Active generation: ~6-10GB
Safety margin: 80% limit via
```
GPU_MEMORY_LIMIT=0.8
```
Auto-cleanup: torch.cuda.empty_cache() after each generation

CORRECT GPU MANAGER USAGE:

# backend/gpu_manager.py
gpu_mgr = get_gpu_manager()

if gpu_mgr.can_process_job(estimated_vram=6000):  # 6GB estimate
    with gpu_mgr.allocate_job(job_id):
        result = generate_3d_model(image)
        gpu_mgr.cleanup_after_job()  # Force CUDA cache clear
else:
    # Queue job for later or reject
    raise ResourceError("GPU memory insufficient")

NEVER:

Load models without checking available VRAM
Skip
```
torch.cuda.empty_cache()
```
between generations
Process concurrent jobs without queue management

[2.3] FLASK API ARCHITECTURE

ENDPOINT PATTERN:

@app.route('/api/generate-3d', methods=['POST'])
@track_request_metrics('generate_3d')  # Prometheus tracking
@security_headers  # CORS and security headers
def generate_3d():
    """
    Text/Image → 3D STL generation

    Request: multipart/form-data with 'image' file or 'prompt' text
    Response: Binary STL file or JSON with job_id for async processing
    Metrics: Tracked via prometheus_metrics.py
    """
    # 1. Validate input (validation.py)
    validator = FileUploadValidator()
    image = validator.validate_image(request.files.get('image'))

    # 2. Check rate limits
    if not get_rate_limiter().check_limit(request.remote_addr):
        return jsonify({'error': 'Rate limit exceeded'}), 429

    # 3. Queue job (batch_processor.py)
    job = async_queue.submit_job('3d_generation', image=image)

    # 4. Return job ID or wait for completion
    return jsonify({'job_id': job.id, 'status': 'processing'})

WEBSOCKET PROGRESS UPDATES:

# Use Flask-SocketIO for real-time progress
socketio.emit('generation_progress', {
    'job_id': job_id,
    'stage': 'shape_generation',
    'progress': 45,
    'eta_seconds': 20
}, room=client_sid)

[2.4] STL PROCESSING PIPELINE

ADVANCED MESH OPERATIONS:

# backend/stl_processor.py
processor = AdvancedSTLProcessor()

# Analysis
stats = processor.analyze_stl(mesh_path)
# Returns: triangle_count, surface_area, volume, bounds, manifold_status

# Repair non-manifold geometry
repaired_mesh = processor.repair_stl(mesh)

# Optimize for 3D printing
optimized_mesh = processor.optimize_stl_for_printing(
    mesh,
    target_size_mm=100,
    wall_thickness_mm=2.0,
    supports=True
)

3D EXPORT FORMATS:

STL: Binary format (production default)
OBJ: With material .mtl files
GLB: Embedded textures for web
PLY: Point cloud or mesh with colors

[2.4.1] HTML5 FRONTEND ARCHITECTURE

PROGRESSIVE WEB APP (PWA) FEATURES:

# backend/pwa_manager.py
class PWAManager:
    """
    Progressive Web App functionality manager
    """

    def __init__(self):
        # Define color constants using RGB values to avoid parser issues
        THEME_COLOR = "rgb(30, 64, 175)"  # Blue theme
        BACKGROUND_COLOR = "rgb(255, 255, 255)"  # White background

        self.manifest_config = {
            "name": "ORFEAS AI 3D Studio",
            "short_name": "ORFEAS",
            "description": "AI-powered 2D to 3D model generation",
            "start_url": "/",
            "display": "standalone",
            "theme_color": THEME_COLOR,
            "background_color": BACKGROUND_COLOR
        }    def generate_manifest(self):
        """Generate manifest.json for PWA installation"""
        return json.dumps(self.manifest_config, indent=2)

    def register_service_worker(self):
        """Setup service worker for offline functionality"""
        return """
        if ('serviceWorker' in navigator) {
            navigator.serviceWorker.register('/service-worker.js')
                .then(() => console.log('SW registered'))
                .catch(() => console.log('SW registration failed'));
        }
        """

HTML5 FEATURES INTEGRATION:

<!-- orfeas-studio.html - Main interface -->
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <meta name="theme-color" content="rgb(30, 64, 175)" />
    <link rel="manifest" href="/manifest.json" />
    <link rel="icon" href="/static/icons/icon-192x192.png" />
    <!-- PWA iOS meta tags -->
    <meta name="apple-mobile-web-app-capable" content="yes" />
    <meta
      name="apple-mobile-web-app-status-bar-style"
      content="black-translucent"
    />
    <link rel="apple-touch-icon" href="/static/icons/icon-192x192.png" />
  </head>
</html>

[2.4.2] SSL/TLS SECURITY IMPLEMENTATION

SSL CERTIFICATE MANAGEMENT:

# backend/ssl_manager.py
class SSLManager:
    """
    SSL certificate management and HTTPS enforcement
    """

    def __init__(self):
        self.cert_path = "ssl/orfeas.crt"
        self.key_path = "ssl/orfeas.key"
        self.lets_encrypt_path = "ssl/lets-encrypt/"

    def enforce_https(self, app):
        """Force HTTPS redirects for all requests"""
        @app.before_request
        def force_https():
            if not request.is_secure and app.env != 'development':
                return redirect(request.url.replace('http://', 'https://'), 301)

    def setup_ssl_context(self):
        """Configure SSL context for Flask application"""
        context = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)
        context.load_cert_chain(self.cert_path, self.key_path)
        return context

    def auto_renew_certificates(self):
        """Auto-renewal setup for Let's Encrypt certificates"""
        # Certbot integration for automatic renewal
        return subprocess.run([
            'certbot', 'renew', '--quiet',
            '--deploy-hook', 'systemctl reload nginx'
        ])

WEB SECURITY HEADERS:

# backend/web_security.py
class WebSecurityManager:
    """
    Web security headers and Content Security Policy
    """

    def __init__(self):
        self.security_headers = {
            'Strict-Transport-Security': 'max-age=31536000; includeSubDomains',
            'X-Content-Type-Options': 'nosniff',
            'X-Frame-Options': 'DENY',
            'X-XSS-Protection': '1; mode=block',
            'Referrer-Policy': 'strict-origin-when-cross-origin'
        }

    def apply_security_headers(self, app):
        """Apply security headers to all responses"""
        @app.after_request
        def set_security_headers(response):
            for header, value in self.security_headers.items():
                response.headers[header] = value
            return response

    def setup_csp(self):
        """Content Security Policy configuration"""
        csp_policy = (
            "default-src 'self'; "
            "script-src 'self' 'unsafe-inline' https://cdnjs.cloudflare.com; "
            "style-src 'self' 'unsafe-inline'; "
            "img-src 'self' data: blob:; "
            "connect-src 'self' wss: ws:; "
            "worker-src 'self' blob:;"
        )
        return {'Content-Security-Policy': csp_policy}

[2.4.3] PHP COMPATIBILITY LAYER

PHP INTEGRATION SUPPORT:

<?php
// php/api.php - PHP API endpoints for legacy systems
class OrfeasPHPAPI {
    private $python_api_url = 'http://localhost:5000/api';

    public function generate3D($image_data, $quality = 7) {
        // Proxy requests to Python backend
        $curl = curl_init();
        curl_setopt_array($curl, [
            CURLOPT_URL => $this->python_api_url . '/generate-3d',
            CURLOPT_RETURNTRANSFER => true,
            CURLOPT_POST => true,
            CURLOPT_POSTFIELDS => [
                'image' => new CURLFile($image_data),
                'quality' => $quality
            ],
            CURLOPT_HTTPHEADER => [
                'Authorization: Bearer ' . $_ENV['API_TOKEN'],
                'Content-Type: multipart/form-data'
            ]
        ]);

        $response = curl_exec($curl);
        curl_close($curl);

        return json_decode($response, true);
    }

    public function checkJobStatus($job_id) {
        // Check generation job status
        $curl = curl_init();
        curl_setopt_array($curl, [
            CURLOPT_URL => $this->python_api_url . '/job-status/' . $job_id,
            CURLOPT_RETURNTRANSFER => true
        ]);

        $response = curl_exec($curl);
        curl_close($curl);

        return json_decode($response, true);
    }
}
?>

[2.5] AI AGENTS & AUTONOMOUS WORKFLOWS

AI AGENT ARCHITECTURE:

The ORFEAS platform includes autonomous AI agents for workflow automation, quality assurance, and intelligent processing decisions.

# backend/agent_api.py - Core agent framework
class OrfeasAIAgent:
    """
    Autonomous AI agent for workflow orchestration

    Features:
    - Intelligent model selection based on input analysis
    - Quality assessment and optimization recommendations
    - Automated error recovery and fallback strategies
    - Performance monitoring and optimization
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        self.agent_type = agent_type  # 'quality_agent', 'workflow_agent', 'optimization_agent'
        self.capabilities = capabilities
        self.decision_tree = self.load_decision_model()

    async def analyze_input(self, input_data: Dict) -> Dict[str, Any]:
        """Analyze input and recommend processing strategy"""
        analysis = {
            'input_type': self.classify_input(input_data),
            'complexity_score': self.assess_complexity(input_data),
            'recommended_pipeline': self.select_pipeline(input_data),
            'estimated_resources': self.estimate_resources(input_data)
        }
        return analysis

    def select_optimal_model(self, analysis: Dict) -> str:
        """Select best AI model based on input analysis"""
        if analysis['complexity_score'] > 0.8:
            return 'hunyuan3d_high_quality'
        elif analysis['input_type'] == 'simple_object':
            return 'hunyuan3d_fast'
        else:
            return 'hunyuan3d_balanced'

AGENT TYPES:

Quality Assessment Agent
- Analyzes input image quality and complexity
- Recommends preprocessing steps
- Predicts generation success probability
- Suggests quality improvements
Workflow Orchestration Agent
- Manages multi-step generation pipelines
- Handles error recovery and retries
- Optimizes resource allocation
- Coordinates between different AI models
Performance Optimization Agent
- Monitors GPU usage and performance
- Adjusts generation parameters for optimal speed/quality
- Manages model caching and memory optimization
- Provides performance recommendations

AGENT AUTHENTICATION & SECURITY:

# backend/agent_auth.py
class AgentAuthManager:
    """
    Secure authentication for autonomous agents
    """

    def __init__(self):
        self.agent_keys = self.load_agent_credentials()
        self.permission_matrix = self.load_permissions()

    def authenticate_agent(self, agent_id: str, api_key: str) -> bool:
        """Verify agent credentials and permissions"""
        if agent_id not in self.agent_keys:
            return False
        return self.verify_key(agent_id, api_key)

    def check_permissions(self, agent_id: str, action: str) -> bool:
        """Check if agent has permission for specific action"""
        return action in self.permission_matrix.get(agent_id, [])

[2.6] MACHINE LEARNING OPTIMIZATION

MODEL PERFORMANCE OPTIMIZATION:

# backend/ml_optimizer.py
class MLPerformanceOptimizer:
    """
    Advanced ML optimization for production inference
    """

    def __init__(self):
        self.optimization_strategies = {
            'tensorrt': self.apply_tensorrt_optimization,
            'quantization': self.apply_quantization,
            'mixed_precision': self.enable_mixed_precision,
            'model_pruning': self.apply_model_pruning
        }

    def optimize_model_for_inference(self, model, strategy: str):
        """Apply production optimizations to ML models"""
        if strategy == 'tensorrt':
            # Convert PyTorch model to TensorRT for 2-5x speedup
            optimized_model = self.convert_to_tensorrt(model)
        elif strategy == 'quantization':
            # INT8 quantization for 4x memory reduction
            optimized_model = torch.quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
        return optimized_model

    def enable_mixed_precision(self, model):
        """Enable FP16/BF16 mixed precision for 40% speedup"""
        model = model.half()  # Convert to FP16
        torch.backends.cudnn.benchmark = True
        return model

BATCH INFERENCE EXTENSIONS:

# backend/batch_inference_extension.py
class BatchInferenceManager:
    """
    Advanced batch processing for ML workloads
    """

    def __init__(self, max_batch_size: int = 4):
        self.max_batch_size = max_batch_size
        self.batch_queue = asyncio.Queue()
        self.processing_strategies = {
            'dynamic_batching': self.dynamic_batch_processing,
            'pipeline_parallel': self.pipeline_parallel_processing,
            'model_parallel': self.model_parallel_processing
        }

    async def dynamic_batch_processing(self, requests: List[Dict]):
        """Group requests into optimal batch sizes"""
        batches = []
        current_batch = []

        for request in requests:
            if len(current_batch) >= self.max_batch_size:
                batches.append(current_batch)
                current_batch = [request]
            else:
                current_batch.append(request)

        if current_batch:
            batches.append(current_batch)

        results = []
        for batch in batches:
            batch_result = await self.process_batch(batch)
            results.extend(batch_result)

        return results

HUGGINGFACE COMPATIBILITY LAYER:

# backend/huggingface_compat.py
class HuggingFaceModelManager:
    """
    Integration layer for HuggingFace models and pipelines
    """

    def __init__(self):
        self.model_cache = {}
        self.supported_models = {
            'stable_diffusion': 'runwayml/stable-diffusion-v1-5',
            'clip': 'openai/clip-vit-base-patch32',
            'controlnet': 'lllyasviel/sd-controlnet-canny',
            'instant_mesh': 'TencentARC/InstantMesh'
        }

    def load_huggingface_model(self, model_name: str, optimization: str = 'none'):
        """Load and optimize HuggingFace models"""
        if model_name in self.model_cache:
            return self.model_cache[model_name]

        if model_name not in self.supported_models:
            raise ValueError(f"Unsupported model: {model_name}")

        model_id = self.supported_models[model_name]

        if optimization == 'tensorrt':
            model = self.load_with_tensorrt(model_id)
        elif optimization == 'onnx':
            model = self.load_with_onnx(model_id)
        else:
            model = transformers.pipeline('text-to-image', model=model_id)

        self.model_cache[model_name] = model
        return model

    def fallback_generation(self, prompt: str, fallback_model: str = 'stable_diffusion'):
        """Fallback generation when primary model fails"""
        try:
            model = self.load_huggingface_model(fallback_model)
            result = model(prompt)
            return result
        except Exception as e:
            logger.error(f"Fallback generation failed: {e}")
            raise RuntimeError("All generation methods failed")

[2.7] INTELLIGENT CONTEXT HANDLING

CONTEXT-AWARE AI PROCESSING:

The ORFEAS platform implements intelligent context handling to enhance AI decision-making, optimize processing workflows, and provide contextual awareness across all components.

# backend/context_manager.py
class IntelligentContextManager:
    """
    Advanced context handling for AI-driven decision making
    """

    def __init__(self):
        self.context_store = {}
        self.session_contexts = {}
        self.global_context = {
            'system_performance': {},
            'user_preferences': {},
            'model_performance_history': {},
            'resource_availability': {}
        }

    def build_processing_context(self, request_data: Dict) -> Dict[str, Any]:
        """Build comprehensive context for AI processing decisions"""
        context = {
            'input_analysis': self.analyze_input_characteristics(request_data),
            'user_context': self.get_user_context(request_data.get('user_id')),
            'system_context': self.get_system_context(),
            'historical_context': self.get_historical_performance(),
            'resource_context': self.get_resource_availability(),
            'quality_context': self.get_quality_requirements(request_data)
        }
        return context

    def analyze_input_characteristics(self, request_data: Dict) -> Dict[str, Any]:
        """Analyze input to determine processing requirements"""
        analysis = {
            'input_type': self.classify_input_type(request_data),
            'complexity_score': self.calculate_complexity(request_data),
            'quality_requirements': self.extract_quality_requirements(request_data),
            'processing_priority': self.determine_priority(request_data),
            'estimated_resources': self.estimate_resource_needs(request_data)
        }
        return analysis

    def get_contextual_recommendations(self, context: Dict) -> Dict[str, Any]:
        """Generate intelligent recommendations based on context"""
        recommendations = {
            'optimal_model': self.recommend_model(context),
            'processing_parameters': self.recommend_parameters(context),
            'quality_settings': self.recommend_quality_settings(context),
            'resource_allocation': self.recommend_resources(context),
            'fallback_strategies': self.recommend_fallbacks(context)
        }
        return recommendations

CONTEXTUAL AI AGENT COORDINATION:

# backend/contextual_agent_coordinator.py
class ContextualAgentCoordinator:
    """
    Coordinates AI agents with intelligent context sharing
    """

    def __init__(self, context_manager: IntelligentContextManager):
        self.context_manager = context_manager
        self.agent_registry = {}
        self.context_sharing_graph = {}

    async def coordinate_with_context(self, task: Dict, agents: List[str]) -> Dict:
        """Coordinate multiple agents with shared context"""
        # Build comprehensive context
        context = self.context_manager.build_processing_context(task)

        # Share relevant context with each agent
        agent_contexts = {}
        for agent_id in agents:
            agent_contexts[agent_id] = self.filter_context_for_agent(context, agent_id)

        # Execute coordinated workflow
        results = {}
        for agent_id in agents:
            agent = await self.get_agent(agent_id)
            results[agent_id] = await agent.execute_with_context(
                task, agent_contexts[agent_id]
            )

            # Update shared context with results
            self.update_shared_context(agent_id, results[agent_id], context)

        return self.synthesize_results(results, context)

    def filter_context_for_agent(self, context: Dict, agent_id: str) -> Dict:
        """Filter context to relevant information for specific agent"""
        agent_type = self.agent_registry[agent_id]['type']

        if agent_type == 'quality_agent':
            return {
                'input_analysis': context['input_analysis'],
                'quality_context': context['quality_context'],
                'historical_context': context['historical_context']
            }
        elif agent_type == 'workflow_agent':
            return {
                'system_context': context['system_context'],
                'resource_context': context['resource_context'],
                'input_analysis': context['input_analysis']
            }
        elif agent_type == 'optimization_agent':
            return {
                'system_context': context['system_context'],
                'resource_context': context['resource_context'],
                'historical_context': context['historical_context']
            }
        else:
            return context  # Full context for unknown agent types

CONTEXT-AWARE MODEL SELECTION:

# backend/contextual_model_selector.py
class ContextualModelSelector:
    """
    Intelligent model selection based on comprehensive context analysis
    """

    def __init__(self):
        self.model_performance_matrix = {}
        self.context_model_mapping = {}
        self.learning_history = {}

    def select_optimal_model(self, context: Dict) -> Dict[str, Any]:
        """Select best model based on context analysis"""
        # Analyze context dimensions
        context_signature = self.generate_context_signature(context)

        # Check for similar historical contexts
        similar_contexts = self.find_similar_contexts(context_signature)

        # Calculate model scores based on context
        model_scores = {}
        for model_name in self.available_models:
            score = self.calculate_model_score(model_name, context, similar_contexts)
            model_scores[model_name] = score

        # Select best model
        optimal_model = max(model_scores, key=model_scores.get)

        # Generate selection reasoning
        selection_reasoning = self.generate_selection_reasoning(
            optimal_model, context, model_scores
        )

        return {
            'selected_model': optimal_model,
            'confidence_score': model_scores[optimal_model],
            'reasoning': selection_reasoning,
            'fallback_models': self.get_fallback_sequence(model_scores),
            'context_signature': context_signature
        }

    def generate_context_signature(self, context: Dict) -> str:
        """Generate unique signature for context matching"""
        key_features = [
            context['input_analysis']['complexity_score'],
            context['quality_context']['target_quality'],
            context['resource_context']['available_vram'],
            context['system_context']['current_load']
        ]
        return hashlib.md5(str(key_features).encode()).hexdigest()[:16]

    def learn_from_result(self, context: Dict, model: str, result: Dict):
        """Update model performance based on actual results"""
        context_sig = self.generate_context_signature(context)

        if context_sig not in self.learning_history:
            self.learning_history[context_sig] = {}

        self.learning_history[context_sig][model] = {
            'quality_score': result.get('quality_score', 0),
            'processing_time': result.get('processing_time', 0),
            'resource_usage': result.get('resource_usage', {}),
            'success': result.get('success', False),
            'timestamp': time.time()
        }

CONTEXTUAL ERROR HANDLING & RECOVERY:

# backend/contextual_error_handler.py
class ContextualErrorHandler:
    """
    Context-aware error handling and recovery strategies
    """

    def __init__(self, context_manager: IntelligentContextManager):
        self.context_manager = context_manager
        self.error_patterns = {}
        self.recovery_strategies = {}

    async def handle_error_with_context(self, error: Exception, context: Dict) -> Dict:
        """Handle errors using contextual information for smart recovery"""
        # Analyze error in context
        error_analysis = self.analyze_error_context(error, context)

        # Determine recovery strategy
        recovery_strategy = self.select_recovery_strategy(error_analysis, context)

        # Execute recovery
        recovery_result = await self.execute_recovery(recovery_strategy, context)

        # Learn from the error and recovery
        self.learn_from_error_recovery(error, context, recovery_strategy, recovery_result)

        return recovery_result

    def analyze_error_context(self, error: Exception, context: Dict) -> Dict:
        """Analyze error within the broader context"""
        return {
            'error_type': type(error).__name__,
            'error_message': str(error),
            'system_state': context['system_context'],
            'input_characteristics': context['input_analysis'],
            'resource_state': context['resource_context'],
            'similar_errors': self.find_similar_error_patterns(error, context),
            'recovery_probability': self.estimate_recovery_probability(error, context)
        }

    def select_recovery_strategy(self, error_analysis: Dict, context: Dict) -> Dict:
        """Select optimal recovery strategy based on context"""
        if error_analysis['error_type'] == 'OutOfMemoryError':
            if context['resource_context']['available_vram'] < 2000:  # < 2GB
                return {'type': 'reduce_quality', 'fallback_model': 'lightweight'}
            else:
                return {'type': 'clear_cache_retry', 'max_retries': 2}

        elif error_analysis['error_type'] == 'ModelLoadError':
            return {'type': 'fallback_model', 'fallback_sequence': ['instant_mesh', 'stable_diffusion']}

        elif error_analysis['recovery_probability'] > 0.7:
            return {'type': 'intelligent_retry', 'context_adjustment': True}
        else:
            return {'type': 'graceful_failure', 'user_notification': True}

CONTEXT PERSISTENCE & LEARNING:

# backend/context_persistence.py
class ContextPersistenceManager:
    """
    Manages context persistence and learning across sessions
    """

    def __init__(self):
        self.context_db = self.initialize_context_database()
        self.learning_models = self.load_learning_models()

    def persist_context(self, session_id: str, context: Dict):
        """Save context for future learning and optimization"""
        context_record = {
            'session_id': session_id,
            'timestamp': time.time(),
            'context_data': context,
            'context_hash': self.hash_context(context)
        }
        self.context_db.insert(context_record)

    def learn_patterns(self):
        """Analyze historical contexts to learn patterns"""
        # Analyze successful processing patterns
        successful_patterns = self.analyze_successful_contexts()

        # Identify failure patterns
        failure_patterns = self.analyze_failed_contexts()

        # Update recommendation models
        self.update_recommendation_models(successful_patterns, failure_patterns)

        # Update context similarity models
        self.update_similarity_models()

    def get_contextual_insights(self, current_context: Dict) -> Dict:
        """Provide insights based on historical context analysis"""
        similar_contexts = self.find_similar_historical_contexts(current_context)

        insights = {
            'success_probability': self.predict_success_probability(current_context),
            'optimal_parameters': self.recommend_parameters(current_context),
            'potential_issues': self.predict_potential_issues(current_context),
            'processing_time_estimate': self.estimate_processing_time(current_context),
            'resource_requirements': self.estimate_resource_requirements(current_context)
        }

        return insights

[2.8] PERFORMANCE & SECURITY OPTIMIZATION

SPEED OPTIMIZATION STRATEGIES:

# backend/performance_optimizer.py
class PerformanceOptimizer:
    """
    Comprehensive performance optimization for maximum speed
    """

    def __init__(self):
        self.cache_layers = {
            'model_cache': {},      # Model instance caching
            'result_cache': {},     # Generation result caching
            'context_cache': {},    # Context analysis caching
            'precompute_cache': {}  # Pre-computed optimizations
        }
        self.optimization_config = self.load_optimization_config()

    def apply_speed_optimizations(self, model, processing_type: str):
        """Apply all speed optimizations for maximum performance"""

        # 1. Model-level optimizations
        optimized_model = self.optimize_model_inference(model)

        # 2. Memory optimization
        optimized_model = self.apply_memory_optimization(optimized_model)

        # 3. Compilation optimization
        if self.optimization_config.get('torch_compile', False):
            optimized_model = torch.compile(optimized_model, mode='max-autotune')

        # 4. GPU-specific optimizations
        optimized_model = self.apply_gpu_optimizations(optimized_model)

        return optimized_model

    def optimize_model_inference(self, model):
        """Apply inference-specific optimizations"""
        # Enable inference mode
        model.eval()
        torch.set_grad_enabled(False)

        # Apply quantization if configured
        if self.optimization_config.get('quantization', False):
            model = torch.quantization.quantize_dynamic(
                model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8
            )

        # Apply pruning for faster inference
        if self.optimization_config.get('pruning', False):
            model = self.apply_structured_pruning(model, sparsity=0.3)

        return model

    def apply_memory_optimization(self, model):
        """Optimize memory usage for better performance"""
        # Enable memory efficient attention
        try:
            from xformers.ops import memory_efficient_attention
            model.use_memory_efficient_attention = True
        except ImportError:
            pass

        # Enable gradient checkpointing for large models
        if hasattr(model, 'gradient_checkpointing_enable'):
            model.gradient_checkpointing_enable()

        # Optimize CUDA memory allocation
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

        return model

    def apply_gpu_optimizations(self, model):
        """Apply GPU-specific performance optimizations"""
        # Enable TensorFloat-32 for RTX GPUs
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True

        # Enable cuDNN benchmark mode
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = False

        # Enable mixed precision training
        if self.optimization_config.get('mixed_precision', True):
            model = model.half()  # Convert to FP16

        return model

ACCURACY ENHANCEMENT SYSTEMS:

# backend/accuracy_enhancer.py
class AccuracyEnhancer:
    """
    Advanced accuracy enhancement through intelligent processing
    """

    def __init__(self):
        self.quality_metrics = {}
        self.accuracy_thresholds = {
            'minimum_quality': 0.7,
            'target_quality': 0.85,
            'excellence_quality': 0.95
        }
        self.enhancement_strategies = self.load_enhancement_strategies()

    def enhance_generation_accuracy(self, input_data: Dict, context: Dict) -> Dict:
        """Apply multi-layer accuracy enhancement"""

        # 1. Input preprocessing for accuracy
        preprocessed_input = self.preprocess_for_accuracy(input_data, context)

        # 2. Multi-model ensemble for higher accuracy
        if context.get('accuracy_priority', False):
            result = self.ensemble_generation(preprocessed_input, context)
        else:
            result = self.single_model_generation(preprocessed_input, context)

        # 3. Post-processing quality enhancement
        enhanced_result = self.post_process_for_quality(result, context)

        # 4. Quality validation and refinement
        final_result = self.validate_and_refine(enhanced_result, context)

        return final_result

    def preprocess_for_accuracy(self, input_data: Dict, context: Dict) -> Dict:
        """Intelligent preprocessing to maximize generation accuracy"""

        # Analyze input quality
        quality_analysis = self.analyze_input_quality(input_data)

        # Apply enhancement based on analysis
        if quality_analysis['needs_enhancement']:
            # Image denoising
            if 'noise_level' in quality_analysis and quality_analysis['noise_level'] > 0.3:
                input_data['image'] = self.denoise_image(input_data['image'])

            # Contrast enhancement
            if quality_analysis.get('low_contrast', False):
                input_data['image'] = self.enhance_contrast(input_data['image'])

            # Resolution upsampling if needed
            if quality_analysis.get('resolution_too_low', False):
                input_data['image'] = self.upsample_image(input_data['image'])

        return input_data

    def ensemble_generation(self, input_data: Dict, context: Dict) -> Dict:
        """Use multiple models for higher accuracy"""

        models = ['hunyuan3d_primary', 'hunyuan3d_alternative', 'instant_mesh']
        results = []

        for model_name in models:
            try:
                model_result = self.generate_with_model(model_name, input_data)
                results.append({
                    'model': model_name,
                    'result': model_result,
                    'quality_score': self.calculate_quality_score(model_result)
                })
            except Exception as e:
                logger.warning(f"Model {model_name} failed: {e}")
                continue

        # Select best result or combine results
        return self.select_best_or_combine(results, context)

    def validate_and_refine(self, result: Dict, context: Dict) -> Dict:
        """Final validation and refinement for accuracy"""

        quality_score = self.calculate_comprehensive_quality(result)

        if quality_score < self.accuracy_thresholds['minimum_quality']:
            # Apply refinement techniques
            result = self.apply_mesh_refinement(result)
            result = self.apply_texture_enhancement(result)

            # Recalculate quality
            quality_score = self.calculate_comprehensive_quality(result)

        result['accuracy_metrics'] = {
            'quality_score': quality_score,
            'accuracy_grade': self.get_accuracy_grade(quality_score),
            'enhancement_applied': quality_score >= self.accuracy_thresholds['target_quality']
        }

        return result

SECURITY HARDENING FRAMEWORK:

# backend/security_hardening.py
class SecurityHardening:
    """
    Comprehensive security hardening for production deployment
    """

    def __init__(self):
        self.security_config = self.load_security_config()
        self.threat_detector = ThreatDetector()
        self.access_monitor = AccessMonitor()

    def apply_security_hardening(self, app):
        """Apply comprehensive security hardening"""

        # 1. Input validation and sanitization
        self.setup_input_validation(app)

        # 2. Authentication and authorization
        self.setup_authentication_security(app)

        # 3. API security
        self.setup_api_security(app)

        # 4. File upload security
        self.setup_file_upload_security(app)

        # 5. Rate limiting and DoS protection
        self.setup_rate_limiting(app)

        # 6. Security monitoring
        self.setup_security_monitoring(app)

    def setup_input_validation(self, app):
        """Comprehensive input validation and sanitization"""

        @app.before_request
        def validate_all_inputs():
            # SQL injection protection
            if self.detect_sql_injection(request):
                abort(400, "Invalid input detected")

            # XSS protection
            if self.detect_xss_attempt(request):
                abort(400, "Malicious script detected")

            # Path traversal protection
            if self.detect_path_traversal(request):
                abort(400, "Path traversal attempt detected")

            # Command injection protection
            if self.detect_command_injection(request):
                abort(400, "Command injection attempt detected")

    def setup_api_security(self, app):
        """API-specific security measures"""

        # API key validation
        @app.before_request
        def validate_api_access():
            if request.endpoint and request.endpoint.startswith('api/'):
                api_key = request.headers.get('X-API-Key')
                if not self.validate_api_key(api_key):
                    abort(401, "Invalid API key")

        # Request signing for critical operations
        @app.before_request
        def validate_request_signature():
            if request.endpoint in self.security_config['signed_endpoints']:
                signature = request.headers.get('X-Request-Signature')
                if not self.validate_request_signature(request, signature):
                    abort(401, "Invalid request signature")

    def setup_file_upload_security(self, app):
        """Secure file upload handling"""

        def secure_file_validation(file):
            # File type validation
            if not self.validate_file_type(file):
                raise SecurityError("Invalid file type")

            # File size validation
            if not self.validate_file_size(file):
                raise SecurityError("File too large")

            # Malware scanning
            if self.detect_malware(file):
                raise SecurityError("Malicious file detected")

            # Content validation
            if not self.validate_file_content(file):
                raise SecurityError("Invalid file content")

            return True

    def setup_security_monitoring(self, app):
        """Real-time security monitoring"""

        @app.after_request
        def monitor_security_events(response):
            # Log security events
            self.log_security_event(request, response)

            # Detect anomalies
            if self.detect_anomaly(request, response):
                self.alert_security_team(request, response)

            # Update threat intelligence
            self.update_threat_intelligence(request, response)

            return response

AI/ML INTEGRATION OPTIMIZATION:

# backend/ml_integration_optimizer.py
class MLIntegrationOptimizer:
    """
    Optimized machine learning integration for maximum efficiency
    """

    def __init__(self):
        self.model_registry = ModelRegistry()
        self.inference_scheduler = InferenceScheduler()
        self.resource_optimizer = ResourceOptimizer()

    def optimize_ml_pipeline(self, pipeline_config: Dict) -> Dict:
        """Optimize entire ML pipeline for speed and accuracy"""

        # 1. Model selection optimization
        optimal_models = self.select_optimal_models(pipeline_config)

        # 2. Inference optimization
        optimized_inference = self.optimize_inference_pipeline(optimal_models)

        # 3. Resource allocation optimization
        optimized_resources = self.optimize_resource_allocation(optimized_inference)

        # 4. Caching strategy optimization
        optimized_caching = self.optimize_caching_strategy(optimized_resources)

        return optimized_caching

    def select_optimal_models(self, config: Dict) -> List[Dict]:
        """Select optimal model combination based on requirements"""

        requirements = {
            'speed_priority': config.get('speed_priority', 0.5),
            'accuracy_priority': config.get('accuracy_priority', 0.5),
            'resource_constraints': config.get('resource_constraints', {}),
            'quality_requirements': config.get('quality_requirements', {})
        }

        # Multi-objective optimization for model selection
        optimal_combination = self.multi_objective_model_selection(requirements)

        return optimal_combination

    def optimize_inference_pipeline(self, models: List[Dict]) -> Dict:
        """Optimize inference pipeline for maximum throughput"""

        # Pipeline parallelization
        parallel_stages = self.identify_parallel_stages(models)

        # Batch optimization
        optimal_batch_config = self.optimize_batch_processing(models)

        # Memory optimization
        memory_config = self.optimize_memory_usage(models)

        # GPU utilization optimization
        gpu_config = self.optimize_gpu_utilization(models)

        return {
            'parallel_stages': parallel_stages,
            'batch_config': optimal_batch_config,
            'memory_config': memory_config,
            'gpu_config': gpu_config
        }

    def optimize_caching_strategy(self, pipeline_config: Dict) -> Dict:
        """Intelligent caching for maximum performance"""

        caching_layers = {
            'model_cache': {
                'strategy': 'LRU',
                'size_limit': '8GB',
                'persistence': True
            },
            'inference_cache': {
                'strategy': 'Adaptive',
                'ttl': 3600,  # 1 hour
                'compression': True
            },
            'result_cache': {
                'strategy': 'Context-aware',
                'size_limit': '4GB',
                'deduplication': True
            }
        }

        return {
            **pipeline_config,
            'caching_layers': caching_layers,
            'cache_warming': self.setup_cache_warming(),
            'cache_invalidation': self.setup_smart_invalidation()
        }

[2.8] MULTI-ENCODING & INTERNATIONALIZATION IMPLEMENTATION

INTELLIGENT ENCODING DETECTION & CONVERSION:

# backend/encoding_manager.py - Core encoding management
class EncodingManager:
    """
    Advanced multi-character encoding detection and conversion system
    """

    def __init__(self):
        self.supported_encodings = [
            'utf-8', 'utf-16-le', 'utf-16-be', 'utf-32-le', 'utf-32-be',
            'ascii', 'latin-1', 'cp1252', 'gb2312', 'gbk', 'big5',
            'shift-jis', 'euc-kr', 'iso-8859-1', 'iso-8859-2', 'iso-8859-15'
        ]
        self.encoding_detectors = self.initialize_detectors()

    def detect_encoding_with_fallback(self, file_path: str) -> Dict[str, Any]:
        """Detect file encoding with intelligent fallback chain"""

        # Read file bytes for analysis
        with open(file_path, 'rb') as f:
            raw_data = f.read()

        # 1. BOM detection (highest priority)
        bom_encoding = self.detect_bom(raw_data)
        if bom_encoding:
            return {
                'encoding': bom_encoding,
                'confidence': 1.0,
                'method': 'bom_detection',
                'bom_present': True
            }

        # 2. Automatic detection using multiple libraries
        detection_results = []

        # chardet detection
        chardet_result = chardet.detect(raw_data)
        if chardet_result['encoding']:
            detection_results.append({
                'encoding': chardet_result['encoding'].lower(),
                'confidence': chardet_result['confidence'],
                'method': 'chardet'
            })

        # charset-normalizer detection
        normalizer_result = from_bytes(raw_data).best()
        if normalizer_result:
            detection_results.append({
                'encoding': str(normalizer_result.encoding).lower(),
                'confidence': normalizer_result.coherence,
                'method': 'charset_normalizer'
            })

        # 3. Select best detection result
        if detection_results:
            best_result = max(detection_results, key=lambda x: x['confidence'])
            if best_result['confidence'] > 0.7:
                return best_result

        # 4. Fallback chain validation
        fallback_chain = os.getenv('ENCODING_FALLBACK_CHAIN', 'utf-8,latin-1,cp1252,ascii').split(',')

        for encoding in fallback_chain:
            if self.validate_encoding(raw_data, encoding.strip()):
                return {
                    'encoding': encoding.strip(),
                    'confidence': 0.5,
                    'method': 'fallback_validation',
                    'fallback_position': fallback_chain.index(encoding)
                }

        # 5. Default to UTF-8 with error handling
        return {
            'encoding': 'utf-8',
            'confidence': 0.1,
            'method': 'default_fallback',
            'error_handling': 'replace'
        }

    def convert_file_encoding(self, input_path: str, output_path: str, target_encoding: str = 'utf-8') -> Dict[str, Any]:
        """Convert file from detected encoding to target encoding"""

        # Detect source encoding
        source_info = self.detect_encoding_with_fallback(input_path)
        source_encoding = source_info['encoding']

        try:
            # Read with detected encoding
            with open(input_path, 'r', encoding=source_encoding, errors='replace') as f:
                content = f.read()

            # Unicode normalization
            normalized_content = self.normalize_unicode(content)

            # Write with target encoding
            with open(output_path, 'w', encoding=target_encoding, errors='replace') as f:
                f.write(normalized_content)

            return {
                'success': True,
                'source_encoding': source_encoding,
                'target_encoding': target_encoding,
                'source_confidence': source_info['confidence'],
                'normalization_applied': True,
                'file_size_bytes': len(normalized_content.encode(target_encoding))
            }

        except Exception as e:
            return {
                'success': False,
                'error': str(e),
                'source_encoding': source_encoding,
                'target_encoding': target_encoding
            }

    def normalize_unicode(self, text: str, form: str = None) -> str:
        """Apply Unicode normalization to text"""

        normalization_form = form or os.getenv('UNICODE_NORMALIZATION', 'NFC')

        if normalization_form.upper() in ['NFC', 'NFD', 'NFKC', 'NFKD']:
            return unicodedata.normalize(normalization_form.upper(), text)
        else:
            return text

# backend/i18n_manager.py - Internationalization management
class InternationalizationManager:
    """
    Comprehensive internationalization and localization management
    """

    def __init__(self):
        self.supported_languages = self.load_supported_languages()
        self.language_detector = self.initialize_language_detector()
        self.translation_engine = self.setup_translation_engine()

    def detect_content_language(self, text: str) -> Dict[str, Any]:
        """Detect language of text content"""

        try:
            # Primary detection with langdetect
            detected_lang = detect(text)
            confidence = detect_langs(text)[0].prob

            # Validate against supported languages
            if detected_lang in self.supported_languages:
                return {
                    'language': detected_lang,
                    'confidence': confidence,
                    'method': 'langdetect',
                    'supported': True
                }
            else:
                # Find closest supported language
                closest_lang = self.find_closest_language(detected_lang)
                return {
                    'language': closest_lang,
                    'original_detection': detected_lang,
                    'confidence': confidence * 0.8,  # Reduced confidence
                    'method': 'langdetect_mapped',
                    'supported': True
                }

        except Exception as e:
            # Fallback to default language
            default_lang = os.getenv('DEFAULT_LANGUAGE', 'en-US')
            return {
                'language': default_lang,
                'confidence': 0.1,
                'method': 'fallback',
                'error': str(e),
                'supported': True
            }

    def setup_multilingual_processing(self, content: str, target_languages: List[str] = None) -> Dict[str, Any]:
        """Setup multilingual processing pipeline"""

        # Detect source language
        source_detection = self.detect_content_language(content)

        # Determine target languages
        if not target_languages:
            target_languages = os.getenv('SUPPORTED_LANGUAGES', 'en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi').split(',')

        # Create processing pipeline
        pipeline = {
            'source_language': source_detection['language'],
            'target_languages': target_languages,
            'processing_steps': [
                'encoding_normalization',
                'language_detection',
                'text_segmentation',
                'nlp_processing',
                'translation_preparation'
            ],
            'language_specific_processors': {}
        }

        # Setup language-specific processors
        for lang in target_languages:
            processor_config = self.get_language_processor_config(lang.strip())
            pipeline['language_specific_processors'][lang.strip()] = processor_config

        return pipeline

MULTI-PROGRAMMING LANGUAGE SUPPORT:

# backend/polyglot_code_manager.py - Multi-language code management
class PolyglotCodeManager:
    """
    Advanced multi-programming language code management and analysis
    """

    def __init__(self):
        self.supported_languages = {
            'python': {'extensions': ['.py', '.pyw'], 'keywords': ['def', 'class', 'import'], 'priority': 1},
            'javascript': {'extensions': ['.js', '.mjs'], 'keywords': ['function', 'const', 'let'], 'priority': 2},
            'typescript': {'extensions': ['.ts', '.tsx'], 'keywords': ['interface', 'type', 'enum'], 'priority': 3},
            'java': {'extensions': ['.java'], 'keywords': ['public class', 'private', 'protected'], 'priority': 4},
            'csharp': {'extensions': ['.cs'], 'keywords': ['namespace', 'using', 'public class'], 'priority': 5},
            'go': {'extensions': ['.go'], 'keywords': ['package', 'func', 'import'], 'priority': 6},
            'rust': {'extensions': ['.rs'], 'keywords': ['fn', 'struct', 'impl'], 'priority': 7},
            'php': {'extensions': ['.php'], 'keywords': ['<?php', 'function', 'class'], 'priority': 8},
            'cpp': {'extensions': ['.cpp', '.cc', '.cxx'], 'keywords': ['include_directive', 'namespace', 'class'], 'priority': 9},
            'html': {'extensions': ['.html', '.htm'], 'keywords': ['<!DOCTYPE', '<html>', '<head>'], 'priority': 10},
            'css': {'extensions': ['.css'], 'keywords': ['{', '}', ':'], 'priority': 11},
            'sql': {'extensions': ['.sql'], 'keywords': ['SELECT', 'FROM', 'WHERE'], 'priority': 12}
        }
        self.language_parsers = self.initialize_parsers()

    def detect_programming_language(self, code_content: str, file_path: str = None) -> Dict[str, Any]:
        """Intelligent programming language detection"""

        detection_methods = []

        # 1. File extension detection
        if file_path:
            ext_detection = self.detect_by_extension(file_path)
            if ext_detection:
                detection_methods.append({
                    'method': 'file_extension',
                    'language': ext_detection,
                    'confidence': 0.9
                })

        # 2. Keyword analysis
        keyword_detection = self.detect_by_keywords(code_content)
        detection_methods.extend(keyword_detection)

        # 3. Syntax pattern analysis
        syntax_detection = self.detect_by_syntax_patterns(code_content)
        detection_methods.extend(syntax_detection)

        # 4. Tree-sitter parsing (if available)
        if self.tree_sitter_available():
            tree_detection = self.detect_by_tree_sitter(code_content)
            detection_methods.extend(tree_detection)

        # Select best detection
        if detection_methods:
            best_detection = max(detection_methods, key=lambda x: x['confidence'])

            return {
                'detected_language': best_detection['language'],
                'confidence': best_detection['confidence'],
                'method': best_detection['method'],
                'all_detections': detection_methods,
                'supported': best_detection['language'] in self.supported_languages
            }
        else:
            return {
                'detected_language': 'unknown',
                'confidence': 0.0,
                'method': 'none',
                'supported': False
            }

    def create_polyglot_project_structure(self, languages: List[str], project_name: str) -> Dict[str, Any]:
        """Create multi-language project structure"""

        project_structure = {
            'project_name': project_name,
            'languages': languages,
            'directories': {},
            'config_files': {},
            'build_systems': {},
            'dependencies': {}
        }

        for lang in languages:
            lang_config = self.get_language_config(lang)

            # Create language-specific directories
            project_structure['directories'][lang] = {
                'source': f'src/{lang}',
                'tests': f'tests/{lang}',
                'docs': f'docs/{lang}',
                'build': f'build/{lang}'
            }

            # Language-specific configuration files
            project_structure['config_files'][lang] = lang_config.get('config_files', [])

            # Build system configuration
            project_structure['build_systems'][lang] = lang_config.get('build_system', 'generic')

            # Dependency management
            project_structure['dependencies'][lang] = lang_config.get('dependency_manager', 'manual')

        # Cross-language integration
        project_structure['integration'] = {
            'api_contracts': 'contracts/',
            'shared_schemas': 'schemas/',
            'documentation': 'docs/integration/',
            'communication_protocols': ['rest_api', 'grpc', 'graphql']
        }

        return project_structure

    def setup_cross_language_interop(self, source_lang: str, target_lang: str) -> Dict[str, Any]:
        """Setup interoperability between different programming languages"""

        interop_config = {
            'source_language': source_lang,
            'target_language': target_lang,
            'communication_methods': [],
            'data_serialization': [],
            'build_integration': {},
            'testing_strategy': {}
        }

        # Determine communication methods
        if source_lang == 'python' and target_lang == 'javascript':
            interop_config['communication_methods'] = ['rest_api', 'websocket', 'json_rpc']
            interop_config['data_serialization'] = ['json', 'msgpack']
        elif source_lang == 'java' and target_lang == 'python':
            interop_config['communication_methods'] = ['grpc', 'rest_api', 'jni']
            interop_config['data_serialization'] = ['protobuf', 'json', 'avro']
        elif 'cpp' in [source_lang, target_lang]:
            interop_config['communication_methods'] = ['ffi', 'shared_library', 'c_api']
            interop_config['data_serialization'] = ['protobuf', 'flatbuffers', 'binary']

        # Build integration setup
        interop_config['build_integration'] = self.setup_build_integration(source_lang, target_lang)

        return interop_config

# backend/cross_language_translator.py - Code translation engine
class CrossLanguageTranslator:
    """
    AI-powered code translation between programming languages
    """

    def __init__(self):
        self.translation_models = {
            'ast_based': self.setup_ast_translator(),
            'ai_powered': self.setup_ai_translator(),
            'pattern_based': self.setup_pattern_translator()
        }

    def translate_code(self, source_code: str, source_lang: str, target_lang: str, translation_method: str = 'ai_powered') -> Dict[str, Any]:
        """Translate code from one language to another"""

        if translation_method not in self.translation_models:
            translation_method = 'ai_powered'  # Default fallback

        try:
            # Pre-processing
            preprocessed_code = self.preprocess_for_translation(source_code, source_lang)

            # Translation
            translator = self.translation_models[translation_method]
            translated_code = translator.translate(preprocessed_code, source_lang, target_lang)

            # Post-processing
            final_code = self.postprocess_translation(translated_code, target_lang)

            # Validation
            validation_result = self.validate_translated_code(final_code, target_lang)

            return {
                'translated_code': final_code,
                'source_language': source_lang,
                'target_language': target_lang,
                'translation_method': translation_method,
                'validation': validation_result,
                'confidence': validation_result.get('syntax_valid', False) * 0.8 + 0.2
            }

        except Exception as e:
            return {
                'error': str(e),
                'source_language': source_lang,
                'target_language': target_lang,
                'translation_method': translation_method,
                'success': False
            }

    def validate_translated_code(self, code: str, language: str) -> Dict[str, Any]:
        """Validate translated code for syntax and basic structure"""

        validation = {
            'syntax_valid': False,
            'structure_valid': False,
            'warnings': [],
            'errors': []
        }

        try:
            # Language-specific syntax validation
            if language == 'python':
                validation = self.validate_python_syntax(code)
            elif language == 'javascript':
                validation = self.validate_javascript_syntax(code)
            elif language == 'java':
                validation = self.validate_java_syntax(code)
            # Add more languages as needed

            return validation

        except Exception as e:
            validation['errors'].append(f"Validation error: {str(e)}")
            return validation

DEBUGGING PATTERNS FOR MULTI-LANGUAGE SUPPORT:

# Enhanced debugging for multi-encoding and multi-language issues
def debug_encoding_issues(file_path: str, error: Exception) -> Dict[str, Any]:
    """Debug encoding-related issues with comprehensive analysis"""

    debug_info = {
        'file_path': file_path,
        'error_type': type(error).__name__,
        'error_message': str(error),
        'encoding_analysis': {},
        'suggested_fixes': []
    }

    try:
        # Analyze file encoding
        encoding_mgr = EncodingManager()
        detection_result = encoding_mgr.detect_encoding_with_fallback(file_path)
        debug_info['encoding_analysis'] = detection_result

        # Generate specific fixes based on error type
        if 'UnicodeDecodeError' in str(error):
            debug_info['suggested_fixes'].extend([
                f"Try reading with detected encoding: {detection_result['encoding']}",
                "Enable error handling: errors='replace' or errors='ignore'",
                "Convert file to UTF-8 using encoding_manager.convert_file_encoding()"
            ])
        elif 'UnicodeEncodeError' in str(error):
            debug_info['suggested_fixes'].extend([
                "Normalize Unicode text using unicodedata.normalize()",
                "Use UTF-8 encoding for output",
                "Apply Unicode escape sequences for problematic characters"
            ])

        return debug_info

    except Exception as debug_error:
        debug_info['debug_error'] = str(debug_error)
        return debug_info

def debug_language_detection_issues(code: str, expected_lang: str = None) -> Dict[str, Any]:
    """Debug programming language detection issues"""

    debug_info = {
        'code_sample': code[:200] + '...' if len(code) > 200 else code,
        'expected_language': expected_lang,
        'detection_results': {},
        'suggested_improvements': []
    }

    try:
        # Multi-method language detection
        polyglot_mgr = PolyglotCodeManager()
        detection_result = polyglot_mgr.detect_programming_language(code)
        debug_info['detection_results'] = detection_result

        # Analyze detection accuracy
        if expected_lang and detection_result['detected_language'] != expected_lang:
            debug_info['suggested_improvements'].extend([
                f"Add more {expected_lang}-specific keywords to improve detection",
                "Provide file extension hint for better accuracy",
                "Use Tree-sitter parser for more accurate syntax analysis",
                "Enable multi-language detection for polyglot code"
            ])

        return debug_info

    except Exception as debug_error:
        debug_info['debug_error'] = str(debug_error)
        return debug_info

[3] CODING STANDARDS

[3.1] PYTHON REQUIREMENTS

CODING PATTERNS:

# backend/main.py structure
"""
ORFEAS AI 2D→3D Studio - Module Name
==================================================
ORFEAS AI

Features:
- Feature 1
- Feature 2
"""

# ALWAYS: Type hints for function signatures
def generate_3d_model(
    image: Union[str, Image.Image],
    quality: int = 7,
    format: str = 'stl'
) -> Dict[str, Any]:
    """
    Generate 3D model from 2D image

    Args:
        image: PIL Image or file path
        quality: Model quality 1-10 (default: 7)
        format: Output format (stl, obj, glb)

    Returns:
        Dict with 'mesh_path', 'stats', 'metadata'

    Raises:
        ValueError: Invalid quality or format
        RuntimeError: GPU memory insufficient
    """
    pass

# ALWAYS: Comprehensive error handling
try:
    mesh = processor.generate_shape(image)
except torch.cuda.OutOfMemoryError:
    logger.error("GPU OOM - clearing cache and retrying")
    torch.cuda.empty_cache()
    mesh = processor.generate_shape(image, low_vram_mode=True)
except Exception as e:
    logger.error(f"Generation failed: {traceback.format_exc()}")
    raise RuntimeError(f"3D generation failed: {str(e)}")

# ALWAYS: Use consistent logger format
logger = logging.getLogger(__name__)
logger.info("[ORFEAS] Feature initialized successfully")
logger.warning("[ORFEAS] GPU memory at 85% - consider cleanup")
logger.error("[ORFEAS] Critical failure - reverting to fallback")

[3.2] CONFIGURATION MANAGEMENT

Environment Variables (.env):

# backend/.env
ORFEAS_PORT=5000
DEVICE=cuda                    # auto, cuda, cpu
MAX_CONCURRENT_JOBS=3
GPU_MEMORY_LIMIT=0.8           # 80% of total VRAM
HUNYUAN3D_PATH=../Hunyuan3D-2.1
MODEL_CACHE_DIR=./models
DEFAULT_STEPS=50
MAX_STEPS=100
LOG_LEVEL=INFO

# AI Video Composition Settings
ENABLE_VIDEO_GENERATION=true
SORA_MODEL_PATH=./models/sora
VIDEO_OUTPUT_DIR=./outputs/videos
DEFAULT_VIDEO_FPS=24
DEFAULT_VIDEO_RESOLUTION=1920x1080
MAX_VIDEO_DURATION=60          # seconds
ENABLE_CINEMATIC_MODE=true
VIDEO_QUALITY_PRESET=high      # low, medium, high, ultra
ENABLE_3D_TO_VIDEO=true
ENABLE_TEXT_TO_VIDEO=true
ENABLE_IMAGE_TO_VIDEO=true
VIDEO_CACHE_SIZE=10            # GB for video caching
TEMPORAL_CONSISTENCY_ENABLED=true

# AI Code Development Settings
ENABLE_CODE_WRITING=true
ENABLE_CODE_DEBUGGING=true
ENABLE_CODE_ANALYSIS=true
CODE_AI_MODEL=github-copilot   # github-copilot, codeT5, starcoder, deepseek-coder
CODE_OUTPUT_DIR=./outputs/code
CODE_QUALITY_THRESHOLD=0.8     # Minimum code quality score
ENABLE_AUTO_FORMATTING=true
ENABLE_SYNTAX_VALIDATION=true
ENABLE_PERFORMANCE_ANALYSIS=true
CODE_GENERATION_TIMEOUT=30     # seconds
ENABLE_CODE_DOCUMENTATION=true
ENABLE_TEST_GENERATION=true

# AI Multimedia Generation Settings
ENABLE_TEXT_TO_IMAGE=true
ENABLE_TEXT_TO_SPEECH=true
ENABLE_SPEECH_TO_TEXT=true
TEXT_TO_IMAGE_MODEL=dalle3        # dalle3, stable_diffusion_xl, midjourney
TEXT_TO_IMAGE_QUALITY=high        # low, medium, high, ultra
TEXT_TO_IMAGE_OUTPUT_DIR=./outputs/images
TEXT_TO_IMAGE_CACHE_SIZE=5        # GB for image caching
SPEECH_MODEL=whisper_large        # whisper_large, azure_speech, google_speech
TTS_MODEL=elevenlabs              # elevenlabs, azure_tts, openai_tts
TTS_VOICE_ID=default              # Voice ID for text-to-speech
TTS_OUTPUT_DIR=./outputs/audio
AUDIO_QUALITY=high                # low, medium, high
MAX_AUDIO_DURATION=300            # seconds (5 minutes)
ENABLE_REAL_TIME_TRANSCRIPTION=true
ENABLE_VOICE_CLONING=false        # Advanced voice synthesis
SPEECH_LANGUAGE=en-US             # Language for speech processing

# Enterprise LLM Integration Settings
ENABLE_LLM_INTEGRATION=true
LLM_PRIMARY_MODEL=gpt4_turbo    # gpt4_turbo, claude_3_5_sonnet, gemini_ultra
LLM_FALLBACK_MODEL=claude_3_5_sonnet
LLM_API_TIMEOUT=30              # seconds
LLM_MAX_TOKENS=4000             # Maximum tokens per request
LLM_TEMPERATURE=0.3             # Creativity vs determinism (0.0-1.0)
LLM_CONTEXT_WINDOW=32000        # Context window size

# Multi-LLM Orchestration
ENABLE_MULTI_LLM_ORCHESTRATION=true
LLM_TASK_DECOMPOSITION=true     # Break complex tasks into subtasks
LLM_RESULT_SYNTHESIS=true       # Combine results from multiple LLMs
LLM_QUALITY_VALIDATION=true     # Validate LLM responses for quality
LLM_RESPONSE_CACHING=true       # Cache frequent responses
LLM_CACHE_TTL=3600              # Cache time-to-live in seconds

# RAG (Retrieval-Augmented Generation) Settings
ENABLE_RAG_SYSTEM=true
VECTOR_DATABASE_TYPE=pinecone   # pinecone, weaviate, qdrant, chroma
VECTOR_DIMENSION=1536           # Embedding dimension
KNOWLEDGE_BASE_PATH=./knowledge
RAG_RETRIEVAL_TOP_K=10          # Number of relevant documents to retrieve
RAG_SIMILARITY_THRESHOLD=0.7    # Minimum similarity score for retrieval
ENABLE_KNOWLEDGE_GRAPH=true     # Neo4j knowledge graph integration

# GitHub Copilot Enterprise Integration
GITHUB_COPILOT_ENABLED=true
GITHUB_COPILOT_MODEL=copilot-chat    # copilot-chat, copilot-code
COPILOT_SUGGESTIONS_COUNT=3     # Number of code suggestions
COPILOT_QUALITY_THRESHOLD=0.8   # Minimum quality score for suggestions
ENABLE_COPILOT_CONTEXT=true     # Include project context in requests
COPILOT_SECURITY_SCANNING=true  # Scan generated code for security issues

# LLM Performance & Optimization
LLM_BATCH_PROCESSING=true       # Enable batch processing for multiple requests
LLM_PARALLEL_REQUESTS=4         # Maximum parallel LLM requests
ENABLE_LLM_COMPRESSION=true     # Compress prompts for efficiency
LLM_RESPONSE_STREAMING=true     # Enable streaming responses
LLM_LOAD_BALANCING=true         # Distribute requests across multiple models
LLM_FAILOVER_ENABLED=true       # Automatic failover to backup models

# AI Agents & ML Configuration
ENABLE_AI_AGENTS=true
AGENT_AUTH_ENABLED=true
QUALITY_AGENT_ENABLED=true
WORKFLOW_AGENT_ENABLED=true
OPTIMIZATION_AGENT_ENABLED=true

# ML Optimization Settings
ENABLE_TENSORRT=false          # Requires TensorRT installation
ENABLE_MIXED_PRECISION=true    # FP16 optimization
ENABLE_MODEL_QUANTIZATION=false # INT8 quantization
BATCH_INFERENCE_SIZE=4         # Max batch size for inference
AUTO_MODEL_SELECTION=true      # Let agents choose optimal models

# Alternative AI Models
ENABLE_STABLE_DIFFUSION=true
ENABLE_COMFYUI_INTEGRATION=true
ENABLE_HUGGINGFACE_FALLBACK=true
CLIP_MODEL_NAME=openai/clip-vit-base-patch32
STABLE_DIFFUSION_MODEL=runwayml/stable-diffusion-v1-5

# Performance Monitoring
ENABLE_ML_METRICS=true
TRACK_AGENT_PERFORMANCE=true
AUTO_OPTIMIZATION=true
PERFORMANCE_THRESHOLD=0.8      # Trigger optimization below this score

# Web Technologies & Security
ENABLE_HTTPS=true
SSL_CERT_PATH=ssl/orfeas.crt
SSL_KEY_PATH=ssl/orfeas.key
ENABLE_HSTS=true
CSP_ENABLED=true
PWA_ENABLED=true
SERVICE_WORKER_ENABLED=true

# PHP Integration (Optional)
ENABLE_PHP_COMPATIBILITY=false
PHP_API_ENDPOINT=/php/api.php
PHP_SESSION_ENABLED=false

# Web Server Configuration
NGINX_SSL_ENABLED=true
STATIC_FILE_COMPRESSION=true
RATE_LIMITING_ENABLED=true
MAX_REQUESTS_PER_MINUTE=60

# Intelligent Context & Learning
ENABLE_CONTEXT_MANAGER=true
CONTEXT_PERSISTENCE_ENABLED=true
CONTEXT_LEARNING_ENABLED=true
CONTEXT_DB_PATH=data/context.db
AUTO_CONTEXT_OPTIMIZATION=true
CONTEXT_CACHE_SIZE=1000
CONTEXT_SIMILARITY_THRESHOLD=0.8
ENABLE_PREDICTIVE_INSIGHTS=true
CONTEXT_RETENTION_DAYS=90

# Performance & Security Optimization
ENABLE_PERFORMANCE_OPTIMIZER=true
AUTO_PERFORMANCE_TUNING=true
PERFORMANCE_MONITORING_ENABLED=true
PERFORMANCE_THRESHOLD=0.8          # Trigger optimization below this score
SPEED_OPTIMIZATION_ENABLED=true
ACCURACY_ENHANCEMENT_ENABLED=true
SECURITY_HARDENING_ENABLED=true

# Speed Optimization Settings
ENABLE_MODEL_COMPILATION=true      # torch.compile() optimization
ENABLE_CACHE_WARMING=true          # Pre-warm model caches
FAST_MODE_QUALITY_THRESHOLD=5      # Quality level for fast mode
BALANCED_MODE_QUALITY_THRESHOLD=7  # Quality level for balanced mode
QUALITY_MODE_QUALITY_THRESHOLD=9   # Quality level for quality mode

# Accuracy Enhancement Settings
ENABLE_ENSEMBLE_GENERATION=false   # Multi-model ensemble (resource intensive)
QUALITY_VALIDATION_THRESHOLD=0.85  # Minimum quality score threshold
ENABLE_AUTOMATIC_REFINEMENT=true   # Auto-refine low quality results
ENABLE_FEEDBACK_LEARNING=true      # Learn from user quality feedback
QUALITY_ASSESSMENT_ENABLED=true    # Pre-generation quality assessment

# TQM (Total Quality Management) Audit System
ENABLE_TQM_AUDITS=true              # Enable comprehensive quality audits
TQM_AUDIT_LEVEL=enterprise          # basic, professional, enterprise
AUTOMATED_AUDIT_SCHEDULING=true     # Schedule automatic audits
CONTINUOUS_QUALITY_MONITORING=true  # Real-time quality monitoring
QUALITY_GATE_ENFORCEMENT=true       # Enforce quality gates for requests

# Quality Audit Scheduling
DAILY_AUDITS_ENABLED=true           # Enable daily performance audits
WEEKLY_AUDITS_ENABLED=true          # Enable weekly process audits
MONTHLY_AUDITS_ENABLED=true         # Enable monthly comprehensive audits
QUARTERLY_AUDITS_ENABLED=true       # Enable quarterly strategic reviews
ANNUAL_AUDITS_ENABLED=true          # Enable annual certification audits

# Quality Standards & Compliance
ISO_9001_COMPLIANCE=true            # ISO 9001:2015 Quality Management
ISO_27001_COMPLIANCE=true           # ISO 27001:2022 Information Security
SOC2_TYPE2_COMPLIANCE=true          # SOC2 Type 2 Security Controls
SIX_SIGMA_METHODOLOGY=true          # Six Sigma process improvement
CMMI_LEVEL5_TARGET=true             # CMMI Level 5 capability maturity
LEAN_MANUFACTURING_PRINCIPLES=true  # Lean waste elimination

# Quality Metrics & Thresholds
OVERALL_QUALITY_THRESHOLD=0.85      # Overall quality score threshold
PERFORMANCE_QUALITY_THRESHOLD=0.90  # Performance quality threshold
RELIABILITY_QUALITY_THRESHOLD=0.95  # Reliability quality threshold
SECURITY_QUALITY_THRESHOLD=0.90     # Security quality threshold
COMPLIANCE_QUALITY_THRESHOLD=0.98   # Compliance quality threshold
USER_SATISFACTION_THRESHOLD=0.85    # User satisfaction threshold

# Quality Audit Reporting
AUTO_AUDIT_REPORT_GENERATION=true   # Automatically generate audit reports
EXECUTIVE_DASHBOARD_ENABLED=true    # Quality executive dashboard
STAKEHOLDER_NOTIFICATIONS=true      # Notify stakeholders of audit results
QUALITY_TREND_ANALYSIS=true         # Analyze quality trends over time
BENCHMARK_COMPARISONS=true          # Compare against industry benchmarks

# Quality Improvement Actions
AUTO_QUALITY_CORRECTION=true        # Automatic quality corrections
QUALITY_ALERT_SYSTEM=true           # Real-time quality alerts
PROACTIVE_QUALITY_MANAGEMENT=true   # Proactive quality issue prevention
QUALITY_TRAINING_RECOMMENDATIONS=true # Recommend quality training
BEST_PRACTICES_ENFORCEMENT=true     # Enforce quality best practices

# Advanced Quality Analytics
AI_POWERED_QUALITY_ANALYSIS=true    # AI-driven quality analysis
PREDICTIVE_QUALITY_MODELING=true    # Predict quality issues before they occur
QUALITY_PATTERN_RECOGNITION=true    # Recognize quality improvement patterns
CUSTOMER_FEEDBACK_INTEGRATION=true  # Integrate customer feedback into quality metrics
QUALITY_ROI_TRACKING=true           # Track return on investment for quality initiatives

# Security Hardening Settings
THREAT_DETECTION_ENABLED=true      # Real-time threat detection
SECURITY_AUDIT_LOGGING=true        # Comprehensive security logging
FILE_MALWARE_SCANNING=true         # Scan uploaded files for malware
REQUEST_SIGNATURE_VALIDATION=false # Validate signed requests
SECURITY_MONITORING_LEVEL=MEDIUM   # LOW, MEDIUM, HIGH, PARANOID

# ML Integration Optimization
ADAPTIVE_MODEL_SELECTION=true      # Intelligent model selection
PIPELINE_OPTIMIZATION_ENABLED=true # Optimize ML pipelines
RESOURCE_ALLOCATION_OPTIMIZATION=true # Optimize resource allocation
MODEL_PERFORMANCE_TRACKING=true    # Track model performance metrics
AUTO_MODEL_SWITCHING=true          # Switch models based on performance

# Enterprise Cloud Deployment & Scaling
CLOUD_PROVIDER=aws                  # aws, azure, gcp, hybrid
KUBERNETES_ENABLED=true             # Enable Kubernetes deployment
KUBERNETES_NAMESPACE=orfeas-prod    # K8s namespace for production
AUTO_SCALING_ENABLED=true           # Horizontal pod autoscaling
MIN_REPLICAS=2                      # Minimum pod replicas
MAX_REPLICAS=20                     # Maximum pod replicas
CPU_TARGET_UTILIZATION=70           # CPU target for autoscaling
MEMORY_TARGET_UTILIZATION=80        # Memory target for autoscaling

# Enterprise Monitoring & Observability
PROMETHEUS_ENABLED=true             # Enable Prometheus metrics
GRAFANA_ENABLED=true                # Enable Grafana dashboards
JAEGER_TRACING_ENABLED=true         # Distributed tracing
ELK_STACK_ENABLED=true              # Elasticsearch, Logstash, Kibana
DATADOG_INTEGRATION=false           # Datadog APM integration
NEW_RELIC_INTEGRATION=false         # New Relic monitoring

# Enterprise Database & Caching
DATABASE_TYPE=postgresql            # postgresql, mysql, mongodb
DATABASE_CLUSTER_ENABLED=true       # Database clustering
REDIS_CLUSTER_ENABLED=true          # Redis clustering for caching
DATABASE_BACKUP_ENABLED=true        # Automated database backups
DATABASE_ENCRYPTION_ENABLED=true    # Database encryption at rest

# Enterprise CI/CD & DevOps
GITHUB_ACTIONS_ENABLED=true         # GitHub Actions CI/CD
JENKINS_INTEGRATION=false           # Jenkins CI/CD
GITLAB_CI_INTEGRATION=false         # GitLab CI integration
DOCKER_REGISTRY=ghcr.io             # Container registry
HELM_CHARTS_ENABLED=true            # Helm for Kubernetes deployments
TERRAFORM_ENABLED=true              # Infrastructure as Code

# Enterprise API & Microservices
API_GATEWAY_ENABLED=true            # API Gateway for microservices
LOAD_BALANCER_TYPE=nginx            # nginx, haproxy, aws-alb
SERVICE_MESH_ENABLED=false          # Istio service mesh
CIRCUIT_BREAKER_ENABLED=true        # Circuit breaker pattern
API_RATE_LIMITING_ENABLED=true      # Enterprise API rate limiting
API_KEY_MANAGEMENT_ENABLED=true     # Enterprise API key management

# Enterprise Security & Compliance
OAUTH2_ENABLED=true                 # OAuth2 authentication
SAML_SSO_ENABLED=false             # SAML Single Sign-On
LDAP_INTEGRATION=false             # LDAP authentication
RBAC_ENABLED=true                  # Role-based access control
AUDIT_LOGGING_ENABLED=true         # Comprehensive audit logging
COMPLIANCE_FRAMEWORK=SOC2          # SOC2, ISO27001, GDPR, HIPAA
VULNERABILITY_SCANNING=true        # Automated vulnerability scanning
PENETRATION_TESTING=quarterly      # Penetration testing schedule

# Advanced Problem Identification & Automated Fixing
ENABLE_PROBLEM_DETECTION=true      # Enable intelligent problem detection
ENABLE_AUTOMATED_FIXING=true       # Enable automated problem resolution
PROBLEM_DETECTION_INTERVAL=30      # Problem detection check interval (seconds)
AUTO_FIX_CONFIDENCE_THRESHOLD=0.8  # Minimum confidence for automated fixes
ENABLE_PROACTIVE_MONITORING=true   # Enable proactive problem prevention
HEALTH_MONITORING_ENABLED=true     # Enable continuous health monitoring
ALERT_SYSTEM_ENABLED=true          # Enable alerting system
PROBLEM_LEARNING_ENABLED=true      # Enable learning from problem patterns
DIAGNOSTIC_LOGGING_ENABLED=true    # Enable detailed diagnostic logging
ERROR_RECOVERY_ENABLED=true        # Enable intelligent error recovery
MANUAL_INTERVENTION_THRESHOLD=3    # Max auto-fix attempts before manual intervention

# Multi-Encoding & Internationalization Settings
ENABLE_MULTI_ENCODING=true         # Enable multi-character encoding support
DEFAULT_ENCODING=utf-8             # Primary character encoding
ENCODING_AUTO_DETECTION=true       # Automatic encoding detection for files
ENCODING_FALLBACK_CHAIN=utf-8,latin-1,cp1252,ascii  # Fallback encoding order
UNICODE_NORMALIZATION=NFC          # Unicode normalization form (NFC, NFD, NFKC, NFKD)
BOM_DETECTION=true                 # Byte Order Mark detection
ENCODING_ERROR_HANDLING=replace    # strict, ignore, replace, xmlcharrefreplace
LANGUAGE_DETECTION_ENABLED=true    # Automatic language detection
DEFAULT_LANGUAGE=en-US             # Default language for processing
SUPPORTED_LANGUAGES=en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi  # Supported languages
I18N_LOCALE_PATH=./locales         # Path to internationalization files
I18N_DOMAIN=orfeas                 # Translation domain name
BABEL_CONFIG_FILE=babel.cfg        # Babel configuration file
GETTEXT_ENABLED=true               # Enable GNU gettext support
POLYGLOT_NLP_ENABLED=true          # Enable polyglot NLP processing
LANGCODES_NORMALIZATION=true       # Language code standardization

# Multi-Code Language Programming Settings
ENABLE_MULTI_LANGUAGE_SUPPORT=true    # Enable multi-programming language support
PRIMARY_LANGUAGES=python,javascript,typescript,html,css,sql  # Primary development languages
ENTERPRISE_LANGUAGES=php,java,csharp,go,rust,cpp  # Enterprise programming languages
SPECIALIZED_LANGUAGES=r,matlab,swift,kotlin,dart,wasm  # Specialized programming languages
INFRASTRUCTURE_LANGUAGES=bash,powershell,yaml,json,xml,toml  # Infrastructure & DevOps languages
DATABASE_LANGUAGES=sql,nosql,graphql,sparql  # Database query languages
LANGUAGE_SERVER_PROTOCOL=true     # Enable LSP integration
TREE_SITTER_ENABLED=true          # Universal syntax highlighting
ANTLR_PARSER_ENABLED=true         # Cross-language parser generator
PROTOBUF_ENABLED=true             # Protocol Buffers cross-language serialization
GRPC_ENABLED=true                 # Cross-language RPC framework
THRIFT_ENABLED=true               # Apache Thrift cross-language services
MULTI_LANGUAGE_AI_ENABLED=true    # AI-powered multi-language code generation
CODE_TRANSLATION_ENABLED=true     # Code translation between languages
CROSS_LANGUAGE_REFACTORING=true   # Cross-language refactoring support
LANGUAGE_INTEROP_ENABLED=true     # Language interoperability features
CODE_ANALYSIS_MULTI_LANG=true     # Multi-language code analysis
SYNTAX_VALIDATION_MULTI_LANG=true # Multi-language syntax validation
AUTO_LANGUAGE_DETECTION=true      # Automatic programming language detection
LANGUAGE_SPECIFIC_OPTIMIZATION=true # Language-specific performance optimizations
POLYGLOT_PROJECT_SUPPORT=true     # Support for polyglot programming projects
LANGUAGE_MIGRATION_TOOLS=true     # Tools for migrating code between languages

Config Access Pattern:

# backend/config.py
class Config:
    def __init__(self):
        self.config = {
            "processing": {
                "device": os.getenv("DEVICE", "auto"),
                "max_concurrent_jobs": int(os.getenv("MAX_CONCURRENT_JOBS", 3))
            }
        }

# Usage in modules
from config import Config
config = Config()
device = config.get("processing.device")  # auto, cuda, or cpu

[3.3] ERROR HANDLING PATTERNS

ERROR HANDLING PHILOSOPHY:

Log comprehensive error context
Intelligent problem detection and classification
Attempt automated fixes when confidence is high
Graceful recovery with fallback strategies
Proactive problem prevention
Emit WebSocket error notification with diagnostics
Return user-friendly error message with support guidance

ENHANCED PATTERN WITH AUTOMATED PROBLEM DETECTION:

@app.route('/api/generate-3d')
def generate_3d():
    try:
        # Primary path: Hunyuan3D-2.1
        mesh = hunyuan_processor.generate_shape(image)
    except torch.cuda.OutOfMemoryError as e:
        # Automatic problem detection and fixing
        error_context = {
            'error_type': 'GPU_MEMORY',
            'error_message': str(e),
            'system_state': get_gpu_stats()
        }

        problem_detector = IntelligentProblemDetector()
        fix_result = problem_detector.auto_fix_problems([{
            'category': 'GPU_MEMORY',
            'automated_fix': True,
            'solutions': ['clear_cache', 'reduce_batch_size']
        }])

        if fix_result['successful_fixes']:
            logger.info("[ORFEAS] GPU OOM auto-fixed, retrying generation")
            mesh = hunyuan_processor.generate_shape(image)
        else:
            logger.warning("[ORFEAS] GPU OOM - using fallback processor")
            mesh = FallbackProcessor().generate_shape(image)

    except FileNotFoundError as e:
        # Auto-detect and fix model file issues
        error_context = {
            'error_type': 'MODEL_FILES',
            'error_message': str(e),
            'file_path': getattr(e, 'filename', 'unknown')
        }

        problem_detector = IntelligentProblemDetector()
        detection_result = problem_detector.detect_and_classify_problems(error_context)

        if detection_result['automated_fixes_available']:
            fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])
            if fix_result['successful_fixes']:
                logger.info("[ORFEAS] Model files auto-fixed, retrying generation")
                mesh = hunyuan_processor.generate_shape(image)
            else:
                return jsonify({
                    'error': 'AI model not initialized',
                    'details': 'Please run setup_full_ai.py first',
                    'diagnostic_info': detection_result,
                    'auto_fix_attempted': True
                }), 500
        else:
            logger.error(f"[ORFEAS] Model files missing: {e}")
            return jsonify({
                'error': 'AI model not initialized',
                'details': 'Please run setup_full_ai.py first'
            }), 500

    except Exception as e:
        # Comprehensive error handling with problem detection
        error_context = {
            'error_type': type(e).__name__,
            'error_message': str(e),
            'traceback': traceback.format_exc(),
            'request_data': request.get_json() if request.is_json else None,
            'system_state': get_system_state(),
            'timestamp': datetime.utcnow().isoformat()
        }

        # Attempt automated problem detection and fixing
        problem_detector = IntelligentProblemDetector()
        detection_result = problem_detector.detect_and_classify_problems(error_context)

        fix_result = None
        if detection_result['automated_fixes_available']:
            fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])

            if fix_result['successful_fixes']:
                logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}")
                try:
                    # Retry the operation after fix
                    mesh = hunyuan_processor.generate_shape(image)
                except Exception as retry_error:
                    logger.error(f"[ORFEAS] Retry after fix failed: {retry_error}")

        logger.error(f"[ORFEAS] Unexpected error: {traceback.format_exc()}")
        socketio.emit('generation_error', {
            'message': 'Generation failed',
            'recoverable': bool(fix_result and fix_result['successful_fixes']),
            'diagnostic_info': detection_result,
            'auto_fix_attempted': fix_result is not None
        })

        return jsonify({
            'error': str(e),
            'error_id': generate_error_id(),
            'diagnostic_info': detection_result,
            'auto_fix_result': fix_result,
            'support_guidance': generate_user_guidance(detection_result)
        }), 500

[3.4] ASYNC JOB QUEUE USAGE

Batch Processing for Long Operations:

# backend/batch_processor.py
from batch_processor import AsyncJobQueue

async_queue = AsyncJobQueue(max_workers=3, gpu_mgr=gpu_manager)

# Submit job
job = async_queue.submit_job(
    job_type='3d_generation',
    image=image_data,
    quality=7,
    callback=on_complete
)

# Track progress
@socketio.on('check_job_status')
def check_status(job_id):
    status = async_queue.get_job_status(job_id)
    emit('job_status', {
        'progress': status.progress,
        'stage': status.current_stage,
        'eta': status.eta_seconds
    })

[4] DEVELOPER WORKFLOWS

[4.1] SETUP & INSTALLATION

Initial Setup:

# 1. Clone repository
git clone --recursive https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1.git
cd orfeas

# 2. Create Python virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1  # Windows
source .venv/bin/activate   # Linux/Mac

# 3. Install dependencies
cd backend
pip install -r requirements.txt
pip install -r requirements-production.txt  # For production features

# 4. Download Hunyuan3D-2.1 models
python download_models.py

# 5. Configure environment
copy .env.example .env
# Edit .env with your settings

# 6. Test installation
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

[4.2] DEVELOPMENT COMMANDS

Backend Server:

# Start backend (development)
cd backend
python main.py

# Start backend (production mode)
$env:FLASK_ENV="production"
python main.py

# Start with monitoring
docker-compose -f monitoring_stack/docker-compose-monitoring.yml up -d
python main.py

# Run tests
pytest                    # All tests
pytest -m unit           # Unit tests only
pytest -m integration    # Integration tests
pytest --cov=.           # With coverage

Docker Deployment:

# Full stack with GPU support
docker-compose up -d

# Build and restart
docker-compose up -d --build

# View logs
docker-compose logs -f backend
docker-compose logs -f frontend

# Stop all services
docker-compose down

# Monitor GPU usage
docker exec orfeas-backend-production nvidia-smi

[4.3] TESTING WORKFLOWS

Test Organization:

backend/tests/
├ conftest.py                    # Shared fixtures
├ unit/                          # Fast isolated tests
   ├ test_validation.py         # Input validation
   ├ test_config.py             # Configuration
   └ test_utils.py              # Utility functions
├ integration/                   # API integration tests
   ├ test_api_endpoints.py      # All endpoints
   ├ test_workflow.py           # Complete workflows
   └ test_formats.py            # Format conversion
├ security/                      # Security tests
   ├ test_security_fixes.py     # Security fixes (26 tests)
   └ test_vulnerabilities.py    # Attack vectors
└ performance/                   # Load/benchmark tests
    ├ test_load.py               # Concurrent requests
    └ test_benchmarks.py         # Performance metrics

Run Specific Tests:

# Security validation
pytest tests/security/test_security_fixes.py -v

# API integration
pytest tests/integration/test_api_endpoints.py

# Load testing
pytest tests/performance/test_load.py -v -s

# Coverage report
pytest --cov=backend --cov-report=html
start htmlcov/index.html  # View coverage

[4.3.1] TQM (TOTAL QUALITY MANAGEMENT) AUDIT TECHNIQUES

COMPREHENSIVE QUALITY AUDIT FRAMEWORK:

The ORFEAS platform implements enterprise-grade TQM audit techniques to ensure continuous quality improvement, compliance adherence, and operational excellence across all AI-powered multimedia generation processes.

# backend/tqm_audit_system.py
class TQMAuditSystem:
    """
    Advanced Total Quality Management audit system for enterprise operations
    """

    def __init__(self):
        self.audit_standards = {
            'iso_9001_2015': 'Quality Management Systems',
            'iso_27001_2022': 'Information Security Management',
            'soc2_type2': 'Security and Availability Controls',
            'six_sigma': 'Process Improvement and Defect Reduction',
            'cmmi_level5': 'Capability Maturity Model Integration',
            'lean_manufacturing': 'Waste Elimination and Efficiency'
        }

        self.quality_dimensions = {
            'performance': 'Speed, accuracy, and reliability metrics',
            'features': 'Functionality and capability assessment',
            'reliability': 'Consistency and error rate analysis',
            'conformance': 'Standards compliance verification',
            'durability': 'Long-term operation stability',
            'serviceability': 'Support and maintenance quality',
            'aesthetics': 'User experience and interface quality',
            'perceived_quality': 'Customer satisfaction metrics'
        }

    def conduct_comprehensive_quality_audit(self, audit_scope: str) -> Dict[str, Any]:
        """Execute comprehensive TQM audit across all quality dimensions"""

        audit_results = {
            'audit_metadata': {
                'audit_id': self.generate_audit_id(),
                'audit_scope': audit_scope,
                'audit_date': datetime.utcnow().isoformat(),
                'auditor_team': self.get_audit_team(),
                'audit_standards': list(self.audit_standards.keys())
            },
            'quality_assessments': {},
            'compliance_verification': {},
            'performance_benchmarks': {},
            'improvement_recommendations': {},
            'risk_assessments': {}
        }

        # 1. Performance Quality Audit
        audit_results['quality_assessments']['performance'] = self.audit_performance_quality()

        # 2. Process Quality Audit
        audit_results['quality_assessments']['process'] = self.audit_process_quality()

        # 3. Product Quality Audit
        audit_results['quality_assessments']['product'] = self.audit_product_quality()

        # 4. Service Quality Audit
        audit_results['quality_assessments']['service'] = self.audit_service_quality()

        # 5. Compliance Quality Audit
        audit_results['compliance_verification'] = self.audit_compliance_status()

        # 6. Security Quality Audit
        audit_results['quality_assessments']['security'] = self.audit_security_quality()

        # 7. AI Model Quality Audit
        audit_results['quality_assessments']['ai_models'] = self.audit_ai_model_quality()

        return audit_results

    def audit_performance_quality(self) -> Dict[str, Any]:
        """Audit system performance against quality benchmarks"""

        performance_metrics = {
            'response_time_analysis': {
                'api_endpoints': self.measure_api_response_times(),
                'ai_generation_speed': self.measure_generation_performance(),
                'database_query_performance': self.measure_db_performance(),
                'quality_targets': {
                    'api_p95_response_time': '<500ms',
                    'generation_completion_time': '<60s',
                    'database_query_time': '<100ms'
                }
            },
            'throughput_analysis': {
                'concurrent_users_supported': self.measure_concurrent_capacity(),
                'requests_per_second': self.measure_rps_capacity(),
                'resource_utilization': self.measure_resource_efficiency(),
                'quality_targets': {
                    'concurrent_users': '>1000',
                    'requests_per_second': '>100',
                    'cpu_utilization_efficiency': '>80%'
                }
            },
            'reliability_metrics': {
                'uptime_percentage': self.calculate_system_uptime(),
                'error_rates': self.analyze_error_patterns(),
                'mean_time_to_recovery': self.calculate_mttr(),
                'quality_targets': {
                    'system_uptime': '>99.95%',
                    'error_rate': '<0.1%',
                    'mttr': '<15min'
                }
            }
        }

        return {
            'metrics': performance_metrics,
            'quality_score': self.calculate_performance_quality_score(performance_metrics),
            'compliance_status': self.verify_performance_compliance(performance_metrics),
            'improvement_areas': self.identify_performance_improvements(performance_metrics)
        }

    def audit_process_quality(self) -> Dict[str, Any]:
        """Audit business processes for quality, efficiency, and compliance"""

        process_audit = {
            'development_processes': {
                'code_review_compliance': self.audit_code_review_process(),
                'testing_coverage_analysis': self.audit_testing_processes(),
                'deployment_process_quality': self.audit_deployment_practices(),
                'documentation_standards': self.audit_documentation_quality()
            },
            'operational_processes': {
                'incident_management': self.audit_incident_processes(),
                'change_management': self.audit_change_control(),
                'monitoring_effectiveness': self.audit_monitoring_processes(),
                'backup_recovery_procedures': self.audit_disaster_recovery()
            },
            'quality_control_processes': {
                'input_validation_procedures': self.audit_input_validation(),
                'output_quality_verification': self.audit_output_quality(),
                'automated_testing_pipeline': self.audit_ci_cd_quality(),
                'user_acceptance_testing': self.audit_uat_processes()
            }
        }

        return {
            'process_assessments': process_audit,
            'process_maturity_level': self.assess_process_maturity(),
            'compliance_gaps': self.identify_process_gaps(),
            'optimization_opportunities': self.identify_process_optimizations()
        }

    def audit_ai_model_quality(self) -> Dict[str, Any]:
        """Comprehensive audit of AI model quality and performance"""

        ai_model_audit = {
            'model_accuracy_assessment': {
                'hunyuan3d_accuracy': self.measure_3d_generation_accuracy(),
                'video_generation_quality': self.measure_video_quality(),
                'code_generation_accuracy': self.measure_code_quality(),
                'benchmark_comparisons': self.compare_against_benchmarks()
            },
            'model_bias_analysis': {
                'demographic_bias_testing': self.test_demographic_bias(),
                'cultural_bias_assessment': self.assess_cultural_bias(),
                'fairness_metrics': self.calculate_fairness_metrics(),
                'bias_mitigation_effectiveness': self.assess_bias_mitigation()
            },
            'model_robustness_testing': {
                'adversarial_input_resistance': self.test_adversarial_robustness(),
                'edge_case_handling': self.test_edge_cases(),
                'input_variation_stability': self.test_input_stability(),
                'error_handling_quality': self.test_error_scenarios()
            },
            'model_explainability': {
                'decision_transparency': self.assess_model_transparency(),
                'output_justification': self.test_explanation_quality(),
                'interpretability_scores': self.measure_interpretability(),
                'user_understanding_metrics': self.measure_user_comprehension()
            }
        }

        return {
            'ai_quality_metrics': ai_model_audit,
            'overall_ai_quality_score': self.calculate_ai_quality_score(ai_model_audit),
            'regulatory_compliance': self.verify_ai_compliance(),
            'ethical_ai_assessment': self.assess_ethical_compliance()
        }

    def generate_quality_audit_report(self, audit_results: Dict) -> Dict[str, Any]:
        """Generate comprehensive TQM audit report with actionable insights"""

        executive_summary = {
            'overall_quality_score': self.calculate_overall_quality_score(audit_results),
            'compliance_status': self.summarize_compliance_status(audit_results),
            'critical_findings': self.identify_critical_issues(audit_results),
            'improvement_priorities': self.prioritize_improvements(audit_results),
            'investment_recommendations': self.recommend_quality_investments(audit_results)
        }

        detailed_findings = {
            'quality_dimension_scores': self.score_quality_dimensions(audit_results),
            'process_maturity_assessment': self.assess_process_maturity_levels(audit_results),
            'risk_analysis': self.analyze_quality_risks(audit_results),
            'benchmark_comparisons': self.compare_industry_benchmarks(audit_results),
            'trend_analysis': self.analyze_quality_trends(audit_results)
        }

        action_plan = {
            'immediate_actions': self.define_immediate_actions(audit_results),
            'short_term_improvements': self.plan_short_term_improvements(audit_results),
            'long_term_strategic_initiatives': self.plan_strategic_initiatives(audit_results),
            'resource_requirements': self.estimate_resource_needs(audit_results),
            'success_metrics': self.define_success_metrics(audit_results)
        }

        return {
            'report_metadata': {
                'report_id': self.generate_report_id(),
                'generation_date': datetime.utcnow().isoformat(),
                'report_version': '1.0',
                'next_audit_due': self.calculate_next_audit_date()
            },
            'executive_summary': executive_summary,
            'detailed_findings': detailed_findings,
            'action_plan': action_plan,
            'appendices': {
                'raw_audit_data': audit_results,
                'compliance_evidence': self.compile_compliance_evidence(),
                'quality_metrics_history': self.retrieve_historical_metrics()
            }
        }

CONTINUOUS QUALITY MONITORING:

# backend/continuous_quality_monitor.py
class ContinuousQualityMonitor:
    """
    Real-time quality monitoring and continuous improvement system
    """

    def __init__(self):
        self.quality_thresholds = {
            'performance': {'warning': 0.8, 'critical': 0.6},
            'reliability': {'warning': 0.95, 'critical': 0.90},
            'security': {'warning': 0.9, 'critical': 0.8},
            'compliance': {'warning': 0.98, 'critical': 0.95}
        }

    def real_time_quality_monitoring(self):
        """Continuous monitoring of quality metrics with real-time alerting"""

        while True:
            try:
                # Collect real-time quality metrics
                current_metrics = {
                    'performance_metrics': self.collect_performance_metrics(),
                    'reliability_metrics': self.collect_reliability_metrics(),
                    'security_metrics': self.collect_security_metrics(),
                    'compliance_metrics': self.collect_compliance_metrics(),
                    'user_satisfaction_metrics': self.collect_satisfaction_metrics()
                }

                # Analyze quality trends
                quality_trends = self.analyze_quality_trends(current_metrics)

                # Detect quality degradation
                quality_issues = self.detect_quality_degradation(current_metrics)

                # Trigger automated responses
                if quality_issues:
                    self.trigger_quality_response(quality_issues)

                # Generate quality alerts
                alerts = self.generate_quality_alerts(current_metrics, quality_trends)

                if alerts:
                    self.send_quality_alerts(alerts)

                # Update quality dashboard
                self.update_quality_dashboard(current_metrics, quality_trends)

                time.sleep(30)  # Monitor every 30 seconds

            except Exception as e:
                logger.error(f"[TQM] Quality monitoring error: {e}")
                time.sleep(60)

    def automated_quality_improvement(self, quality_metrics: Dict) -> Dict:
        """Automated quality improvement based on continuous monitoring"""

        improvement_actions = {
            'performance_optimizations': [],
            'reliability_enhancements': [],
            'security_strengthening': [],
            'compliance_adjustments': []
        }

        # Performance improvements
        if quality_metrics['performance_score'] < self.quality_thresholds['performance']['warning']:
            improvement_actions['performance_optimizations'].extend([
                'enable_additional_caching',
                'optimize_database_queries',
                'scale_horizontal_instances',
                'implement_connection_pooling'
            ])

        # Reliability improvements
        if quality_metrics['reliability_score'] < self.quality_thresholds['reliability']['warning']:
            improvement_actions['reliability_enhancements'].extend([
                'implement_circuit_breakers',
                'enhance_error_handling',
                'improve_health_checks',
                'strengthen_monitoring'
            ])

        # Security improvements
        if quality_metrics['security_score'] < self.quality_thresholds['security']['warning']:
            improvement_actions['security_strengthening'].extend([
                'update_security_policies',
                'enhance_access_controls',
                'strengthen_encryption',
                'improve_audit_logging'
            ])

        return improvement_actions

QUALITY AUDIT AUTOMATION:

# backend/automated_audit_scheduler.py
class AutomatedAuditScheduler:
    """
    Automated scheduling and execution of quality audits
    """

    def __init__(self):
        self.audit_schedule = {
            'daily_audits': ['performance', 'security_baseline'],
            'weekly_audits': ['process_compliance', 'ai_model_quality'],
            'monthly_audits': ['comprehensive_tqm', 'regulatory_compliance'],
            'quarterly_audits': ['strategic_quality_review', 'external_audit_prep'],
            'annual_audits': ['full_certification_audit', 'quality_system_review']
        }

    def schedule_automated_audits(self):
        """Schedule and execute automated quality audits"""

        scheduler = BackgroundScheduler()

        # Daily audits
        scheduler.add_job(
            func=self.execute_performance_audit,
            trigger='cron',
            hour=2,  # 2 AM daily
            minute=0,
            id='daily_performance_audit'
        )

        # Weekly audits
        scheduler.add_job(
            func=self.execute_comprehensive_audit,
            trigger='cron',
            day_of_week='sun',
            hour=3,  # 3 AM Sunday
            minute=0,
            id='weekly_comprehensive_audit'
        )

        # Monthly audits
        scheduler.add_job(
            func=self.execute_tqm_audit,
            trigger='cron',
            day=1,  # First day of month
            hour=4,  # 4 AM
            minute=0,
            id='monthly_tqm_audit'
        )

        scheduler.start()

[4.4] DEBUGGING

Enable Debug Logging:

# backend/main.py
logging.basicConfig(level=logging.DEBUG)

# Or via environment
$env:LOG_LEVEL="DEBUG"
python main.py

GPU Debugging:

# Check GPU availability
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU device: {torch.cuda.get_device_name(0)}")
print(f"VRAM total: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")

# Monitor GPU memory
from gpu_manager import get_gpu_manager
gpu_mgr = get_gpu_manager()
print(gpu_mgr.get_gpu_stats())

Common Issues with Automated Detection:

# Issue: XFORMERS DLL crash (0xc0000139)
# Fix: Already handled via XFORMERS_DISABLED=1 in hunyuan_integration.py
# Auto-Detection: Pattern 'xformers.*DLL.*0xc0000139' triggers automatic XFORMERS disabling

# Issue: GPU out of memory
# Fix: Reduce concurrent jobs or enable low_vram_mode
$env:MAX_CONCURRENT_JOBS=1
$env:GPU_MEMORY_LIMIT=0.7
# Auto-Detection: OutOfMemoryError triggers automatic cache clearing and job reduction

# Issue: Models not found
# Fix: Verify Hunyuan3D-2.1 path
python backend/download_models.py
# Auto-Detection: FileNotFoundError with 'hunyuan3d' triggers automatic model download

# Issue: WebSocket disconnects
# Fix: Check CORS origins and ping timeout
$env:CORS_ORIGINS="*"
$env:WS_PING_TIMEOUT=60
# Auto-Detection: ConnectionError with 'websocket' triggers automatic CORS fix and restart

# Issue: Context processing failures
# Fix: Clear context cache and reset learning system
$env:CONTEXT_CACHE_SIZE=500
$env:CONTEXT_SIMILARITY_THRESHOLD=0.9
# Auto-Detection: Context-related errors trigger cache cleanup and threshold adjustment

# Issue: AI agent coordination failures
# Fix: Restart agent coordinator and verify authentication
$env:AGENT_AUTH_ENABLED=true
$env:QUALITY_AGENT_ENABLED=true
# Auto-Detection: Agent communication errors trigger automatic agent restart

# Issue: Text-to-Image generation failures
# Fix: Switch to alternative model and verify API keys
$env:TEXT_TO_IMAGE_MODEL=stable_diffusion_xl
$env:DALLE3_API_KEY=verify_key
$env:TEXT_TO_IMAGE_QUALITY=medium
# Auto-Detection: Image generation errors trigger model switching

# Issue: Text-to-Speech synthesis failures
# Fix: Switch TTS engine and verify voice models
$env:TTS_MODEL=azure_tts
$env:TTS_VOICE_ID=default
$env:AUDIO_QUALITY=medium
# Auto-Detection: TTS errors trigger engine switching and voice validation

# Issue: Speech-to-Text transcription errors
# Fix: Switch recognition engine and adjust audio quality
$env:SPEECH_MODEL=azure_speech
$env:SPEECH_LANGUAGE=en-US
$env:ENABLE_REAL_TIME_TRANSCRIPTION=false
# Auto-Detection: STT errors trigger engine switching and language detection

# Issue: Audio/Image file format compatibility
# Fix: Enable format conversion and validate file types
$env:ENABLE_AUDIO_CONVERSION=true
$env:ENABLE_IMAGE_CONVERSION=true
$env:MAX_AUDIO_DURATION=180
# Auto-Detection: Format errors trigger automatic conversion

# Issue: Multimedia model loading failures
# Fix: Download missing models and verify storage space
python backend/download_multimedia_models.py
$env:MULTIMEDIA_CACHE_SIZE=10
$env:MODEL_DOWNLOAD_RETRY=3
# Auto-Detection: Model loading errors trigger automatic download

# Issue: Model performance degradation
# Fix: Trigger model optimization and selection
$env:AUTO_MODEL_SWITCHING=true
$env:ADAPTIVE_MODEL_SELECTION=true
# Auto-Detection: Performance metrics below threshold trigger model optimization

# Issue: Security policy violations
# Fix: Update security configuration and audit logs
$env:SECURITY_MONITORING_LEVEL=HIGH
$env:AUDIT_LOGGING_ENABLED=true
# Auto-Detection: Security violations trigger automatic policy enforcement

AGENT-SPECIFIC DEBUGGING PATTERNS:

# Issue: Agent authentication failures
# Fix: Verify agent credentials and regenerate API keys
$env:AGENT_AUTH_ENABLED=true
$env:AGENT_API_KEY_REGENERATION=true
$env:AGENT_SECURITY_LEVEL="enterprise"
# Auto-Detection: 401/403 errors from agent endpoints trigger authentication debugging

# Issue: Agent communication timeouts
# Fix: Adjust timeout settings and check network connectivity
$env:AGENT_COMMUNICATION_TIMEOUT=30
$env:AGENT_RETRY_ATTEMPTS=3
$env:AGENT_HEARTBEAT_INTERVAL=10
# Auto-Detection: Communication timeout errors trigger automatic retry with extended timeout

# Issue: Agent load balancing failures
# Fix: Restart load balancer and redistribute agent instances
$env:AGENT_LOAD_BALANCING=true
$env:AGENT_HEALTH_CHECK_INTERVAL=5
$env:AGENT_FAILOVER_ENABLED=true
# Auto-Detection: Agent health check failures trigger automatic load redistribution

# Issue: Agent performance degradation
# Fix: Scale agent instances and optimize resource allocation
$env:AGENT_AUTO_SCALING=true
$env:AGENT_PERFORMANCE_THRESHOLD=0.8
$env:AGENT_RESOURCE_OPTIMIZATION=true
# Auto-Detection: Agent performance below 80% triggers automatic scaling

# Issue: Agent workflow orchestration failures
# Fix: Reset workflow state and restart orchestration engine
$env:WORKFLOW_ORCHESTRATION_ENABLED=true
$env:WORKFLOW_STATE_RECOVERY=true
$env:WORKFLOW_CHECKPOINT_INTERVAL=30
# Auto-Detection: Workflow interruption triggers automatic state recovery

# Issue: Agent memory leaks or resource exhaustion
# Fix: Restart agents and implement resource cleanup
$env:AGENT_MEMORY_LIMIT="2Gi"
$env:AGENT_RESOURCE_CLEANUP_INTERVAL=300
$env:AGENT_GRACEFUL_SHUTDOWN_TIMEOUT=30
# Auto-Detection: Agent memory usage above threshold triggers restart

# Issue: Agent learning system failures
# Fix: Reset learning state and rebuild experience buffer
$env:AGENT_LEARNING_ENABLED=true
$env:AGENT_EXPERIENCE_BUFFER_SIZE=1000
$env:AGENT_LEARNING_CHECKPOINT_INTERVAL=60
# Auto-Detection: Learning system errors trigger experience buffer reset

# Issue: Agent deployment failures in Kubernetes
# Fix: Check deployment manifests and resource availability
$env:KUBERNETES_AGENT_NAMESPACE="orfeas-agents"
$env:KUBERNETES_AGENT_REPLICAS=3
$env:KUBERNETES_AGENT_AUTO_RECOVERY=true
# Auto-Detection: Kubernetes deployment errors trigger manifest validation

# Issue: Agent service mesh connectivity issues
# Fix: Restart service mesh components and verify configuration
$env:AGENT_SERVICE_MESH_ENABLED=true
$env:ISTIO_SIDECAR_INJECTION=true
$env:SERVICE_MESH_TELEMETRY=true
# Auto-Detection: Service mesh errors trigger sidecar restart

# Issue: Agent monitoring and observability failures
# Fix: Restart monitoring agents and verify metric collection
$env:AGENT_MONITORING_ENABLED=true
$env:PROMETHEUS_AGENT_METRICS=true
$env:GRAFANA_AGENT_DASHBOARDS=true
# Auto-Detection: Missing agent metrics trigger monitoring system restart

AGENT DEBUGGING INTEGRATION:

# Enable agent-specific debugging in development
from agent_api import OrfeasAIAgent
from agent_monitoring import AgentPerformanceMonitor
from agent_discovery import AgentServiceDiscovery
from problem_detector import IntelligentProblemDetector

# Initialize agent debugging system
agent_monitor = AgentPerformanceMonitor()
agent_discovery = AgentServiceDiscovery()
problem_detector = IntelligentProblemDetector()

# Manual agent debugging
def debug_with_agent_analysis(agent_issue_or_exception):
    """Debug with comprehensive agent analysis"""

    # Build agent context
    agent_context = {
        'issue_type': type(agent_issue_or_exception).__name__,
        'issue_message': str(agent_issue_or_exception),
        'agent_states': agent_monitor.get_agent_states(),
        'agent_health': agent_discovery.check_agent_health(),
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Analyze agent performance
    performance_analysis = agent_monitor.analyze_agent_performance()

    print(f"[AGENT-DEBUG] Agent Performance Analysis:")
    print(f"  - Total Agents: {performance_analysis['total_agents']}")
    print(f"  - Healthy Agents: {performance_analysis['healthy_agents']}")
    print(f"  - Average Response Time: {performance_analysis['avg_response_time']:.2f}ms")
    print(f"  - Success Rate: {performance_analysis['success_rate']:.2f}%")

    # Check agent discovery
    agent_status = agent_discovery.get_discovery_status()
    print(f"[AGENT-DEBUG] Agent Discovery Status:")
    for agent_type, agents in agent_status.items():
        print(f"  - {agent_type}: {len(agents)} instances")
        for agent in agents:
            print(f"    * {agent['id']}: {agent['status']} ({agent['last_seen']})")

    # Detect agent-specific problems
    agent_problems = problem_detector.detect_agent_problems(agent_context)
    print(f"[AGENT-DEBUG] Detected Agent Problems: {len(agent_problems)}")
    for problem in agent_problems:
        print(f"  - {problem['category']}: {problem['description']}")

    # Apply automated agent fixes
    if agent_problems:
        print("[AGENT-DEBUG] Applying automated agent fixes...")
        fix_results = problem_detector.auto_fix_agent_problems(agent_problems)
        print(f"[AGENT-DEBUG] Applied fixes: {fix_results}")

    return {
        'performance_analysis': performance_analysis,
        'agent_status': agent_status,
        'detected_problems': agent_problems,
        'fix_results': fix_results if 'fix_results' in locals() else None
    }

# Agent-specific problem detection patterns
def detect_agent_specific_issues():
    """Detect agent-specific issues with automated patterns"""

    agent_issues = []

    # Check agent authentication status
    auth_status = agent_discovery.check_agent_authentication()
    for agent_id, auth_result in auth_status.items():
        if not auth_result['authenticated']:
            agent_issues.append({
                'category': 'AGENT_AUTHENTICATION',
                'severity': 'HIGH',
                'description': f"Agent {agent_id} authentication failed",
                'automated_fix': True,
                'solutions': ['regenerate_api_key', 'verify_credentials']
            })

    # Check agent communication
    comm_status = agent_discovery.check_agent_communication()
    for agent_id, comm_result in comm_status.items():
        if comm_result['response_time'] > 5000:  # > 5 seconds
            agent_issues.append({
                'category': 'AGENT_COMMUNICATION',
                'severity': 'MEDIUM',
                'description': f"Agent {agent_id} slow communication: {comm_result['response_time']}ms",
                'automated_fix': True,
                'solutions': ['restart_agent', 'optimize_network']
            })

    # Check agent resource usage
    resource_status = agent_monitor.check_agent_resources()
    for agent_id, resource_usage in resource_status.items():
        if resource_usage['memory_percent'] > 90:
            agent_issues.append({
                'category': 'AGENT_RESOURCE',
                'severity': 'HIGH',
                'description': f"Agent {agent_id} high memory usage: {resource_usage['memory_percent']}%",
                'automated_fix': True,
                'solutions': ['restart_agent', 'increase_memory_limit']
            })

    return agent_issues

# Auto-fix agent issues
def auto_fix_agent_issues(agent_issues: List[Dict]) -> Dict[str, Any]:
    """Automatically fix agent-specific issues"""

    fix_results = {
        'attempted_fixes': [],
        'successful_fixes': [],
        'failed_fixes': []
    }

    for issue in agent_issues:
        if issue.get('automated_fix', False):
            try:
                category = issue['category']
                solutions = issue.get('solutions', [])

                for solution in solutions:
                    if category == 'AGENT_AUTHENTICATION':
                        result = fix_agent_authentication_issue(solution, issue)
                    elif category == 'AGENT_COMMUNICATION':
                        result = fix_agent_communication_issue(solution, issue)
                    elif category == 'AGENT_RESOURCE':
                        result = fix_agent_resource_issue(solution, issue)
                    else:
                        result = fix_generic_agent_issue(solution, issue)

                    if result['success']:
                        fix_results['successful_fixes'].append({
                            'issue': issue,
                            'solution': solution,
                            'result': result
                        })
                        break  # Success, no need to try other solutions

                if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']):
                    fix_results['failed_fixes'].append(issue)

            except Exception as e:
                logger.error(f"[AGENT-DEBUG] Failed to fix agent issue {issue['category']}: {e}")
                fix_results['failed_fixes'].append(issue)

    return fix_results

def fix_agent_authentication_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent authentication issues"""

    if solution == 'regenerate_api_key':
        # Regenerate API key for agent
        agent_id = issue.get('agent_id')
        new_api_key = agent_discovery.regenerate_agent_api_key(agent_id)
        return {'success': True, 'details': f'New API key generated for {agent_id}'}

    elif solution == 'verify_credentials':
        # Verify and update agent credentials
        agent_id = issue.get('agent_id')
        verification_result = agent_discovery.verify_agent_credentials(agent_id)
        return {'success': verification_result, 'details': 'Credentials verified and updated'}

    return {'success': False, 'details': f'Unknown authentication solution: {solution}'}

def fix_agent_communication_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent communication issues"""

    if solution == 'restart_agent':
        # Restart specific agent
        agent_id = issue.get('agent_id')
        restart_result = agent_discovery.restart_agent(agent_id)
        return {'success': restart_result, 'details': f'Agent {agent_id} restarted'}

    elif solution == 'optimize_network':
        # Optimize network configuration for agent
        network_optimization = agent_discovery.optimize_agent_network()
        return {'success': True, 'details': 'Agent network optimized'}

    return {'success': False, 'details': f'Unknown communication solution: {solution}'}

def fix_agent_resource_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent resource issues"""

    if solution == 'restart_agent':
        # Restart agent to free resources
        agent_id = issue.get('agent_id')
        restart_result = agent_discovery.restart_agent(agent_id)
        return {'success': restart_result, 'details': f'Agent {agent_id} restarted for resource cleanup'}

    elif solution == 'increase_memory_limit':
        # Increase memory limit for agent
        agent_id = issue.get('agent_id')
        limit_result = agent_discovery.increase_agent_memory_limit(agent_id)
        return {'success': limit_result, 'details': f'Memory limit increased for {agent_id}'}

    return {'success': False, 'details': f'Unknown resource solution: {solution}'}

Problem Detection Integration:

# Enable automated problem detection in development
from problem_detector import IntelligentProblemDetector
from problem_prevention import ProactiveProblemPrevention

# Initialize problem detection system
problem_detector = IntelligentProblemDetector()
prevention_system = ProactiveProblemPrevention()

# Start proactive monitoring
if os.getenv('ENABLE_PROACTIVE_MONITORING', 'true').lower() == 'true':
    prevention_system.start_continuous_monitoring()

# Manual problem detection for debugging
def debug_with_problem_detection(error_or_exception):
    """Debug with intelligent problem detection"""
    error_context = {
        'error_type': type(error_or_exception).__name__,
        'error_message': str(error_or_exception),
        'traceback': traceback.format_exc() if hasattr(error_or_exception, '__traceback__') else None,
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Detect problems
    detection_result = problem_detector.detect_and_classify_problems(error_context)

    print(f"[DEBUG] Detected Problems: {len(detection_result['detected_problems'])}")
    for problem in detection_result['detected_problems']:
        print(f"  - {problem['category']}: {problem['pattern']} (Confidence: {problem.get('confidence', 0):.2f})")

    # Attempt fixes
    if detection_result['automated_fixes_available']:
        fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])
        print(f"[DEBUG] Fix Results: {fix_result}")

    return detection_result

[4.5] ADVANCED PROBLEM IDENTIFICATION & AUTOMATED FIXING

INTELLIGENT PROBLEM DETECTION SYSTEM:

# backend/problem_detector.py
class IntelligentProblemDetector:
    """
    Advanced problem identification and automated fixing system
    """

    def __init__(self):
        self.problem_patterns = self.load_problem_database()
        self.solution_engine = AutomatedSolutionEngine()
        self.learning_system = ProblemLearningSystem()

    def detect_and_classify_problems(self, error_context: Dict) -> Dict[str, Any]:
        """Detect and classify problems with confidence scoring"""

        detected_problems = []

        # 1. Error pattern matching
        pattern_matches = self.match_error_patterns(error_context)

        # 2. System state analysis
        system_issues = self.analyze_system_state()

        # 3. Performance degradation detection
        performance_issues = self.detect_performance_issues()

        # 4. Resource constraint analysis
        resource_issues = self.analyze_resource_constraints()

        # 5. Integration point failures
        integration_issues = self.check_integration_points()

        all_issues = {
            'error_patterns': pattern_matches,
            'system_state': system_issues,
            'performance': performance_issues,
            'resources': resource_issues,
            'integrations': integration_issues
        }

        # Classify and prioritize problems
        classified_problems = self.classify_problems(all_issues)

        return {
            'detected_problems': classified_problems,
            'confidence_scores': self.calculate_confidence_scores(classified_problems),
            'severity_levels': self.assess_severity(classified_problems),
            'automated_fixes_available': self.check_automated_fixes(classified_problems)
        }

    def match_error_patterns(self, error_context: Dict) -> List[Dict]:
        """Match errors against known problem patterns"""

        patterns = [
            {
                'pattern': 'torch.cuda.OutOfMemoryError',
                'category': 'GPU_MEMORY',
                'severity': 'HIGH',
                'automated_fix': True,
                'solutions': ['reduce_batch_size', 'enable_cpu_fallback', 'clear_cache']
            },
            {
                'pattern': 'FileNotFoundError.*hunyuan3d',
                'category': 'MODEL_FILES',
                'severity': 'CRITICAL',
                'automated_fix': True,
                'solutions': ['download_models', 'verify_paths', 'check_permissions']
            },
            {
                'pattern': 'ConnectionError.*websocket',
                'category': 'NETWORK',
                'severity': 'MEDIUM',
                'automated_fix': True,
                'solutions': ['restart_websocket', 'check_cors', 'verify_ports']
            },
            {
                'pattern': 'xformers.*DLL.*0xc0000139',
                'category': 'DEPENDENCY',
                'severity': 'HIGH',
                'automated_fix': True,
                'solutions': ['disable_xformers', 'reinstall_dependencies']
            }
        ]

        matches = []
        error_message = error_context.get('error_message', '')

        for pattern in patterns:
            if re.search(pattern['pattern'], error_message, re.IGNORECASE):
                matches.append({
                    **pattern,
                    'matched_text': error_message,
                    'confidence': self.calculate_pattern_confidence(pattern, error_context)
                })

        return matches

    def auto_fix_problems(self, detected_problems: List[Dict]) -> Dict[str, Any]:
        """Automatically fix detected problems"""

        fix_results = {
            'attempted_fixes': [],
            'successful_fixes': [],
            'failed_fixes': [],
            'manual_intervention_required': []
        }

        for problem in detected_problems:
            if problem.get('automated_fix', False):
                fix_result = self.attempt_automated_fix(problem)
                fix_results['attempted_fixes'].append({
                    'problem': problem,
                    'result': fix_result
                })

                if fix_result['success']:
                    fix_results['successful_fixes'].append(problem)
                else:
                    fix_results['failed_fixes'].append(problem)
            else:
                fix_results['manual_intervention_required'].append(problem)

        return fix_results

    def attempt_automated_fix(self, problem: Dict) -> Dict[str, Any]:
        """Attempt to automatically fix a specific problem"""

        category = problem['category']
        solutions = problem.get('solutions', [])

        for solution in solutions:
            try:
                if category == 'GPU_MEMORY':
                    result = self.fix_gpu_memory_issue(solution)
                elif category == 'MODEL_FILES':
                    result = self.fix_model_files_issue(solution)
                elif category == 'NETWORK':
                    result = self.fix_network_issue(solution)
                elif category == 'DEPENDENCY':
                    result = self.fix_dependency_issue(solution)
                else:
                    result = self.fix_generic_issue(solution, problem)

                if result['success']:
                    return {
                        'success': True,
                        'solution_applied': solution,
                        'details': result['details']
                    }

            except Exception as e:
                logger.warning(f"[ORFEAS] Failed to apply solution {solution}: {e}")
                continue

        return {
            'success': False,
            'attempted_solutions': solutions,
            'error': 'All automated solutions failed'
        }

    def fix_gpu_memory_issue(self, solution: str) -> Dict[str, Any]:
        """Fix GPU memory related issues"""

        if solution == 'reduce_batch_size':
            # Reduce concurrent processing
            os.environ['MAX_CONCURRENT_JOBS'] = '1'
            return {'success': True, 'details': 'Reduced concurrent jobs to 1'}

        elif solution == 'enable_cpu_fallback':
            # Enable CPU fallback mode
            os.environ['ENABLE_CPU_FALLBACK'] = 'true'
            return {'success': True, 'details': 'Enabled CPU fallback processing'}

        elif solution == 'clear_cache':
            # Clear GPU cache
            import torch
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
                torch.cuda.synchronize()
            return {'success': True, 'details': 'Cleared GPU cache'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_model_files_issue(self, solution: str) -> Dict[str, Any]:
        """Fix model files related issues"""

        if solution == 'download_models':
            # Attempt to download missing models
            try:
                subprocess.run(['python', 'backend/download_models.py'], check=True)
                return {'success': True, 'details': 'Models downloaded successfully'}
            except subprocess.CalledProcessError as e:
                return {'success': False, 'details': f'Model download failed: {e}'}

        elif solution == 'verify_paths':
            # Verify and fix model paths
            hunyuan_path = os.getenv('HUNYUAN3D_PATH', '../Hunyuan3D-2.1')
            if os.path.exists(hunyuan_path):
                return {'success': True, 'details': 'Model paths verified'}
            else:
                return {'success': False, 'details': f'Model path not found: {hunyuan_path}'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_network_issue(self, solution: str) -> Dict[str, Any]:
        """Fix network and connectivity issues"""

        if solution == 'restart_websocket':
            # Restart WebSocket connections
            try:
                # Implementation depends on your WebSocket framework
                return {'success': True, 'details': 'WebSocket connections restarted'}
            except Exception as e:
                return {'success': False, 'details': f'WebSocket restart failed: {e}'}

        elif solution == 'check_cors':
            # Fix CORS configuration
            os.environ['CORS_ORIGINS'] = '*'
            return {'success': True, 'details': 'CORS origins updated'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_dependency_issue(self, solution: str) -> Dict[str, Any]:
        """Fix dependency and environment issues"""

        if solution == 'disable_xformers':
            # Disable XFORMERS to prevent DLL crashes
            os.environ['XFORMERS_DISABLED'] = '1'
            return {'success': True, 'details': 'XFORMERS disabled'}

        elif solution == 'reinstall_dependencies':
            # Attempt to reinstall problematic dependencies
            try:
                subprocess.run(['pip', 'install', '--force-reinstall', 'torch', 'torchvision'], check=True)
                return {'success': True, 'details': 'Dependencies reinstalled'}
            except subprocess.CalledProcessError as e:
                return {'success': False, 'details': f'Dependency reinstall failed: {e}'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

PROACTIVE PROBLEM PREVENTION:

# backend/problem_prevention.py
class ProactiveProblemPrevention:
    """
    Proactive problem prevention and system health monitoring
    """

    def __init__(self):
        self.health_monitors = self.setup_health_monitors()
        self.prevention_rules = self.load_prevention_rules()
        self.alert_system = AlertSystem()

    def continuous_health_monitoring(self):
        """Continuously monitor system health and prevent problems"""

        while True:
            try:
                # 1. Monitor GPU health
                gpu_health = self.monitor_gpu_health()
                if gpu_health['risk_level'] > 0.7:
                    self.prevent_gpu_issues(gpu_health)

                # 2. Monitor memory usage
                memory_health = self.monitor_memory_usage()
                if memory_health['usage_percent'] > 85:
                    self.prevent_memory_issues(memory_health)

                # 3. Monitor model performance
                model_health = self.monitor_model_performance()
                if model_health['degradation_detected']:
                    self.prevent_performance_degradation(model_health)

                # 4. Monitor file system
                fs_health = self.monitor_file_system()
                if fs_health['issues_detected']:
                    self.prevent_file_system_issues(fs_health)

                # 5. Monitor network connectivity
                network_health = self.monitor_network_health()
                if network_health['connectivity_issues']:
                    self.prevent_network_issues(network_health)

                time.sleep(30)  # Check every 30 seconds

            except Exception as e:
                logger.error(f"[ORFEAS] Health monitoring error: {e}")
                time.sleep(60)  # Wait longer on error

    def prevent_gpu_issues(self, gpu_health: Dict):
        """Prevent GPU-related problems before they occur"""

        if gpu_health['memory_usage'] > 0.9:
            # Preemptively clear cache
            import torch
            torch.cuda.empty_cache()
            logger.info("[ORFEAS] Preemptive GPU cache clear performed")

        if gpu_health['temperature'] > 80:
            # Reduce processing intensity
            os.environ['GPU_INTENSITY_LIMIT'] = '0.7'
            self.alert_system.send_alert('GPU temperature high', 'warning')

        if gpu_health['utilization'] > 0.95:
            # Queue new requests instead of processing immediately
            self.activate_request_queuing()

    def smart_error_recovery(self, error: Exception, context: Dict) -> Dict[str, Any]:
        """Intelligent error recovery with learning capabilities"""

        # 1. Analyze error context
        error_analysis = self.analyze_error_context(error, context)

        # 2. Check historical recovery patterns
        historical_solutions = self.get_historical_solutions(error_analysis)

        # 3. Apply most successful historical solution first
        if historical_solutions:
            recovery_result = self.apply_historical_solution(
                historical_solutions[0], error, context
            )
            if recovery_result['success']:
                return recovery_result

        # 4. Attempt intelligent recovery strategies
        recovery_strategies = self.generate_recovery_strategies(error_analysis)

        for strategy in recovery_strategies:
            try:
                recovery_result = self.execute_recovery_strategy(strategy, error, context)
                if recovery_result['success']:
                    # Learn from successful recovery
                    self.learn_from_recovery(error_analysis, strategy, recovery_result)
                    return recovery_result
            except Exception as recovery_error:
                logger.warning(f"[ORFEAS] Recovery strategy failed: {recovery_error}")
                continue

        # 5. Escalate to manual intervention
        return {
            'success': False,
            'recovery_attempted': True,
            'escalation_required': True,
            'manual_intervention_steps': self.generate_manual_steps(error_analysis)
        }

AUTOMATED DIAGNOSTICS INTEGRATION:

# Integration with existing error handling
@app.errorhandler(Exception)
def handle_exception(error):
    """Enhanced error handler with automated problem detection and fixing"""

    # 1. Capture comprehensive error context
    error_context = {
        'error_type': type(error).__name__,
        'error_message': str(error),
        'traceback': traceback.format_exc(),
        'request_data': request.get_json() if request.is_json else None,
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # 2. Detect and classify problems
    problem_detector = IntelligentProblemDetector()
    detection_result = problem_detector.detect_and_classify_problems(error_context)

    # 3. Attempt automated fixes
    if detection_result['automated_fixes_available']:
        fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])

        if fix_result['successful_fixes']:
            logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}")
            # Retry the operation if fix was successful
            return retry_operation_after_fix(fix_result)

    # 4. Smart error recovery
    prevention_system = ProactiveProblemPrevention()
    recovery_result = prevention_system.smart_error_recovery(error, error_context)

    if recovery_result['success']:
        return recovery_result['response']

    # 5. Enhanced error response with diagnostic information
    return jsonify({
        'error': str(error),
        'error_id': generate_error_id(),
        'diagnostic_info': {
            'detected_problems': detection_result['detected_problems'],
            'attempted_fixes': fix_result if 'fix_result' in locals() else None,
            'recovery_attempted': recovery_result['recovery_attempted'],
            'escalation_required': recovery_result.get('escalation_required', False)
        },
        'user_guidance': generate_user_guidance(detection_result),
        'support_info': generate_support_information(error_context)
    }), 500

TQM-SPECIFIC DEBUGGING COMMANDS:

# TQM Quality Issue Detection and Resolution

# Issue: Quality audit failures or low quality scores
# Fix: Trigger comprehensive quality audit and optimization
$env:ENABLE_TQM_AUDITS="true"
$env:TQM_AUDIT_LEVEL="enterprise"
$env:CONTINUOUS_QUALITY_MONITORING="true"
# Auto-Detection: Quality scores below threshold trigger automatic audit and improvement

# Issue: Performance quality degradation
# Fix: Enable performance optimization and monitoring
$env:ENABLE_PERFORMANCE_OPTIMIZER="true"
$env:AUTO_PERFORMANCE_TUNING="true"
$env:PERFORMANCE_THRESHOLD="0.8"
# Auto-Detection: Performance metrics below 80% trigger automatic optimization

# Issue: Reliability quality issues
# Fix: Enhance error handling and monitoring
$env:RELIABILITY_QUALITY_THRESHOLD="0.95"
$env:ERROR_RECOVERY_ENABLED="true"
$env:HEALTH_MONITORING_ENABLED="true"
# Auto-Detection: Error rates above 5% trigger reliability enhancement

# Issue: Security quality violations
# Fix: Strengthen security controls and audit logging
$env:SECURITY_HARDENING_ENABLED="true"
$env:SECURITY_MONITORING_LEVEL="HIGH"
$env:AUDIT_LOGGING_ENABLED="true"
# Auto-Detection: Security violations trigger automatic hardening

# Issue: Compliance quality failures
# Fix: Update compliance framework and validation
$env:ISO_9001_COMPLIANCE="true"
$env:SOC2_TYPE2_COMPLIANCE="true"
$env:COMPLIANCE_QUALITY_THRESHOLD="0.98"
# Auto-Detection: Compliance failures trigger automatic validation update

# Issue: User satisfaction quality issues
# Fix: Enable user feedback integration and satisfaction monitoring
$env:USER_SATISFACTION_THRESHOLD="0.85"
$env:CUSTOMER_FEEDBACK_INTEGRATION="true"
$env:QUALITY_ROI_TRACKING="true"
# Auto-Detection: User satisfaction below 85% trigger improvement actions

# Issue: Quality audit scheduling failures
# Fix: Reset audit scheduler and verify configuration
$env:AUTOMATED_AUDIT_SCHEDULING="true"
$env:DAILY_AUDITS_ENABLED="true"
$env:QUALITY_ALERT_SYSTEM="true"
# Auto-Detection: Audit scheduling errors trigger automatic scheduler restart

# Issue: Quality metrics collection failures
# Fix: Restart metrics collector and verify database connection
$env:QUALITY_METRICS_COLLECTOR="enabled"
$env:QUALITY_TREND_ANALYSIS="true"
$env:BENCHMARK_COMPARISONS="true"
# Auto-Detection: Metrics collection errors trigger collector restart

# Issue: Quality improvement actions not applied
# Fix: Enable automated quality correction and alert system
$env:AUTO_QUALITY_CORRECTION="true"
$env:PROACTIVE_QUALITY_MANAGEMENT="true"
$env:QUALITY_IMPROVEMENT_ENGINE="enabled"
# Auto-Detection: Quality degradation triggers automatic improvement actions

# Issue: TQM audit report generation failures
# Fix: Reset report generator and verify template access
$env:AUTO_AUDIT_REPORT_GENERATION="true"
$env:EXECUTIVE_DASHBOARD_ENABLED="true"
$env:STAKEHOLDER_NOTIFICATIONS="true"
# Auto-Detection: Report generation errors trigger template verification

# Issue: Quality standards compliance violations
# Fix: Update compliance validation and trigger corrective actions
$env:SIX_SIGMA_METHODOLOGY="true"
$env:CMMI_LEVEL5_TARGET="true"
$env:LEAN_MANUFACTURING_PRINCIPLES="true"
# Auto-Detection: Standards violations trigger compliance update

# Issue: Quality gate enforcement failures
# Fix: Reset quality gateway middleware and verify thresholds
$env:QUALITY_GATE_ENFORCEMENT="true"
$env:OVERALL_QUALITY_THRESHOLD="0.85"
$env:QUALITY_ALERT_SYSTEM="true"
# Auto-Detection: Gate enforcement errors trigger middleware restart

# Issue: Predictive quality modeling failures
# Fix: Retrain models and update prediction algorithms
$env:AI_POWERED_QUALITY_ANALYSIS="true"
$env:PREDICTIVE_QUALITY_MODELING="true"
$env:QUALITY_PATTERN_RECOGNITION="true"
# Auto-Detection: Modeling failures trigger automatic model retraining

TQM Quality Debugging Integration:

# Enable TQM debugging in development
from tqm_audit_system import TQMAuditSystem
from continuous_quality_monitor import ContinuousQualityMonitor
from problem_detector import IntelligentProblemDetector

# Initialize TQM debugging system
tqm_audit = TQMAuditSystem()
quality_monitor = ContinuousQualityMonitor()
problem_detector = IntelligentProblemDetector()

# Manual TQM quality debugging
def debug_with_tqm_quality_analysis(quality_issue_or_exception):
    """Debug with comprehensive TQM quality analysis"""

    # Build quality context
    quality_context = {
        'issue_type': type(quality_issue_or_exception).__name__,
        'issue_message': str(quality_issue_or_exception),
        'quality_metrics': quality_monitor.collect_current_metrics(),
        'audit_status': tqm_audit.get_audit_status(),
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Conduct comprehensive quality audit
    audit_results = tqm_audit.conduct_comprehensive_quality_audit('debug_session')

    print(f"[TQM-DEBUG] Quality Audit Results:")
    print(f"  - Overall Quality Score: {audit_results['executive_summary']['overall_quality_score']:.2f}")
    print(f"  - Performance Quality: {audit_results['quality_assessments']['performance']['quality_score']:.2f}")
    print(f"  - Reliability Quality: {audit_results['quality_assessments']['reliability']['quality_score']:.2f}")
    print(f"  - Security Quality: {audit_results['quality_assessments']['security']['quality_score']:.2f}")

    # Identify quality improvement opportunities
    improvements = audit_results['improvement_recommendations']
    print(f"[TQM-DEBUG] Quality Improvement Opportunities: {len(improvements)}")
    for improvement in improvements[:5]:  # Show top 5
        print(f"  - {improvement['category']}: {improvement['description']}")

    # Apply automated quality corrections
    if audit_results['executive_summary']['overall_quality_score'] < 0.8:
        print("[TQM-DEBUG] Applying automated quality corrections...")
        quality_corrections = quality_monitor.automated_quality_improvement(quality_context)
        print(f"[TQM-DEBUG] Applied corrections: {quality_corrections}")

    return audit_results

# TQM quality issue detection patterns
def detect_tqm_quality_issues():
    """Detect TQM-specific quality issues"""

    quality_issues = []

    # Check audit compliance
    audit_status = tqm_audit.get_audit_status()
    if audit_status.get('overdue_audits', 0) > 0:
        quality_issues.append({
            'category': 'AUDIT_COMPLIANCE',
            'severity': 'HIGH',
            'description': f"{audit_status['overdue_audits']} audits overdue",
            'automated_fix': True,
            'solutions': ['schedule_immediate_audit', 'update_audit_calendar']
        })

    # Check quality thresholds
    current_quality = quality_monitor.assess_current_quality()
    for dimension, score in current_quality.items():
        if score < 0.8:  # Below quality threshold
            quality_issues.append({
                'category': f'QUALITY_{dimension.upper()}',
                'severity': 'MEDIUM' if score > 0.6 else 'HIGH',
                'description': f"{dimension} quality below threshold: {score:.2f}",
                'automated_fix': True,
                'solutions': [f'improve_{dimension}_quality', 'trigger_quality_review']
            })

    # Check compliance status
    compliance_status = tqm_audit.check_compliance_status()
    for standard, status in compliance_status.items():
        if not status.get('compliant', True):
            quality_issues.append({
                'category': 'COMPLIANCE_VIOLATION',
                'severity': 'CRITICAL',
                'description': f"{standard} compliance violation detected",
                'automated_fix': True,
                'solutions': ['update_compliance_controls', 'schedule_compliance_audit']
            })

    return quality_issues

# Auto-fix TQM quality issues
def auto_fix_tqm_quality_issues(quality_issues: List[Dict]) -> Dict[str, Any]:
    """Automatically fix TQM quality issues"""

    fix_results = {
        'attempted_fixes': [],
        'successful_fixes': [],
        'failed_fixes': []
    }

    for issue in quality_issues:
        if issue.get('automated_fix', False):
            try:
                category = issue['category']
                solutions = issue.get('solutions', [])

                for solution in solutions:
                    if category.startswith('AUDIT_'):
                        result = fix_audit_issue(solution, issue)
                    elif category.startswith('QUALITY_'):
                        result = fix_quality_issue(solution, issue)
                    elif category.startswith('COMPLIANCE_'):
                        result = fix_compliance_issue(solution, issue)
                    else:
                        result = fix_generic_quality_issue(solution, issue)

                    if result['success']:
                        fix_results['successful_fixes'].append({
                            'issue': issue,
                            'solution': solution,
                            'result': result
                        })
                        break  # Success, no need to try other solutions

                if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']):
                    fix_results['failed_fixes'].append(issue)

            except Exception as e:
                logger.error(f"[TQM-DEBUG] Failed to fix quality issue {issue['category']}: {e}")
                fix_results['failed_fixes'].append(issue)

    return fix_results

def fix_audit_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix audit-related quality issues"""

    if solution == 'schedule_immediate_audit':
        # Schedule immediate audit
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.schedule_immediate_audit('comprehensive_tqm')
        return {'success': True, 'details': 'Immediate audit scheduled'}

    elif solution == 'update_audit_calendar':
        # Update audit calendar
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.update_audit_schedule()
        return {'success': True, 'details': 'Audit calendar updated'}

    return {'success': False, 'details': f'Unknown audit solution: {solution}'}

def fix_quality_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix quality dimension issues"""

    if solution.startswith('improve_') and solution.endswith('_quality'):
        dimension = solution.replace('improve_', '').replace('_quality', '')

        # Apply dimension-specific improvements
        improvement_actions = quality_monitor.automated_quality_improvement({
            f'{dimension}_score': issue.get('current_score', 0.5)
        })

        return {'success': True, 'details': f'Quality improvement applied for {dimension}'}

    elif solution == 'trigger_quality_review':
        # Trigger comprehensive quality review
        review_result = tqm_audit.conduct_quality_review(issue)
        return {'success': True, 'details': 'Quality review triggered'}

    return {'success': False, 'details': f'Unknown quality solution: {solution}'}

def fix_compliance_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix compliance-related quality issues"""

    if solution == 'update_compliance_controls':
        # Update compliance controls
        compliance_manager = ComplianceManager()
        compliance_manager.update_controls_for_standard(issue.get('standard'))
        return {'success': True, 'details': 'Compliance controls updated'}

    elif solution == 'schedule_compliance_audit':
        # Schedule compliance-specific audit
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.schedule_compliance_audit(issue.get('standard'))
        return {'success': True, 'details': 'Compliance audit scheduled'}

    return {'success': False, 'details': f'Unknown compliance solution: {solution}'}

[5] PROJECT-SPECIFIC PATTERNS

[5.1] HUNYUAN3D MODEL CACHING

CRITICAL PERFORMANCE OPTIMIZATION:

Hunyuan3D models take 30-36 seconds to load initially. The

_model_cache

singleton pattern reduces subsequent loads to <1 second (94% speed improvement).

# CORRECT: Check cache before loading
class Hunyuan3DProcessor:
    _model_cache = {'shapegen_pipeline': None, 'initialized': False}
    _cache_lock = threading.Lock()

    def __init__(self, device=None):
        with Hunyuan3DProcessor._cache_lock:
            if Hunyuan3DProcessor._model_cache['initialized']:
                # Instant load from cache
                self.shapegen_pipeline = self._model_cache['shapegen_pipeline']
                return
        self.initialize_model()  # First-time load

# WRONG: Loading models every time
def generate_3d():
    processor = Hunyuan3DProcessor()  # 30s load every request!
    return processor.generate(image)

[5.2] GPU MEMORY PATTERNS

ALWAYS clean up GPU memory after generation:

# CORRECT: Proper cleanup
def generate_3d_model(image):
    try:
        mesh = processor.generate_shape(image)
        return mesh
    finally:
        torch.cuda.empty_cache()  # CRITICAL: Free unused memory

# WRONG: Memory leak
def generate_3d_model(image):
    mesh = processor.generate_shape(image)
    return mesh  # GPU memory not released!

Monitor GPU before processing:

from gpu_manager import get_gpu_manager

gpu_mgr = get_gpu_manager()
if gpu_mgr.can_process_job(estimated_vram=6000):
    with gpu_mgr.allocate_job(job_id):
        result = generate()
else:
    raise ResourceError("Insufficient GPU memory")

[5.3] ASYNC JOB PATTERNS

Long-running operations MUST use AsyncJobQueue:

# CORRECT: Async processing for 3D generation
from batch_processor import get_async_queue

async_queue = get_async_queue()
job = async_queue.submit_job(
    job_type='3d_generation',
    image=image,
    quality=7
)

# Return job ID immediately
return jsonify({'job_id': job.id, 'status': 'queued'})

# WRONG: Blocking request (30-60s timeout)
@app.route('/generate-3d')
def generate_3d():
    mesh = processor.generate_shape(image)  # Blocks for 30-60s!
    return send_file(mesh)

[5.4] STL FILE HANDLING

ALWAYS validate STL files before sending:

from stl_processor import AdvancedSTLProcessor

processor = AdvancedSTLProcessor()

# Analyze mesh quality
stats = processor.analyze_stl(mesh_path)
if not stats['manifold']:
    # Repair non-manifold geometry
    mesh = processor.repair_stl(mesh)

# Optimize for 3D printing
mesh = processor.optimize_stl_for_printing(
    mesh,
    target_size_mm=100,
    wall_thickness_mm=2.0
)

# Export with validation
mesh.export('output.stl')

[5.5] MONITORING & METRICS

Track ALL API requests and generations:

from production_metrics import track_request, GenerationTracker

@app.route('/api/generate-3d')
@track_request('generate_3d')  # Automatic request tracking
def generate_3d():
    tracker = GenerationTracker('3d_generation')
    try:
        mesh = processor.generate(image)
        tracker.success(duration=elapsed, quality=8)
    except Exception as e:
        tracker.failure(reason=str(e), duration=elapsed)
        raise

Access metrics:

Prometheus: http://localhost:9090
Grafana: http://localhost:3000 (admin/orfeas_admin_2025)
Health check: http://localhost:5000/health

[5.6] AI AGENT PATTERNS

ENTERPRISE AGENT AUTHENTICATION & SECURITY:

from agent_auth import AgentAuthManager
from agent_security import AgentSecurityManager

auth_mgr = AgentAuthManager()
security_mgr = AgentSecurityManager()

@app.route('/api/agent/quality-check', methods=['POST'])
@security_mgr.agent_security_wrapper
def agent_quality_check():
    """Enterprise-grade agent endpoint with comprehensive security"""
    agent_id = request.headers.get('X-Agent-ID')
    api_key = request.headers.get('X-Agent-Key')
    request_signature = request.headers.get('X-Request-Signature')

    # Multi-layer authentication
    if not auth_mgr.authenticate_agent(agent_id, api_key):
        security_mgr.log_security_event('unauthorized_agent_access', agent_id)
        return jsonify({'error': 'Unauthorized agent'}), 401

    # Permission validation
    if not auth_mgr.check_permissions(agent_id, 'quality_assessment'):
        security_mgr.log_security_event('insufficient_permissions', agent_id)
        return jsonify({'error': 'Insufficient permissions'}), 403

    # Request signature validation for critical operations
    if not security_mgr.validate_request_signature(request, request_signature, agent_id):
        security_mgr.log_security_event('invalid_signature', agent_id)
        return jsonify({'error': 'Invalid request signature'}), 401

    # Rate limiting for agents
    if not auth_mgr.check_agent_rate_limits(agent_id, 'quality_assessment'):
        return jsonify({'error': 'Agent rate limit exceeded'}), 429

    return process_quality_check(request.get_json())

ADVANCED AGENT ORCHESTRATION WORKFLOW:

from agent_api import OrfeasAIAgent, AgentOrchestrator
from agent_communication import AgentMessageBus
from agent_monitoring import AgentPerformanceMonitor

# Initialize enterprise agent orchestration
orchestrator = AgentOrchestrator()
message_bus = AgentMessageBus()
performance_monitor = AgentPerformanceMonitor()

class EnterpriseAgentWorkflow:
    """
    Advanced agent workflow for complex multi-step processing
    """

    def __init__(self):
        self.quality_agent = OrfeasAIAgent('quality_agent', [
            'image_analysis', 'complexity_assessment', 'quality_prediction'
        ])
        self.workflow_agent = OrfeasAIAgent('workflow_agent', [
            'pipeline_selection', 'resource_optimization', 'error_recovery'
        ])
        self.optimization_agent = OrfeasAIAgent('optimization_agent', [
            'performance_tuning', 'model_selection', 'parameter_optimization'
        ])

    async def execute_intelligent_3d_generation(self, input_data: Dict) -> Dict:
        """Execute intelligent 3D generation with multi-agent coordination"""

        # Phase 1: Intelligent input analysis
        with performance_monitor.track_agent_performance('quality_agent'):
            quality_analysis = await self.quality_agent.analyze_input({
                'image': input_data['image'],
                'requested_quality': input_data.get('quality', 7),
                'user_preferences': input_data.get('user_preferences', {}),
                'deadline_constraints': input_data.get('deadline')
            })

        # Phase 2: Workflow optimization
        with performance_monitor.track_agent_performance('workflow_agent'):
            workflow_plan = await self.workflow_agent.create_optimal_workflow({
                'quality_analysis': quality_analysis,
                'system_resources': self.get_system_resources(),
                'current_load': self.get_current_system_load()
            })

        # Phase 3: Parameter optimization
        with performance_monitor.track_agent_performance('optimization_agent'):
            optimized_params = await self.optimization_agent.optimize_parameters({
                'workflow_plan': workflow_plan,
                'quality_requirements': quality_analysis['quality_requirements'],
                'performance_constraints': workflow_plan['performance_constraints']
            })

        # Phase 4: Coordinated execution with real-time monitoring
        execution_result = await self.execute_with_monitoring(
            workflow_plan, optimized_params, input_data
        )

        # Phase 5: Quality validation and feedback
        final_result = await self.validate_and_enhance_result(
            execution_result, quality_analysis, optimized_params
        )

        return final_result

    async def execute_with_monitoring(self, workflow_plan: Dict, params: Dict, input_data: Dict) -> Dict:
        """Execute generation with real-time agent monitoring and adaptation"""

        # Initialize execution context
        execution_context = {
            'start_time': time.time(),
            'workflow_plan': workflow_plan,
            'optimized_params': params,
            'agent_states': {}
        }

        try:
            # Select optimal model based on agent analysis
            if workflow_plan['recommended_model'] == 'hunyuan3d_ultra':
                processor = UltraQualityHunyuan3DProcessor(params)
            elif workflow_plan['recommended_model'] == 'hunyuan3d_fast':
                processor = FastHunyuan3DProcessor(params)
            elif workflow_plan['recommended_model'] == 'hunyuan3d_balanced':
                processor = BalancedHunyuan3DProcessor(params)
            else:
                # Agent-recommended fallback
                processor = AdaptiveHunyuan3DProcessor(params)

            # Execute with agent-driven resource management
            with self.agent_resource_manager(workflow_plan['resource_allocation']):
                result = await processor.generate_with_monitoring(
                    input_data,
                    monitoring_callback=self.agent_monitoring_callback
                )

            return result

        except Exception as e:
            # Agent-driven error recovery
            recovery_result = await self.workflow_agent.handle_execution_error(
                error=e,
                execution_context=execution_context,
                recovery_strategies=workflow_plan.get('recovery_strategies', [])
            )

            if recovery_result['recovery_successful']:
                return recovery_result['recovered_result']
            else:
                raise RuntimeError(f"Agent workflow execution failed: {e}")

# Initialize enterprise workflow
enterprise_workflow = EnterpriseAgentWorkflow()

# Usage in API endpoints
@app.route('/api/intelligent-generate-3d', methods=['POST'])
async def intelligent_3d_generation():
    """AI-driven intelligent 3D generation with agent orchestration"""
    try:
        input_data = {
            'image': request.files.get('image'),
            'quality': int(request.form.get('quality', 7)),
            'user_preferences': request.get_json().get('preferences', {}),
            'deadline': request.form.get('deadline'),
            'priority': request.form.get('priority', 'normal')
        }

        # Execute with enterprise agent workflow
        result = await enterprise_workflow.execute_intelligent_3d_generation(input_data)

        return jsonify({
            'generation_result': result['output'],
            'agent_analysis': result['agent_insights'],
            'performance_metrics': result['performance_data'],
            'quality_score': result['quality_assessment']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Intelligent generation failed: {e}")
        return jsonify({'error': str(e)}), 500

AGENT COMMUNICATION & MESSAGE PASSING:

from agent_communication import AgentMessageBus, AgentProtocol

class AgentCommunicationPatterns:
    """
    Advanced agent communication patterns for distributed processing
    """

    def __init__(self):
        self.message_bus = AgentMessageBus()
        self.protocol = AgentProtocol()

    async def setup_agent_communication_network(self):
        """Setup inter-agent communication network"""

        # Register message handlers for each agent type
        await self.message_bus.register_handler(
            'quality_assessment_request',
            self.handle_quality_assessment_message
        )

        await self.message_bus.register_handler(
            'workflow_optimization_request',
            self.handle_workflow_optimization_message
        )

        await self.message_bus.register_handler(
            'parameter_optimization_request',
            self.handle_parameter_optimization_message
        )

        # Setup agent discovery and heartbeat
        await self.setup_agent_discovery()

    async def coordinate_multi_agent_task(self, task: Dict) -> Dict:
        """Coordinate complex task across multiple agents"""

        # Create task coordination context
        coordination_context = {
            'task_id': self.generate_task_id(),
            'task_type': task['type'],
            'involved_agents': [],
            'coordination_strategy': self.select_coordination_strategy(task),
            'communication_graph': self.build_communication_graph(task)
        }

        # Phase 1: Task decomposition and agent assignment
        subtasks = await self.decompose_task_for_agents(task, coordination_context)

        # Phase 2: Parallel agent execution with coordination
        agent_results = {}
        async with self.coordination_context(coordination_context):
            for agent_id, subtask in subtasks.items():
                agent_results[agent_id] = await self.execute_agent_subtask(
                    agent_id, subtask, coordination_context
                )

        # Phase 3: Result synthesis and validation
        synthesized_result = await self.synthesize_agent_results(
            agent_results, coordination_context
        )

        return synthesized_result

# Agent message handling patterns
async def handle_quality_assessment_message(message: Dict) -> Dict:
    """Handle quality assessment requests from other agents"""
    quality_agent = await get_agent('quality_assessment')

    assessment_result = await quality_agent.assess_quality({
        'input_data': message['payload']['input_data'],
        'assessment_criteria': message['payload']['criteria'],
        'requesting_agent': message['sender_id']
    })

    return {
        'message_type': 'quality_assessment_response',
        'recipient_id': message['sender_id'],
        'payload': assessment_result,
        'correlation_id': message['correlation_id']
    }

AGENT LEARNING & ADAPTATION PATTERNS:

from agent_learning import AgentLearningSystem, ExperienceBuffer
from agent_adaptation import AdaptiveAgent, PerformanceFeedback

class IntelligentAdaptiveAgent(OrfeasAIAgent):
    """
    Self-improving agent with learning and adaptation capabilities
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        super().__init__(agent_type, capabilities)
        self.learning_system = AgentLearningSystem()
        self.experience_buffer = ExperienceBuffer()
        self.performance_history = {}

    async def execute_with_learning(self, task: Dict, context: Dict) -> Dict:
        """Execute task with learning and adaptation"""

        # Pre-execution: Retrieve relevant experience
        relevant_experience = self.experience_buffer.get_relevant_experience(
            task_signature=self.generate_task_signature(task),
            context_similarity_threshold=0.8
        )

        # Apply learned optimizations
        if relevant_experience:
            optimized_task = self.apply_learned_optimizations(task, relevant_experience)
        else:
            optimized_task = task

        # Execute with performance monitoring
        start_time = time.time()
        try:
            result = await self.execute_core_logic(optimized_task, context)

            # Calculate performance metrics
            execution_time = time.time() - start_time
            performance_score = self.calculate_performance_score(result, execution_time)

            # Store experience for future learning
            experience = {
                'task': task,
                'context': context,
                'optimizations_applied': optimized_task != task,
                'result': result,
                'performance_score': performance_score,
                'execution_time': execution_time,
                'success': True,
                'timestamp': time.time()
            }
            self.experience_buffer.store_experience(experience)

            # Trigger learning if enough new experiences accumulated
            if self.experience_buffer.should_trigger_learning():
                await self.learning_system.update_agent_knowledge(self, self.experience_buffer)

            return result

        except Exception as e:
            # Learn from failures
            failure_experience = {
                'task': task,
                'context': context,
                'error': str(e),
                'error_type': type(e).__name__,
                'success': False,
                'timestamp': time.time()
            }
            self.experience_buffer.store_experience(failure_experience)

            # Attempt intelligent error recovery
            recovery_result = await self.attempt_learned_recovery(e, task, context)
            if recovery_result['recovered']:
                return recovery_result['result']
            else:
                raise

    def apply_learned_optimizations(self, task: Dict, experiences: List[Dict]) -> Dict:
        """Apply optimizations learned from previous similar tasks"""

        optimized_task = task.copy()

        # Extract optimization patterns from successful experiences
        successful_experiences = [exp for exp in experiences if exp['success']]

        if successful_experiences:
            # Learn optimal parameters
            optimal_params = self.learning_system.extract_optimal_parameters(
                successful_experiences
            )
            optimized_task.update(optimal_params)

            # Learn optimal preprocessing steps
            preprocessing_optimizations = self.learning_system.extract_preprocessing_patterns(
                successful_experiences
            )
            optimized_task['preprocessing'] = preprocessing_optimizations

        return optimized_task

# Agent performance optimization patterns
class PerformanceOptimizedAgent(IntelligentAdaptiveAgent):
    """
    Agent optimized for maximum performance with intelligent caching
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        super().__init__(agent_type, capabilities)
        self.result_cache = self.initialize_intelligent_cache()
        self.performance_optimizer = AgentPerformanceOptimizer()

    async def execute_with_performance_optimization(self, task: Dict, context: Dict) -> Dict:
        """Execute with comprehensive performance optimization"""

        # Check intelligent cache
        cache_key = self.generate_cache_key(task, context)
        cached_result = await self.result_cache.get_cached_result(cache_key)

        if cached_result and self.is_cache_valid(cached_result, context):
            # Update cache hit metrics
            self.performance_optimizer.record_cache_hit(cache_key)
            return cached_result['result']

        # No cache hit - execute with optimization
        optimized_execution = await self.performance_optimizer.optimize_execution(
            agent=self,
            task=task,
            context=context
        )

        result = await self.execute_with_learning(task, context)

        # Cache result for future use
        await self.result_cache.cache_result(
            cache_key,
            result,
            ttl=self.calculate_cache_ttl(task, result)
        )

        return result

ENTERPRISE AGENT DEPLOYMENT PATTERNS:

from kubernetes import client, config
from agent_deployment import AgentDeploymentManager, AgentScaler

class EnterpriseAgentDeployment:
    """
    Enterprise-grade agent deployment and scaling patterns
    """

    def __init__(self):
        self.deployment_manager = AgentDeploymentManager()
        self.scaler = AgentScaler()
        self.k8s_client = self.initialize_kubernetes_client()

    def deploy_agent_cluster(self, agent_config: Dict) -> Dict:
        """Deploy agent cluster with auto-scaling and load balancing"""

        deployment_spec = {
            'agent_type': agent_config['type'],
            'replicas': agent_config.get('initial_replicas', 3),
            'resource_requirements': {
                'cpu': agent_config.get('cpu_request', '500m'),
                'memory': agent_config.get('memory_request', '1Gi'),
                'gpu': agent_config.get('gpu_request', 0)
            },
            'auto_scaling': {
                'min_replicas': agent_config.get('min_replicas', 2),
                'max_replicas': agent_config.get('max_replicas', 10),
                'target_cpu_utilization': agent_config.get('target_cpu', 70),
                'target_memory_utilization': agent_config.get('target_memory', 80)
            },
            'health_checks': {
                'readiness_probe': '/health/ready',
                'liveness_probe': '/health/live',
                'startup_probe': '/health/startup'
            }
        }

        # Deploy to Kubernetes
        deployment_result = self.deployment_manager.deploy_to_kubernetes(
            deployment_spec,
            namespace=agent_config.get('namespace', 'orfeas-agents')
        )

        # Setup service mesh integration
        service_mesh_config = self.setup_agent_service_mesh(deployment_result)

        # Configure monitoring and observability
        monitoring_config = self.setup_agent_monitoring(deployment_result)

        return {
            'deployment_id': deployment_result['deployment_id'],
            'service_endpoints': deployment_result['endpoints'],
            'service_mesh_config': service_mesh_config,
            'monitoring_config': monitoring_config,
            'scaling_config': deployment_spec['auto_scaling']
        }

    def setup_agent_circuit_breaker(self, agent_service: str) -> Dict:
        """Setup circuit breaker pattern for agent resilience"""

        circuit_breaker_config = {
            'failure_threshold': 5,     # Trip after 5 failures
            'recovery_timeout': 30,     # Try recovery after 30 seconds
            'success_threshold': 3,     # Close after 3 successes
            'request_timeout': 10,      # 10 second request timeout
            'monitoring_window': 60     # 60 second monitoring window
        }

        return self.deployment_manager.configure_circuit_breaker(
            agent_service, circuit_breaker_config
        )

# Agent load balancing patterns
@app.route('/api/agent-proxy/<agent_type>', methods=['POST'])
async def intelligent_agent_proxy(agent_type: str):
    """Intelligent agent proxy with load balancing and failover"""

    try:
        # Get available agent instances
        available_agents = await agent_discovery.get_healthy_agents(agent_type)

        if not available_agents:
            return jsonify({'error': f'No healthy {agent_type} agents available'}), 503

        # Select optimal agent based on current load
        selected_agent = agent_load_balancer.select_optimal_agent(
            available_agents,
            request_size=len(request.get_data()),
            request_complexity=analyze_request_complexity(request)
        )

        # Execute request with circuit breaker protection
        with agent_circuit_breaker.protected_execution(selected_agent['id']):
            result = await forward_to_agent(selected_agent, request.get_json())

        return jsonify(result)

    except CircuitBreakerOpenError:
        # Circuit breaker is open - try fallback
        fallback_result = await execute_fallback_agent_logic(agent_type, request.get_json())
        return jsonify(fallback_result)

    except Exception as e:
        logger.error(f"[ORFEAS] Agent proxy error: {e}")
        return jsonify({'error': 'Agent execution failed'}), 500

[5.6.1] TQM AUDIT TECHNIQUES PATTERNS

ENTERPRISE QUALITY AUDIT WORKFLOW:

Always integrate TQM (Total Quality Management) audit techniques into development and production workflows:

# CORRECT: Integrated quality audit patterns
from tqm_audit_system import TQMAuditSystem
from continuous_quality_monitor import ContinuousQualityMonitor

# Initialize TQM system at application startup
tqm_audit = TQMAuditSystem()
quality_monitor = ContinuousQualityMonitor()

# Start continuous quality monitoring
quality_monitor.start_real_time_monitoring()

@app.before_request
def quality_gate_check():
    """Quality gate before processing requests"""
    # Pre-request quality assessment
    quality_score = quality_monitor.assess_current_quality()

    if quality_score < 0.8:  # Below quality threshold
        # Trigger quality improvement actions
        improvement_actions = quality_monitor.automated_quality_improvement({
            'performance_score': quality_score,
            'timestamp': datetime.utcnow()
        })
        logger.warning(f"[TQM] Quality below threshold: {quality_score}, actions: {improvement_actions}")

@app.after_request
def quality_metrics_collection(response):
    """Collect quality metrics after each request"""
    # Post-request quality tracking
    quality_monitor.track_request_quality({
        'endpoint': request.endpoint,
        'response_time': g.get('response_time', 0),
        'status_code': response.status_code,
        'error_occurred': response.status_code >= 400
    })
    return response

# WRONG: No quality monitoring or audit integration
@app.route('/api/generate-3d')
def generate_without_quality_checks():
    return process_generation()  # No quality gates or monitoring!

AUTOMATED QUALITY AUDIT INTEGRATION:

# CORRECT: Comprehensive quality audit workflow
from automated_audit_scheduler import AutomatedAuditScheduler

audit_scheduler = AutomatedAuditScheduler()

def schedule_quality_audits():
    """Schedule automated quality audits based on TQM principles"""

    # Daily performance audits
    audit_scheduler.schedule_audit('performance', 'daily', {
        'focus_areas': ['response_times', 'throughput', 'error_rates'],
        'quality_targets': {
            'api_response_p95': '<500ms',
            'error_rate': '<0.1%',
            'uptime': '>99.9%'
        }
    })

    # Weekly process quality audits
    audit_scheduler.schedule_audit('process_quality', 'weekly', {
        'focus_areas': ['code_review', 'testing_coverage', 'deployment_quality'],
        'compliance_standards': ['iso_9001', 'cmmi_level5']
    })

    # Monthly comprehensive TQM audits
    audit_scheduler.schedule_audit('comprehensive_tqm', 'monthly', {
        'focus_areas': ['all_quality_dimensions'],
        'audit_standards': ['iso_9001_2015', 'six_sigma', 'lean_manufacturing'],
        'generate_report': True,
        'stakeholder_notification': True
    })

# Execute quality audit after major releases
def post_deployment_quality_audit(deployment_info: Dict):
    """Execute comprehensive quality audit after deployment"""

    audit_results = tqm_audit.conduct_comprehensive_quality_audit('post_deployment')

    # Generate executive report
    audit_report = tqm_audit.generate_quality_audit_report(audit_results)

    # Check for critical quality issues
    if audit_report['executive_summary']['overall_quality_score'] < 0.85:
        # Trigger immediate quality improvement actions
        logger.critical(f"[TQM] Post-deployment quality below threshold")
        trigger_quality_improvement_plan(audit_report)

    return audit_report

CONTINUOUS QUALITY MONITORING PATTERNS:

# CORRECT: Real-time quality monitoring with automated responses
class QualityGatewayMiddleware:
    """Middleware for continuous quality monitoring and control"""

    def __init__(self):
        self.quality_monitor = ContinuousQualityMonitor()
        self.quality_thresholds = {
            'performance': 0.8,
            'reliability': 0.95,
            'security': 0.9,
            'user_satisfaction': 0.85
        }

    def before_request(self):
        """Quality gate before request processing"""
        current_quality = self.quality_monitor.assess_real_time_quality()

        # Check quality dimensions
        for dimension, threshold in self.quality_thresholds.items():
            if current_quality.get(dimension, 0) < threshold:
                # Quality below threshold - take corrective action
                self.trigger_quality_correction(dimension, current_quality[dimension])

        # Store quality context for post-processing analysis
        g.quality_context = current_quality

    def after_request(self, response):
        """Quality measurement and learning after request"""
        processing_time = time.time() - g.get('start_time', time.time())

        # Update quality metrics
        quality_update = {
            'request_quality': g.get('quality_context', {}),
            'response_quality': {
                'processing_time': processing_time,
                'success': response.status_code < 400,
                'response_size': len(response.get_data())
            }
        }

        # Feed quality data to continuous improvement system
        self.quality_monitor.update_quality_metrics(quality_update)

        return response

    def trigger_quality_correction(self, dimension: str, current_score: float):
        """Automated quality correction based on dimension"""

        if dimension == 'performance':
            # Performance quality correction
            self.enable_performance_optimizations()

        elif dimension == 'reliability':
            # Reliability quality correction
            self.enhance_error_handling()

        elif dimension == 'security':
            # Security quality correction
            self.strengthen_security_controls()

        logger.info(f"[TQM] Quality correction applied for {dimension}: {current_score}")

QUALITY METRICS INTEGRATION:

# CORRECT: Comprehensive quality metrics collection
from prometheus_client import Counter, Histogram, Gauge

# TQM-specific metrics
quality_audit_counter = Counter(
    'tqm_audits_total',
    'Total number of TQM audits executed',
    ['audit_type', 'audit_scope']
)

quality_score_gauge = Gauge(
    'tqm_quality_score',
    'Current TQM quality score',
    ['quality_dimension']
)

quality_improvement_histogram = Histogram(
    'tqm_improvement_actions_duration',
    'Time taken for quality improvement actions',
    ['improvement_type']
)

def track_quality_metrics(func):
    """Decorator to track quality metrics for any function"""

    def wrapper(*args, **kwargs):
        start_time = time.time()

        try:
            result = func(*args, **kwargs)

            # Track successful quality operation
            quality_score = calculate_operation_quality_score(result)
            quality_score_gauge.labels(quality_dimension=func.__name__).set(quality_score)

            return result

        except Exception as e:
            # Track quality failure
            quality_score_gauge.labels(quality_dimension=func.__name__).set(0)
            raise

        finally:
            # Track operation duration
            duration = time.time() - start_time
            quality_improvement_histogram.labels(
                improvement_type=func.__name__
            ).observe(duration)

    return wrapper

# Usage in core functions
@track_quality_metrics
def generate_3d_with_quality_tracking(image):
    """3D generation with integrated quality tracking"""

    # Pre-generation quality assessment
    input_quality = assess_input_quality(image)

    if input_quality < 0.7:
        # Enhance input quality before processing
        image = enhance_image_quality(image)

    # Generation with quality monitoring
    result = processor.generate_shape(image)

    # Post-generation quality validation
    output_quality = assess_output_quality(result)

    if output_quality < 0.8:
        # Apply quality improvement techniques
        result = apply_quality_enhancements(result)

    return result

[5.6.2] LLM INTEGRATION & COPILOT PATTERNS

ENTERPRISE LLM INTEGRATION WORKFLOW:

# backend/llm_integration.py - Core LLM orchestration
from langchain_enterprise import LangChainManager
from llm_router import LLMRouter
from context_manager import IntelligentContextManager

class EnterpriseLLMManager:
    """
    Enterprise-grade Large Language Model integration and orchestration
    """

    def __init__(self):
        self.llm_router = LLMRouter()
        self.context_manager = IntelligentContextManager()
        self.active_models = {
            'gpt4_turbo': None,
            'claude_3_5_sonnet': None,
            'gemini_ultra': None,
            'llama_3_1_405b': None,
            'mistral_8x22b': None
        }
        self.model_capabilities = self.load_model_capabilities()

    def select_optimal_llm(self, task_context: Dict) -> str:
        """Select best LLM based on task requirements and context"""

        task_type = task_context.get('task_type', 'general')
        complexity = task_context.get('complexity_score', 0.5)
        latency_requirement = task_context.get('max_latency_ms', 5000)

        # Task-specific model selection
        if task_type == 'code_generation':
            if complexity > 0.8:
                return 'gpt4_turbo'  # Best for complex coding tasks
            else:
                return 'claude_3_5_sonnet'  # Fast and accurate for simple code

        elif task_type == 'reasoning_analysis':
            return 'gpt4_turbo'  # Superior reasoning capabilities

        elif task_type == 'content_creation':
            return 'claude_3_5_sonnet'  # Excellent creative writing

        elif task_type == 'multimodal_understanding':
            return 'gemini_ultra'  # Best multimodal capabilities

        elif task_type == 'real_time_chat':
            if latency_requirement < 1000:  # < 1 second
                return 'mistral_8x22b'  # Fastest response time
            else:
                return 'claude_3_5_sonnet'

        else:  # General purpose
            return 'gpt4_turbo'  # Default to most capable model

    async def process_with_llm(self, prompt: str, context: Dict, model_override: str = None) -> Dict:
        """Process request with optimal LLM selection and context awareness"""

        # Build comprehensive context
        processing_context = self.context_manager.build_llm_context(context)

        # Select optimal model
        selected_model = model_override or self.select_optimal_llm(processing_context)

        # Apply context-aware prompt enhancement
        enhanced_prompt = self.enhance_prompt_with_context(prompt, processing_context)

        # Execute with selected LLM
        try:
            result = await self.execute_llm_request(selected_model, enhanced_prompt, processing_context)

            # Apply post-processing and validation
            validated_result = self.validate_and_enhance_response(result, processing_context)

            # Update model performance metrics
            self.update_model_performance(selected_model, result, processing_context)

            return validated_result

        except Exception as e:
            # Intelligent fallback to alternative models
            return await self.fallback_llm_processing(prompt, processing_context, failed_model=selected_model)

    def enhance_prompt_with_context(self, prompt: str, context: Dict) -> str:
        """Enhance prompt with intelligent context injection"""

        context_template = """
        SYSTEM CONTEXT:
        - Platform: ORFEAS AI 2D→3D Studio Enterprise
        - User Expertise: {user_expertise}
        - Task Complexity: {complexity_score}
        - Previous Context: {previous_interactions}
        - Quality Requirements: {quality_requirements}

        USER REQUEST:
        {original_prompt}

        ENHANCED INSTRUCTIONS:
        - Provide enterprise-grade responses with technical accuracy
        - Include code examples when relevant (Python/JavaScript preferred)
        - Consider 3D modeling, AI, and multimedia context
        - Ensure responses align with ORFEAS platform capabilities
        """

        return context_template.format(
            user_expertise=context.get('user_expertise', 'intermediate'),
            complexity_score=context.get('complexity_score', 0.5),
            previous_interactions=context.get('previous_context', 'None'),
            quality_requirements=context.get('quality_requirements', 'high'),
            original_prompt=prompt
        )

GITHUB COPILOT ENTERPRISE INTEGRATION:

# backend/copilot_enterprise.py - GitHub Copilot Enterprise integration
class GitHubCopilotEnterprise:
    """
    GitHub Copilot Enterprise integration for advanced code generation
    """

    def __init__(self):
        self.copilot_api = self.initialize_copilot_api()
        self.code_context_manager = CodeContextManager()
        self.quality_validator = CodeQualityValidator()

    async def generate_code_with_copilot(self, requirements: str, context: Dict) -> Dict:
        """Generate code using GitHub Copilot Enterprise with context awareness"""

        # Build code generation context
        code_context = {
            'project_type': 'orfeas_ai_platform',
            'language': context.get('language', 'python'),
            'framework': 'flask_pytorch_enterprise',
            'coding_standards': 'orfeas_enterprise_standards',
            'security_level': 'enterprise_grade',
            'existing_codebase': self.code_context_manager.get_relevant_code(requirements)
        }

        # Generate code with Copilot
        copilot_response = await self.copilot_api.generate_code(
            prompt=self.build_copilot_prompt(requirements, code_context),
            max_tokens=2000,
            temperature=0.2,  # Low temperature for more deterministic code
            include_context=True
        )

        # Validate and enhance generated code
        validated_code = self.quality_validator.validate_and_enhance(
            code=copilot_response['generated_code'],
            requirements=requirements,
            context=code_context
        )

        return {
            'generated_code': validated_code['code'],
            'quality_score': validated_code['quality_score'],
            'suggestions': validated_code['improvement_suggestions'],
            'tests': await self.generate_tests_for_code(validated_code['code']),
            'documentation': self.generate_code_documentation(validated_code['code'])
        }

    def build_copilot_prompt(self, requirements: str, context: Dict) -> str:
        """Build optimized prompt for GitHub Copilot"""

        prompt_template = """
        # ORFEAS AI Enterprise Platform - Code Generation Request

        ## Project Context:
        - Platform: ORFEAS AI 2D→3D Studio Enterprise
        - Framework: Flask + PyTorch + Hunyuan3D-2.1
        - Language: {language}
        - Security: Enterprise-grade with input validation
        - Performance: GPU-optimized for RTX 3090
        - Quality: Production-ready with comprehensive error handling

        ## Requirements:
        {requirements}

        ## Technical Constraints:
        - Follow ORFEAS coding patterns and conventions
        - Include comprehensive error handling and logging
        - Implement proper input validation and security checks
        - Use type hints and docstrings
        - Optimize for GPU memory management
        - Include monitoring and metrics collection

        ## Expected Output:
        - Clean, production-ready code
        - Comprehensive error handling
        - Performance optimizations
        - Security best practices
        - Proper documentation
        """

        return prompt_template.format(
            language=context.get('language', 'python'),
            requirements=requirements
        )

MULTI-LLM ORCHESTRATION PATTERNS:

# Multi-LLM coordination for complex tasks
class MultiLLMOrchestrator:
    """
    Orchestrate multiple LLMs for complex multi-step tasks
    """

    def __init__(self):
        self.llm_manager = EnterpriseLLMManager()
        self.task_decomposer = TaskDecomposer()
        self.result_synthesizer = ResultSynthesizer()

    async def execute_complex_task(self, task_description: str, context: Dict) -> Dict:
        """Execute complex task using multiple specialized LLMs"""

        # 1. Decompose complex task into subtasks
        subtasks = self.task_decomposer.decompose_task(task_description, context)

        # 2. Assign optimal LLM for each subtask
        llm_assignments = {}
        for subtask in subtasks:
            optimal_llm = self.llm_manager.select_optimal_llm(subtask['context'])
            llm_assignments[subtask['id']] = optimal_llm

        # 3. Execute subtasks in parallel or sequence
        results = {}
        for subtask in subtasks:
            assigned_llm = llm_assignments[subtask['id']]

            # Execute with assigned LLM
            result = await self.llm_manager.process_with_llm(
                prompt=subtask['prompt'],
                context=subtask['context'],
                model_override=assigned_llm
            )

            results[subtask['id']] = {
                'result': result,
                'llm_used': assigned_llm,
                'execution_time': result.get('execution_time', 0)
            }

        # 4. Synthesize results from multiple LLMs
        final_result = self.result_synthesizer.synthesize_results(
            results, task_description, context
        )

        return {
            'final_result': final_result,
            'execution_breakdown': results,
            'llm_assignments': llm_assignments,
            'total_execution_time': sum(r['execution_time'] for r in results.values())
        }

RAG (RETRIEVAL-AUGMENTED GENERATION) PATTERNS:

# backend/rag_integration.py - Advanced RAG implementation
class EnterpriseRAGSystem:
    """
    Enterprise-grade Retrieval-Augmented Generation system
    """

    def __init__(self):
        self.vector_store = self.initialize_vector_store()
        self.knowledge_graph = self.initialize_knowledge_graph()
        self.retrieval_engine = AdvancedRetrievalEngine()
        self.generation_engine = LLMGenerationEngine()

    def initialize_vector_store(self):
        """Initialize enterprise vector database"""
        from pinecone_enterprise import PineconeEnterprise

        return PineconeEnterprise(
            index_name="orfeas_knowledge_base",
            dimension=1536,  # OpenAI embedding dimension
            metric="cosine",
            shards=4,  # Enterprise scaling
            replicas=2  # High availability
        )

    async def rag_enhanced_generation(self, query: str, context: Dict) -> Dict:
        """Generate response using RAG with multiple knowledge sources"""

        # 1. Multi-source knowledge retrieval
        retrieved_knowledge = await self.retrieve_relevant_knowledge(query, context)

        # 2. Knowledge synthesis and ranking
        synthesized_knowledge = self.synthesize_knowledge_sources(retrieved_knowledge)

        # 3. Context-aware prompt construction
        enhanced_prompt = self.build_rag_prompt(query, synthesized_knowledge, context)

        # 4. LLM generation with retrieved context
        generated_response = await self.generation_engine.generate_with_context(
            prompt=enhanced_prompt,
            context=context,
            knowledge_context=synthesized_knowledge
        )

        # 5. Response validation and citation
        validated_response = self.validate_and_cite_response(
            response=generated_response,
            sources=retrieved_knowledge,
            original_query=query
        )

        return {
            'response': validated_response['text'],
            'confidence_score': validated_response['confidence'],
            'citations': validated_response['citations'],
            'knowledge_sources': retrieved_knowledge,
            'retrieval_quality': self.assess_retrieval_quality(retrieved_knowledge, query)
        }

    async def retrieve_relevant_knowledge(self, query: str, context: Dict) -> List[Dict]:
        """Retrieve relevant knowledge from multiple sources"""

        retrieval_sources = [
            'orfeas_documentation',
            'technical_specifications',
            'code_examples',
            'best_practices',
            'troubleshooting_guides',
            'api_references'
        ]

        all_retrieved = []

        for source in retrieval_sources:
            # Vector similarity search
            vector_results = await self.vector_store.similarity_search(
                query=query,
                namespace=source,
                top_k=5,
                filter=self.build_retrieval_filter(context, source)
            )

            # Knowledge graph traversal
            graph_results = await self.knowledge_graph.find_related_concepts(
                query=query,
                source=source,
                max_depth=2,
                relationship_types=['related_to', 'implements', 'extends']
            )

            # Combine and score results
            combined_results = self.combine_retrieval_results(vector_results, graph_results)
            all_retrieved.extend(combined_results)

        # Rank and deduplicate
        ranked_results = self.rank_retrieval_results(all_retrieved, query, context)

        return ranked_results[:10]  # Return top 10 most relevant

LLM API ENDPOINTS:

# LLM-powered API endpoints for the ORFEAS platform
@app.route('/api/llm/generate-code', methods=['POST'])
@track_request('llm_code_generation')
def api_llm_generate_code():
    """Generate code using enterprise LLM capabilities"""
    try:
        data = request.get_json()
        requirements = data.get('requirements', '')
        language = data.get('language', 'python')
        context = data.get('context', {})

        if not requirements:
            return jsonify({'error': 'No requirements provided'}), 400

        # Generate code with GitHub Copilot Enterprise
        copilot = GitHubCopilotEnterprise()
        result = await copilot.generate_code_with_copilot(requirements, {
            'language': language,
            'user_context': context
        })

        return jsonify({
            'generated_code': result['generated_code'],
            'quality_score': result['quality_score'],
            'suggestions': result['suggestions'],
            'tests': result['tests'],
            'documentation': result['documentation']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM code generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/llm/intelligent-analysis', methods=['POST'])
@track_request('llm_intelligent_analysis')
def api_llm_intelligent_analysis():
    """Perform intelligent analysis using multiple LLMs"""
    try:
        data = request.get_json()
        analysis_request = data.get('analysis_request', '')
        analysis_type = data.get('analysis_type', 'general')
        context = data.get('context', {})

        # Use multi-LLM orchestration for complex analysis
        orchestrator = MultiLLMOrchestrator()
        result = await orchestrator.execute_complex_task(
            task_description=f"Perform {analysis_type} analysis: {analysis_request}",
            context=context
        )

        return jsonify({
            'analysis_result': result['final_result'],
            'llm_breakdown': result['execution_breakdown'],
            'execution_time': result['total_execution_time'],
            'confidence_score': result.get('confidence_score', 0.85)
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM intelligent analysis failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/llm/rag-query', methods=['POST'])
@track_request('llm_rag_query')
def api_llm_rag_query():
    """Answer questions using RAG (Retrieval-Augmented Generation)"""
    try:
        data = request.get_json()
        query = data.get('query', '')
        context = data.get('context', {})

        if not query:
            return jsonify({'error': 'No query provided'}), 400

        # Use RAG system for knowledge-enhanced responses
        rag_system = EnterpriseRAGSystem()
        result = await rag_system.rag_enhanced_generation(query, context)

        return jsonify({
            'response': result['response'],
            'confidence_score': result['confidence_score'],
            'citations': result['citations'],
            'knowledge_sources': result['knowledge_sources'],
            'retrieval_quality': result['retrieval_quality']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM RAG query failed: {e}")
        return jsonify({'error': str(e)}), 500

[5.7] ALTERNATIVE AI ENGINE INTEGRATION

HuggingFace fallback patterns:

from huggingface_compat import HuggingFaceModelManager

hf_mgr = HuggingFaceModelManager()

try:
    # Primary: Hunyuan3D-2.1
    mesh = hunyuan_processor.generate_3d(image)
except Exception as e:
    logger.warning(f"[ORFEAS] Primary model failed: {e}")
    try:
        # Fallback: InstantMesh via HuggingFace
        mesh = hf_mgr.fallback_generation(prompt, 'instant_mesh')
    except Exception as e2:
        logger.error(f"[ORFEAS] All models failed: {e2}")
        return jsonify({'error': 'Generation failed'}), 500

ComfyUI workflow integration:

# backend/comfyui_integration.py
class ComfyUIWorkflowManager:
    def __init__(self):
        self.api_url = "http://localhost:8188"
        self.workflows = {
            'text_to_3d': 'workflows/text_to_3d.json',
            'image_to_3d': 'workflows/image_to_3d.json',
            'style_transfer_3d': 'workflows/style_transfer_3d.json'
        }

    async def execute_workflow(self, workflow_type: str, inputs: Dict):
        """Execute ComfyUI workflow as alternative generation method"""
        workflow_path = self.workflows.get(workflow_type)
        if not workflow_path:
            raise ValueError(f"Unknown workflow: {workflow_type}")

        # Load and customize workflow
        with open(workflow_path) as f:
            workflow = json.load(f)

        # Update inputs
        self.update_workflow_inputs(workflow, inputs)

        # Submit to ComfyUI API
        response = await self.submit_workflow(workflow)
        return response

[5.7.1] AI VIDEO COMPOSITION PATTERNS

VIDEO GENERATION WORKFLOW:

# backend/video_processor.py - Core video composition
from sora_video_integration import SoraVideoProcessor
from cinematic_composer import CinematicComposer

video_processor = SoraVideoProcessor()
cinematic_composer = CinematicComposer()

def generate_cinematic_video_from_3d(model_path: str, style: str = "cinematic") -> Dict:
    """Generate cinematic video from 3D model"""

    # 1. Analyze 3D model for optimal camera movements
    model_analysis = video_processor.analyze_3d_model(model_path)

    # 2. Generate camera motion sequence
    camera_sequence = cinematic_composer.generate_camera_movements(
        model_analysis, style=style
    )

    # 3. Apply lighting and environment
    scene_setup = cinematic_composer.setup_cinematic_scene(
        model_path, camera_sequence
    )

    # 4. Render video with Sora-inspired generation
    video_output = video_processor.render_cinematic_video(
        scene_setup, fps=24, resolution=(1920, 1080)
    )

    return video_output

# Text-to-Video generation workflow
def generate_video_from_text(prompt: str, duration: int = 10) -> Dict:
    """Generate cinematic video from text prompt"""

    # 1. Analyze prompt for visual elements
    prompt_analysis = video_processor.analyze_text_prompt(prompt)

    # 2. Generate initial frames
    key_frames = video_processor.generate_key_frames(
        prompt, frame_count=duration * 4  # 4 key frames per second
    )

    # 3. Apply temporal consistency
    consistent_frames = video_processor.apply_temporal_consistency(key_frames)

    # 4. Generate intermediate frames with motion
    full_video = video_processor.interpolate_motion(
        consistent_frames, target_fps=24
    )

    return full_video

VIDEO API ENDPOINTS:

# Video generation API endpoints
@app.route('/api/generate-video-from-3d', methods=['POST'])
def api_generate_video_from_3d():
    """Generate video from 3D model"""
    try:
        model_file = request.files.get('model')
        style = request.form.get('style', 'cinematic')
        duration = int(request.form.get('duration', 10))

        # Validate 3D model file
        if not model_file:
            return jsonify({'error': 'No 3D model provided'}), 400

        # Save uploaded model
        model_path = save_uploaded_file(model_file, 'models')

        # Generate video
        video_result = generate_cinematic_video_from_3d(model_path, style)

        return jsonify({
            'video_path': video_result['output_path'],
            'duration': video_result['duration'],
            'fps': video_result['fps'],
            'resolution': video_result['resolution']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Video generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/generate-video-from-text', methods=['POST'])
def api_generate_video_from_text():
    """Generate video from text prompt"""
    try:
        data = request.get_json()
        prompt = data.get('prompt', '')
        duration = data.get('duration', 10)
        style = data.get('style', 'cinematic')

        if not prompt:
            return jsonify({'error': 'No prompt provided'}), 400

        # Generate video
        video_result = generate_video_from_text(prompt, duration)

        return jsonify({
            'video_path': video_result['output_path'],
            'prompt': prompt,
            'duration': video_result['duration'],
            'style': style
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-video generation failed: {e}")
        return jsonify({'error': str(e)}), 500

CINEMATIC FEATURES IMPLEMENTATION:

# backend/cinematic_composer.py - Advanced cinematic features
class CinematicComposer:
    """Advanced cinematic video composition engine"""

    def __init__(self):
        self.camera_presets = {
            'turntable': self.generate_turntable_motion,
            'fly_around': self.generate_orbital_motion,
            'dolly_zoom': self.generate_dolly_zoom,
            'hero_shot': self.generate_hero_shot
        }

    def generate_turntable_motion(self, model_analysis: Dict) -> List[Dict]:
        """Generate smooth turntable camera motion"""
        frames = []
        center = model_analysis['bounding_box']['center']
        radius = model_analysis['bounding_box']['radius'] * 2.5

        for frame in range(240):  # 10 seconds at 24fps
            angle = (frame / 240) * 360  # Full rotation
            camera_pos = {
                'x': center['x'] + radius * math.cos(math.radians(angle)),
                'y': center['y'] + radius * 0.3,  # Slight elevation
                'z': center['z'] + radius * math.sin(math.radians(angle)),
                'target': center,
                'fov': 45
            }
            frames.append(camera_pos)

        return frames

    def apply_cinematic_lighting(self, scene: Dict, style: str) -> Dict:
        """Apply cinematic lighting setup"""
        # Define color values using RGB to avoid parser issues
        WHITE = 'rgb(255, 255, 255)'
        LIGHT_BLUE = 'rgb(240, 240, 255)'
        WARM_WHITE = 'rgb(255, 248, 225)'
        WARM_YELLOW = 'rgb(255, 238, 170)'
        DARK_GRAY = 'rgb(74, 85, 104)'
        WARM_ORANGE = 'rgb(255, 107, 53)'

        lighting_presets = {
            'studio': {
                'key_light': {'intensity': 1.0, 'color': WHITE, 'position': (2, 3, 2)},
                'fill_light': {'intensity': 0.4, 'color': LIGHT_BLUE, 'position': (-1, 2, 1)},
                'rim_light': {'intensity': 0.8, 'color': WARM_WHITE, 'position': (0, 1, -2)}
            },
            'dramatic': {
                'key_light': {'intensity': 1.2, 'color': WARM_YELLOW, 'position': (3, 4, 1)},
                'fill_light': {'intensity': 0.2, 'color': DARK_GRAY, 'position': (-2, 1, 0)},
                'rim_light': {'intensity': 1.0, 'color': WARM_ORANGE, 'position': (-1, 2, -3)}
            }
        }

        scene['lighting'] = lighting_presets.get(style, lighting_presets['studio'])
        return scene

[5.7.2] AI MULTIMEDIA GENERATION PATTERNS

TEXT-TO-IMAGE GENERATION WORKFLOW:

# backend/text_to_image_processor.py - Core text-to-image generation
from text_to_image_ai import TextToImageProcessor
from image_enhancement import ImageEnhancer

class TextToImageProcessor:
    """Advanced text-to-image generation with multiple AI engines"""

    def __init__(self):
        self.models = {
            'dalle3': self.setup_dalle3(),
            'stable_diffusion_xl': self.setup_sdxl(),
            'midjourney': self.setup_midjourney()
        }
        self.enhancer = ImageEnhancer()

    async def generate_image_from_text(self, prompt: str, style: str = "photorealistic") -> Dict:
        """Generate high-quality image from text prompt"""

        # 1. Analyze and enhance prompt
        enhanced_prompt = self.enhance_prompt(prompt, style)

        # 2. Select optimal model based on prompt analysis
        optimal_model = self.select_optimal_model(enhanced_prompt)

        # 3. Generate image with selected model
        generated_image = await self.models[optimal_model].generate(enhanced_prompt)

        # 4. Apply post-processing enhancements
        enhanced_image = self.enhancer.enhance_image(generated_image)

        return {
            'image': enhanced_image,
            'model_used': optimal_model,
            'prompt': enhanced_prompt,
            'style': style
        }

TEXT-TO-SPEECH GENERATION WORKFLOW:

# backend/text_to_speech_processor.py - Core TTS generation
from speech_synthesis import SpeechSynthesizer
from voice_cloning import VoiceCloningEngine

class TextToSpeechProcessor:
    """Enterprise-grade text-to-speech generation"""

    def __init__(self):
        self.engines = {
            'elevenlabs': self.setup_elevenlabs(),
            'azure_tts': self.setup_azure_tts(),
            'openai_tts': self.setup_openai_tts()
        }
        self.voice_cloner = VoiceCloningEngine()

    async def synthesize_speech(self, text: str, voice_id: str = "default") -> Dict:
        """Generate natural speech from text"""

        # 1. Preprocess text for optimal synthesis
        processed_text = self.preprocess_text(text)

        # 2. Select optimal TTS engine
        optimal_engine = self.select_tts_engine(processed_text, voice_id)

        # 3. Generate speech audio
        audio_data = await self.engines[optimal_engine].synthesize(
            text=processed_text,
            voice_id=voice_id,
            quality='high'
        )

        # 4. Apply audio enhancements
        enhanced_audio = self.enhance_audio_quality(audio_data)

        return {
            'audio_data': enhanced_audio,
            'duration': self.get_audio_duration(enhanced_audio),
            'engine_used': optimal_engine,
            'voice_id': voice_id
        }

SPEECH-TO-TEXT PROCESSING WORKFLOW:

# backend/speech_to_text_processor.py - Core STT processing
from speech_recognition import SpeechRecognizer
from audio_preprocessing import AudioPreprocessor

class SpeechToTextProcessor:
    """Advanced speech-to-text with real-time capabilities"""

    def __init__(self):
        self.recognizers = {
            'whisper_large': self.setup_whisper(),
            'azure_speech': self.setup_azure_stt(),
            'google_speech': self.setup_google_stt()
        }
        self.preprocessor = AudioPreprocessor()

    async def transcribe_audio(self, audio_data: bytes, language: str = "en-US") -> Dict:
        """Convert speech audio to text"""

        # 1. Preprocess audio for optimal recognition
        processed_audio = self.preprocessor.enhance_audio(audio_data)

        # 2. Select optimal recognition engine
        optimal_recognizer = self.select_stt_engine(processed_audio, language)

        # 3. Perform speech recognition
        transcription = await self.recognizers[optimal_recognizer].transcribe(
            audio=processed_audio,
            language=language,
            enable_punctuation=True,
            enable_timestamps=True
        )

        # 4. Post-process transcription
        enhanced_transcription = self.enhance_transcription(transcription)

        return {
            'text': enhanced_transcription['text'],
            'confidence': enhanced_transcription['confidence'],
            'timestamps': enhanced_transcription['timestamps'],
            'language': language,
            'engine_used': optimal_recognizer
        }

    async def real_time_transcription(self, audio_stream) -> AsyncGenerator[str, None]:
        """Real-time speech-to-text streaming"""
        async for audio_chunk in audio_stream:
            partial_result = await self.transcribe_audio_chunk(audio_chunk)
            yield partial_result['text']

MULTIMEDIA API ENDPOINTS:

# Multimedia AI API endpoints
@app.route('/api/generate-image-from-text', methods=['POST'])
def api_text_to_image():
    """Generate image from text prompt"""
    try:
        data = request.get_json()
        prompt = data.get('prompt', '')
        style = data.get('style', 'photorealistic')

        if not prompt:
            return jsonify({'error': 'No prompt provided'}), 400

        # Generate image
        result = text_to_image_processor.generate_image_from_text(prompt, style)

        return jsonify({
            'image_url': result['image_url'],
            'prompt': result['prompt'],
            'style': result['style'],
            'model_used': result['model_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-image generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/synthesize-speech', methods=['POST'])
def api_text_to_speech():
    """Convert text to speech"""
    try:
        data = request.get_json()
        text = data.get('text', '')
        voice_id = data.get('voice_id', 'default')

        if not text:
            return jsonify({'error': 'No text provided'}), 400

        # Generate speech
        result = tts_processor.synthesize_speech(text, voice_id)

        return jsonify({
            'audio_url': result['audio_url'],
            'duration': result['duration'],
            'voice_id': result['voice_id'],
            'engine_used': result['engine_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-speech generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/transcribe-audio', methods=['POST'])
def api_speech_to_text():
    """Convert speech to text"""
    try:
        audio_file = request.files.get('audio')
        language = request.form.get('language', 'en-US')

        if not audio_file:
            return jsonify({'error': 'No audio file provided'}), 400

        # Transcribe audio
        result = stt_processor.transcribe_audio(audio_file.read(), language)

        return jsonify({
            'text': result['text'],
            'confidence': result['confidence'],
            'timestamps': result['timestamps'],
            'language': result['language'],
            'engine_used': result['engine_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Speech-to-text processing failed: {e}")
        return jsonify({'error': str(e)}), 500

[5.7.3] AI CODE DEVELOPMENT PATTERNS

INTELLIGENT CODE GENERATION WORKFLOW:

# backend/code_writer.py - Core code generation
from code_writer import IntelligentCodeWriter
from code_debugger import AutomatedCodeDebugger

code_writer = IntelligentCodeWriter()
code_debugger = AutomatedCodeDebugger()

def generate_code_from_requirements(requirements: str, language: str = "python") -> Dict:
    """Generate code from natural language requirements"""

    # 1. Analyze requirements and extract context
    analysis = code_writer.analyze_requirements(requirements, {
        'target_language': language,
        'code_style': 'clean',
        'include_tests': True,
        'include_docs': True
    })

    # 2. Generate code structure
    code_structure = code_writer.generate_code_structure(analysis, language)

    # 3. Generate implementation
    implementation = code_writer.generate_implementation(code_structure, analysis)

    # 4. Validate and optimize
    optimized_code = code_writer.optimize_code(implementation, language)

    return {
        'code': optimized_code,
        'structure': code_structure,
        'documentation': code_writer.generate_documentation(optimized_code),
        'tests': code_writer.generate_tests(optimized_code),
        'quality_score': code_writer.quality_analyzer.analyze(optimized_code)
    }

# Code debugging workflow
def debug_and_fix_code(code: str, error_message: str, language: str = "python") -> Dict:
    """Debug code and provide automated fixes"""

    # 1. Analyze error and code context
    debug_result = code_debugger.debug_code(code, error_message, language)

    # 2. Apply automated fixes if confidence is high
    if debug_result['confidence'] > 0.8:
        fixed_code = code_debugger.apply_fix(code, debug_result['fix_suggestion'])

        # 3. Validate the fix
        validation_result = code_debugger.validate_fix(fixed_code, code)

        return {
            'original_code': code,
            'fixed_code': fixed_code,
            'error_analysis': debug_result,
            'validation': validation_result,
            'auto_applied': True
        }
    else:
        return {
            'original_code': code,
            'error_analysis': debug_result,
            'suggested_fixes': debug_result['fix_suggestion'],
            'auto_applied': False
        }

CODE DEVELOPMENT API ENDPOINTS:

# Code generation and debugging API endpoints
@app.route('/api/generate-code', methods=['POST'])
def api_generate_code():
    """Generate code from natural language description"""
    try:
        data = request.get_json()
        requirements = data.get('requirements', '')
        language = data.get('language', 'python')
        context = data.get('context', {})

        if not requirements:
            return jsonify({'error': 'No requirements provided'}), 400

        # Generate code
        result = generate_code_from_requirements(requirements, language)

        return jsonify({
            'generated_code': result['code'],
            'documentation': result['documentation'],
            'tests': result['tests'],
            'quality_score': result['quality_score'],
            'language': language
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/debug-code', methods=['POST'])
def api_debug_code():
    """Debug code and provide automated fixes"""
    try:
        data = request.get_json()
        code = data.get('code', '')
        error_message = data.get('error_message', '')
        language = data.get('language', 'python')

        if not code or not error_message:
            return jsonify({'error': 'Code and error message required'}), 400

        # Debug and fix code
        debug_result = debug_and_fix_code(code, error_message, language)

        return jsonify({
            'debug_analysis': debug_result['error_analysis'],
            'fixed_code': debug_result.get('fixed_code'),
            'auto_applied': debug_result['auto_applied'],
            'suggested_fixes': debug_result.get('suggested_fixes'),
            'validation': debug_result.get('validation')
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code debugging failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/analyze-code-quality', methods=['POST'])
def api_analyze_code_quality():
    """Analyze code quality and provide suggestions"""
    try:
        data = request.get_json()
        code = data.get('code', '')
        language = data.get('language', 'python')

        if not code:
            return jsonify({'error': 'No code provided'}), 400

        # Analyze code quality
        quality_analysis = code_writer.quality_analyzer.analyze_comprehensive(code, language)

        return jsonify({
            'quality_score': quality_analysis['overall_score'],
            'metrics': quality_analysis['metrics'],
            'suggestions': quality_analysis['suggestions'],
            'refactoring_opportunities': quality_analysis['refactoring'],
            'language': language
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code quality analysis failed: {e}")
        return jsonify({'error': str(e)}), 500

CODE DEVELOPMENT FEATURES IMPLEMENTATION:

# backend/code_analyzer.py - Advanced code analysis
class CodeQualityAnalyzer:
    """Advanced code quality analysis and suggestions"""

    def __init__(self):
        self.analysis_engines = {
            'syntax': self.analyze_syntax,
            'style': self.analyze_style,
            'complexity': self.analyze_complexity,
            'performance': self.analyze_performance,
            'security': self.analyze_security
        }

    def analyze_comprehensive(self, code: str, language: str) -> Dict:
        """Perform comprehensive code analysis"""
        results = {}

        for analysis_type, analyzer in self.analysis_engines.items():
            try:
                results[analysis_type] = analyzer(code, language)
            except Exception as e:
                logger.warning(f"[ORFEAS] {analysis_type} analysis failed: {e}")
                results[analysis_type] = {'score': 0, 'issues': [str(e)]}

        # Calculate overall score
        overall_score = self.calculate_overall_score(results)

        return {
            'overall_score': overall_score,
            'metrics': results,
            'suggestions': self.generate_suggestions(results),
            'refactoring': self.identify_refactoring_opportunities(code, results)
        }

    def generate_suggestions(self, analysis_results: Dict) -> List[str]:
        """Generate actionable suggestions for code improvement"""
        suggestions = []

        for analysis_type, result in analysis_results.items():
            if result['score'] < 0.7:  # Below acceptable threshold
                suggestions.extend(result.get('suggestions', []))

        return suggestions

    def identify_refactoring_opportunities(self, code: str, analysis: Dict) -> List[Dict]:
        """Identify specific refactoring opportunities"""
        opportunities = []

        # Complexity-based refactoring
        if analysis.get('complexity', {}).get('score', 1) < 0.6:
            opportunities.append({
                'type': 'extract_method',
                'description': 'Consider extracting complex logic into separate methods',
                'priority': 'high'
            })

        # Performance-based refactoring
        if analysis.get('performance', {}).get('score', 1) < 0.7:
            opportunities.append({
                'type': 'optimize_loops',
                'description': 'Optimize loops and data structures for better performance',
                'priority': 'medium'
            })

        return opportunities

[5.8] INTELLIGENT CONTEXT PATTERNS

CONTEXT-AWARE PROCESSING WORKFLOW:

# Always build context before processing
from context_manager import IntelligentContextManager

context_mgr = IntelligentContextManager()

@app.route('/api/generate-3d', methods=['POST'])
def generate_3d_with_context():
    """Context-aware 3D generation with intelligent decision making"""

    # 1. Build comprehensive processing context
    context = context_mgr.build_processing_context({
        'image': request.files.get('image'),
        'user_id': request.headers.get('X-User-ID'),
        'quality_preference': request.form.get('quality', 7),
        'deadline': request.form.get('deadline'),
        'priority': request.form.get('priority', 'normal')
    })

    # 2. Get contextual recommendations
    recommendations = context_mgr.get_contextual_recommendations(context)

    # 3. Select optimal model based on context
    from contextual_model_selector import ContextualModelSelector
    model_selector = ContextualModelSelector()
    model_choice = model_selector.select_optimal_model(context)

    # 4. Execute with context-aware error handling
    try:
        result = execute_generation_with_context(
            model_choice['selected_model'],
            context,
            recommendations
        )

        # 5. Learn from successful execution
        model_selector.learn_from_result(context, model_choice['selected_model'], result)

        return jsonify(result)

    except Exception as e:
        # 6. Context-aware error recovery
        from contextual_error_handler import ContextualErrorHandler
        error_handler = ContextualErrorHandler(context_mgr)
        recovery_result = await error_handler.handle_error_with_context(e, context)

        return jsonify(recovery_result)

AGENT COORDINATION WITH CONTEXT:

# Coordinate multiple agents with shared context
from contextual_agent_coordinator import ContextualAgentCoordinator

async def intelligent_3d_generation_workflow(image_data: Dict):
    """Multi-agent workflow with intelligent context sharing"""

    coordinator = ContextualAgentCoordinator(context_mgr)

    # Define agents for the workflow
    workflow_agents = [
        'quality_assessment_agent',
        'model_selection_agent',
        'resource_optimization_agent',
        'generation_execution_agent'
    ]

    # Execute coordinated workflow with context
    task = {
        'type': '3d_generation',
        'input_data': image_data,
        'quality_requirements': {'min_quality': 7, 'max_time': 60}
    }

    results = await coordinator.coordinate_with_context(task, workflow_agents)

    return results

CONTEXT-AWARE MODEL SELECTION:

# Intelligent model selection based on context analysis
def select_generation_model_with_context(request_data: Dict) -> str:
    """Select optimal generation model based on comprehensive context"""

    # Build context
    context = context_mgr.build_processing_context(request_data)

    # Analyze input characteristics
    input_analysis = context['input_analysis']

    # Context-based decision tree
    if input_analysis['complexity_score'] > 0.8:
        if context['resource_context']['available_vram'] > 16000:  # > 16GB
            return 'hunyuan3d_ultra_quality'
        else:
            return 'hunyuan3d_high_quality_optimized'

    elif input_analysis['input_type'] == 'simple_object':
        if context['user_context']['speed_preference'] == 'fast':
            return 'hunyuan3d_fast'
        else:
            return 'hunyuan3d_balanced'

    elif context['system_context']['current_load'] > 0.8:
        # High system load - use lighter model
        return 'instant_mesh_lightweight'

    else:
        # Default to balanced model with context optimization
        return 'hunyuan3d_context_optimized'

CONTEXT PERSISTENCE AND LEARNING:

# Persist context for continuous learning
from context_persistence import ContextPersistenceManager

persistence_mgr = ContextPersistenceManager()

def process_with_learning(session_id: str, request_data: Dict):
    """Process request with context learning and persistence"""

    # Build context
    context = context_mgr.build_processing_context(request_data)

    # Get historical insights
    insights = persistence_mgr.get_contextual_insights(context)

    # Apply learned optimizations
    if insights['success_probability'] > 0.9:
        # High confidence - use recommended parameters
        processing_params = insights['optimal_parameters']
    else:
        # Lower confidence - use conservative parameters
        processing_params = get_conservative_parameters(context)

    # Execute processing
    start_time = time.time()
    try:
        result = execute_generation(processing_params)

        # Record successful context
        success_context = {
            **context,
            'result_quality': result['quality_score'],
            'processing_time': time.time() - start_time,
            'success': True
        }
        persistence_mgr.persist_context(session_id, success_context)

        return result

    except Exception as e:
        # Record failure context for learning
        failure_context = {
            **context,
            'error_type': type(e).__name__,
            'error_message': str(e),
            'processing_time': time.time() - start_time,
            'success': False
        }
        persistence_mgr.persist_context(session_id, failure_context)
        raise

CONTEXT-AWARE RESOURCE ALLOCATION:

# Intelligent resource allocation based on context
def allocate_resources_with_context(context: Dict) -> Dict:
    """Allocate computational resources based on context analysis"""

    allocation = {
        'gpu_memory': 8000,  # Default 8GB
        'cpu_threads': 4,    # Default 4 threads
        'batch_size': 1,     # Default single processing
        'quality_level': 7   # Default quality
    }

    # Adjust based on input complexity
    if context['input_analysis']['complexity_score'] > 0.8:
        allocation['gpu_memory'] = 12000  # 12GB for complex inputs
        allocation['quality_level'] = 9

    # Adjust based on system load
    if context['system_context']['current_load'] > 0.7:
        allocation['cpu_threads'] = 2  # Reduce CPU usage
        allocation['batch_size'] = 1   # No batching under high load

    # Adjust based on user priority
    if context.get('user_context', {}).get('priority') == 'high':
        allocation['gpu_memory'] = min(allocation['gpu_memory'] * 1.5, 16000)
        allocation['cpu_threads'] = min(allocation['cpu_threads'] * 2, 8)

    # Adjust based on deadline constraints
    deadline = context.get('quality_context', {}).get('deadline')
    if deadline and deadline < 30:  # Less than 30 seconds
        allocation['quality_level'] = 5  # Reduce quality for speed
        allocation['gpu_memory'] = 6000  # Use less memory for faster processing

        return allocation

[5.9] PERFORMANCE OPTIMIZATION PATTERNS

SPEED OPTIMIZATION WORKFLOW:

# Apply comprehensive speed optimizations
from performance_optimizer import PerformanceOptimizer

optimizer = PerformanceOptimizer()

# Model-level optimizations
@app.before_first_request
def optimize_models():
    """Apply optimizations before serving requests"""

    global hunyuan_processor

    # Apply speed optimizations to all models
    hunyuan_processor.shapegen_pipeline = optimizer.apply_speed_optimizations(
        hunyuan_processor.shapegen_pipeline, 'shape_generation'
    )

    hunyuan_processor.texgen_pipeline = optimizer.apply_speed_optimizations(
        hunyuan_processor.texgen_pipeline, 'texture_generation'
    )

    # Pre-warm caches
    optimizer.warm_model_caches()

    logger.info("[ORFEAS] Performance optimizations applied successfully")

# Request-level optimizations
@app.before_request
def optimize_request():
    """Apply per-request optimizations"""

    # Cache request context for faster processing
    g.start_time = time.time()
    g.optimization_context = optimizer.build_optimization_context(request)

    # Apply request-specific optimizations
    if g.optimization_context.get('enable_fast_mode', False):
        g.processing_mode = 'fast'
    elif g.optimization_context.get('enable_quality_mode', False):
        g.processing_mode = 'quality'
    else:
        g.processing_mode = 'balanced'

@app.after_request
def track_performance(response):
    """Track and optimize based on performance metrics"""

    processing_time = time.time() - g.get('start_time', time.time())

    # Update performance metrics
    optimizer.update_performance_metrics(
        endpoint=request.endpoint,
        processing_time=processing_time,
        mode=g.get('processing_mode', 'unknown'),
        success=response.status_code < 400
    )

    # Trigger auto-optimization if performance degrades
    if optimizer.should_auto_optimize(processing_time):
        optimizer.trigger_auto_optimization()

    return response

ACCURACY ENHANCEMENT PATTERNS:

# Quality-first generation workflow
from accuracy_enhancer import AccuracyEnhancer

enhancer = AccuracyEnhancer()

def generate_high_accuracy_3d(image_data: bytes, quality_requirements: Dict) -> Dict:
    """Generate 3D model with maximum accuracy focus"""

    # Build accuracy-focused context
    context = {
        'accuracy_priority': True,
        'quality_requirements': quality_requirements,
        'enable_ensemble': quality_requirements.get('enable_ensemble', False),
        'enable_refinement': quality_requirements.get('enable_refinement', True)
    }

    # Multi-stage accuracy enhancement
    try:
        # Stage 1: Input analysis and preprocessing
        preprocessed_input = enhancer.preprocess_for_accuracy(
            {'image': image_data}, context
        )

        # Stage 2: Enhanced generation
        result = enhancer.enhance_generation_accuracy(preprocessed_input, context)

        # Stage 3: Quality validation
        if result['accuracy_metrics']['quality_score'] < 0.85:
            logger.warning("[ORFEAS] Quality below threshold, applying refinement")
            result = enhancer.apply_advanced_refinement(result, context)

        return result

    except Exception as e:
        logger.error(f"[ORFEAS] High-accuracy generation failed: {e}")
        # Fallback to standard generation with basic enhancement
        return enhancer.generate_with_fallback_enhancement(preprocessed_input)

# Quality monitoring and feedback loop
@app.route('/api/quality-feedback', methods=['POST'])
def submit_quality_feedback():
    """Receive user quality feedback for continuous improvement"""

    feedback = request.get_json()

    # Update accuracy models based on feedback
    enhancer.update_quality_models(
        generation_id=feedback['generation_id'],
        user_rating=feedback['rating'],
        specific_issues=feedback.get('issues', []),
        improvements_suggested=feedback.get('improvements', [])
    )

    # Trigger model retraining if needed
    if enhancer.should_retrain_quality_models():
        enhancer.schedule_quality_model_retraining()

    return jsonify({'status': 'feedback_received', 'quality_updated': True})

SECURITY OPTIMIZATION PATTERNS:

# Multi-layer security validation
from security_hardening import SecurityHardening

security = SecurityHardening()

def secure_3d_generation_workflow(image_file, user_context: Dict) -> Dict:
    """Secure 3D generation with comprehensive protection"""

    # Layer 1: Input validation and sanitization
    try:
        validated_file = security.comprehensive_file_validation(image_file)
    except SecurityError as e:
        logger.security(f"[ORFEAS-SECURITY] File validation failed: {e}")
        raise ValidationError("File failed security validation")

    # Layer 2: User authentication and authorization
    if not security.validate_user_permissions(user_context, 'generate_3d'):
        logger.security(f"[ORFEAS-SECURITY] Unauthorized access attempt from {user_context}")
        raise AuthorizationError("Insufficient permissions")

    # Layer 3: Rate limiting and abuse protection
    if not security.check_rate_limits(user_context['user_id'], 'generation'):
        logger.security(f"[ORFEAS-SECURITY] Rate limit exceeded for user {user_context['user_id']}")
        raise RateLimitError("Generation rate limit exceeded")

    # Layer 4: Secure processing environment
    with security.secure_processing_context() as secure_env:
        result = process_generation_securely(validated_file, secure_env)

    # Layer 5: Output sanitization
    sanitized_result = security.sanitize_generation_output(result)

    # Layer 6: Audit logging
    security.log_secure_operation(
        operation='3d_generation',
        user=user_context['user_id'],
        input_hash=security.hash_input(validated_file),
        output_hash=security.hash_output(sanitized_result),
        success=True
    )

    return sanitized_result

# Real-time threat detection
@app.before_request
def detect_threats():
    """Real-time threat detection and mitigation"""

    threat_level = security.assess_request_threat_level(request)

    if threat_level >= security.THREAT_HIGH:
        # Block immediately and alert
        security.block_request_and_alert(request, threat_level)
        abort(403, "Request blocked due to security threat")

    elif threat_level >= security.THREAT_MEDIUM:
        # Enhanced monitoring
        security.enable_enhanced_monitoring(request)
        g.security_enhanced = True

    # Log all security assessments
    security.log_threat_assessment(request, threat_level)

ML INTEGRATION OPTIMIZATION PATTERNS:

# Optimized ML pipeline execution
from ml_integration_optimizer import MLIntegrationOptimizer

ml_optimizer = MLIntegrationOptimizer()

def optimized_ai_generation_pipeline(input_data: Dict, performance_profile: str) -> Dict:
    """Execute AI generation with optimal ML integration"""

    # Select optimal pipeline configuration
    pipeline_config = ml_optimizer.get_optimal_pipeline_config(performance_profile)

    # Apply ML-specific optimizations
    optimized_pipeline = ml_optimizer.optimize_ml_pipeline(pipeline_config)

    # Execute with intelligent resource management
    with ml_optimizer.managed_ml_execution(optimized_pipeline) as executor:

        # Pre-execution optimizations
        executor.prepare_models(input_data)
        executor.allocate_optimal_resources()

        # Parallel execution stages
        if optimized_pipeline['parallel_stages']:
            results = executor.execute_parallel_stages(
                input_data, optimized_pipeline['parallel_stages']
            )
        else:
            results = executor.execute_sequential_pipeline(input_data)

        # Post-execution cleanup
        executor.cleanup_resources()

    return results

# Adaptive model selection based on real-time performance
class AdaptiveModelSelector:
    def __init__(self):
        self.performance_history = {}
        self.model_rankings = {}

    def select_optimal_model(self, task_context: Dict) -> str:
        """Select best model based on current conditions"""

        # Analyze current system state
        system_state = self.analyze_system_state()

        # Consider task requirements
        task_requirements = self.extract_task_requirements(task_context)

        # Historical performance analysis
        historical_performance = self.get_historical_performance(
            task_requirements, system_state
        )

        # Multi-objective model selection
        model_scores = {}
        for model_name in self.available_models:
            scores = {
                'speed_score': self.calculate_speed_score(model_name, system_state),
                'accuracy_score': self.calculate_accuracy_score(model_name, task_context),
                'resource_score': self.calculate_resource_score(model_name, system_state),
                'reliability_score': self.calculate_reliability_score(model_name)
            }

            # Weighted combination based on priorities
            final_score = self.combine_scores(scores, task_requirements)
            model_scores[model_name] = final_score

        # Select best model
        optimal_model = max(model_scores, key=model_scores.get)

        # Update selection history
        self.update_selection_history(optimal_model, task_context, system_state)

        return optimal_model

# Initialize adaptive components
adaptive_selector = AdaptiveModelSelector()

@app.route('/api/adaptive-generate', methods=['POST'])
def adaptive_3d_generation():
    """AI generation with adaptive optimization"""

    # Extract request context
    task_context = {
        'image_complexity': analyze_image_complexity(request.files['image']),
        'quality_preference': request.form.get('quality', 7),
        'speed_preference': request.form.get('speed_preference', 'balanced'),
        'user_priority': get_user_priority(request.headers.get('X-User-ID'))
    }

    # Select optimal model adaptively
    optimal_model = adaptive_selector.select_optimal_model(task_context)

    # Execute with selected optimization profile
    performance_profile = determine_performance_profile(task_context)
    result = optimized_ai_generation_pipeline(
        {'image': request.files['image']},
        performance_profile
    )

    # Update performance tracking
    adaptive_selector.update_performance_tracking(optimal_model, result)

    return jsonify(result)

[6] CRITICAL INTEGRATION POINTS### [6.1] HUNYUAN3D-2.1 MODELS

Model Location:

Hunyuan3D-2.1/Hunyuan3D-2/
├ hunyuan3d-dit-v2-1/        # Shape generation (3.3GB)
├ hunyuan3d-paintpbr-v2-1/   # Texture generation (2GB)
└ hy3dgen/                   # Core generation pipelines

VRAM Requirements:

Shape generation only: 10GB
Texture generation only: 21GB
Full pipeline (shape + texture): 29GB
Model cache overhead: ~8GB (persistent)

Environment Variables:

HUNYUAN3D_PATH=../Hunyuan3D-2.1
DEVICE=cuda                    # auto, cuda, cpu
GPU_MEMORY_LIMIT=0.8           # 80% safety margin
XFORMERS_DISABLED=1            # Prevent DLL crash

[6.2] DOCKER GPU ACCELERATION

CRITICAL: NVIDIA Container Runtime Required

# docker-compose.yml
services:
  backend:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Verify GPU access:

docker exec orfeas-backend-production nvidia-smi

[6.3] WEBSOCKET COMMUNICATION

Progress Updates Pattern:

# Backend: Emit progress during generation
from flask_socketio import emit

@socketio.on('start_generation')
def handle_generation(data):
    for progress in range(0, 100, 10):
        emit('generation_progress', {
            'job_id': job_id,
            'progress': progress,
            'stage': 'shape_generation',
            'eta_seconds': estimated_remaining
        })

// Frontend: Listen for updates
socket.on("generation_progress", (data) => {
  updateProgressBar(data.progress);
  updateETA(data.eta_seconds);
});

[6.4] FILE UPLOAD VALIDATION

Security-Critical Patterns:

from validation import FileUploadValidator

validator = FileUploadValidator()

# ALWAYS validate before processing
image = validator.validate_image(request.files['file'])
# Checks: file type, size, dimensions, malicious content

# NEVER trust user input
filename = secure_filename(request.files['file'].filename)

Allowed formats:

Images: PNG, JPG, JPEG, GIF, BMP, TIFF, WEBP
3D models: STL, OBJ, PLY, GLTF, FBX
Max size: 50MB (configurable via
```
MAX_FILE_SIZE
```
)

[6.5] AI AGENT ORCHESTRATION

Agent Service Discovery:

# backend/agent_discovery.py
class AgentServiceDiscovery:
    def __init__(self):
        self.registered_agents = {}
        self.agent_capabilities = {}

    def register_agent(self, agent_id: str, capabilities: List[str], endpoint: str):
        """Register AI agent with its capabilities"""
        self.registered_agents[agent_id] = endpoint
        self.agent_capabilities[agent_id] = capabilities

    def find_agents_for_task(self, required_capability: str) -> List[str]:
        """Find agents capable of handling specific task"""
        capable_agents = []
        for agent_id, capabilities in self.agent_capabilities.items():
            if required_capability in capabilities:
                capable_agents.append(agent_id)
        return capable_agents

Agent Communication Protocol:

# Inter-agent communication for complex workflows
async def coordinate_generation_workflow(image_data: bytes, quality_requirements: Dict):
    """Coordinate multiple AI agents for optimal 3D generation"""

    # 1. Quality assessment agent analyzes input
    quality_agent = await get_agent('quality_assessment')
    analysis = await quality_agent.analyze_input(image_data)

    # 2. Workflow agent selects optimal pipeline
    workflow_agent = await get_agent('workflow_orchestration')
    pipeline = await workflow_agent.select_pipeline(analysis)

    # 3. Optimization agent configures parameters
    optimization_agent = await get_agent('performance_optimization')
    optimized_params = await optimization_agent.optimize_parameters(pipeline, analysis)

    # 4. Execute generation with optimized settings
    result = await execute_generation_pipeline(pipeline, optimized_params)

    return result

[6.6] ALTERNATIVE AI MODEL INTEGRATION

Multi-Model Fallback Chain:

# backend/multi_model_manager.py
class MultiModelGenerationManager:
    def __init__(self):
        self.model_chain = [
            'hunyuan3d_2_1',      # Primary model
            'instant_mesh',        # HuggingFace fallback
            'stable_diffusion_3d', # SD-based 3D generation
            'comfyui_workflow'     # ComfyUI alternative
        ]

    async def generate_with_fallback(self, input_data: Dict) -> Dict:
        """Try models in order until one succeeds"""
        last_error = None

        for model_name in self.model_chain:
            try:
                logger.info(f"[ORFEAS] Attempting generation with {model_name}")
                result = await self.generate_with_model(model_name, input_data)

                # Validate result quality
                if self.validate_generation_quality(result):
                    logger.info(f"[ORFEAS] Successful generation with {model_name}")
                    return result
                else:
                    logger.warning(f"[ORFEAS] {model_name} produced low-quality result")
                    continue

            except Exception as e:
                logger.warning(f"[ORFEAS] {model_name} failed: {e}")
                last_error = e
                continue

        # All models failed
        logger.error(f"[ORFEAS] All generation models failed. Last error: {last_error}")
        raise RuntimeError("All generation methods exhausted")

Model Performance Monitoring:

# Track performance metrics for each AI model
from production_metrics import ModelPerformanceTracker

perf_tracker = ModelPerformanceTracker()

@perf_tracker.track_model_performance
async def generate_with_model(model_name: str, input_data: Dict):
    """Generate 3D model with performance tracking"""
    start_time = time.time()

    try:
        model = await load_model(model_name)
        result = await model.generate(input_data)

        # Track success metrics
        duration = time.time() - start_time
        perf_tracker.record_success(model_name, duration, result.quality_score)

        return result

    except Exception as e:
        # Track failure metrics
        duration = time.time() - start_time
        perf_tracker.record_failure(model_name, duration, str(e))
        raise

[6.7] INTELLIGENT CONTEXT INTEGRATION

CONTEXT DATABASE SETUP:

# backend/context_database.py
class ContextDatabase:
    """
    Context persistence and retrieval system
    """

    def __init__(self, db_path: str = "data/context.db"):
        self.db_path = db_path
        self.connection = self.initialize_database()

    def initialize_database(self):
        """Create context tables if they don't exist"""
        conn = sqlite3.connect(self.db_path)
        conn.execute("""
            CREATE TABLE IF NOT EXISTS context_history (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                session_id TEXT NOT NULL,
                timestamp REAL NOT NULL,
                context_data TEXT NOT NULL,
                context_hash TEXT NOT NULL,
                success BOOLEAN NOT NULL,
                processing_time REAL,
                quality_score REAL,
                model_used TEXT,
                resource_usage TEXT
            )
        """)
        conn.execute("""
            CREATE INDEX IF NOT EXISTS idx_context_hash ON context_history(context_hash);
        """)
        conn.execute("""
            CREATE INDEX IF NOT EXISTS idx_timestamp ON context_history(timestamp);
        """)
        return conn

CONTEXT-AWARE INITIALIZATION:

# Always initialize context manager at startup
from context_manager import IntelligentContextManager
from contextual_agent_coordinator import ContextualAgentCoordinator

def initialize_context_system():
    """Initialize intelligent context handling system"""

    # Initialize core context manager
    context_mgr = IntelligentContextManager()

    # Initialize agent coordinator with context
    agent_coordinator = ContextualAgentCoordinator(context_mgr)

    # Register available agents
    agent_coordinator.register_agent(
        'quality_assessment',
        ['image_analysis', 'complexity_scoring'],
        'http://localhost:5001/quality'
    )

    agent_coordinator.register_agent(
        'model_selection',
        ['model_recommendation', 'performance_prediction'],
        'http://localhost:5002/model-select'
    )

    # Start context learning background task
    if os.getenv('CONTEXT_LEARNING_ENABLED') == 'true':
        context_mgr.start_learning_daemon()

    return context_mgr, agent_coordinator

# Initialize at application startup
context_manager, agent_coordinator = initialize_context_system()

CONTEXT MIDDLEWARE INTEGRATION:

# Flask middleware for automatic context building
from flask import g

@app.before_request
def build_request_context():
    """Build context for every request automatically"""

    if not hasattr(g, 'context'):
        request_data = {
            'endpoint': request.endpoint,
            'method': request.method,
            'user_agent': request.headers.get('User-Agent'),
            'ip_address': request.remote_addr,
            'timestamp': time.time()
        }

        # Add file data if present
        if request.files:
            request_data['files'] = {
                name: {
                    'filename': file.filename,
                    'size': len(file.read()),
                    'content_type': file.content_type
                } for name, file in request.files.items()
            }
            # Reset file streams
            for file in request.files.values():
                file.seek(0)

        # Build comprehensive context
        g.context = context_manager.build_processing_context(request_data)
        g.recommendations = context_manager.get_contextual_recommendations(g.context)

@app.after_request
def update_context_learning(response):
    """Update context learning based on request outcome"""

    if hasattr(g, 'context') and hasattr(g, 'processing_result'):
        # Update learning based on request success/failure
        success = response.status_code < 400

        learning_data = {
            'context': g.context,
            'success': success,
            'status_code': response.status_code,
            'processing_time': getattr(g, 'processing_time', 0),
            'response_size': len(response.get_data())
        }

        context_manager.update_learning(learning_data)

    return response

CONTEXT-BASED MONITORING:

# Enhanced monitoring with context awareness
from prometheus_client import Counter, Histogram, Gauge

# Context-aware metrics
context_processing_time = Histogram(
    'context_processing_seconds',
    'Time spent on context processing',
    ['context_type', 'complexity_level']
)

context_accuracy = Gauge(
    'context_prediction_accuracy',
    'Accuracy of context-based predictions',
    ['prediction_type']
)

context_cache_hits = Counter(
    'context_cache_hits_total',
    'Number of context cache hits',
    ['cache_type']
)

def track_context_performance(func):
    """Decorator to track context-aware performance"""
    def wrapper(*args, **kwargs):
        context = g.get('context', {})
        complexity = context.get('input_analysis', {}).get('complexity_score', 0)

        complexity_level = 'simple' if complexity < 0.3 else 'medium' if complexity < 0.7 else 'complex'

        with context_processing_time.labels(
            context_type=context.get('input_analysis', {}).get('input_type', 'unknown'),
            complexity_level=complexity_level
        ).time():
            result = func(*args, **kwargs)

        return result
    return wrapper

[7] PRODUCTION DEPLOYMENT & ENTERPRISE MARKET POSITIONING

COMPREHENSIVE PRODUCTION DEPLOYMENT GUIDE:

For complete production deployment, enterprise sales enablement, industry partnerships, and competitive market positioning documentation, see:

📄

md/ORFEAS_PRODUCTION_DEPLOYMENT_OPTIMIZATION.md

This comprehensive document includes:

Enterprise Production Deployment Architecture with 99.99% SLA guarantees
Enterprise Sales & Customer Success Platform with ROI calculators
Strategic Technology Partnerships with Microsoft, Google, AWS, NVIDIA
Competitive Market Positioning targeting $1B revenue by 2027
Industry Leadership Strategy with thought leadership initiatives

Key Production Metrics:

Total Addressable Market: $5.5 Billion
Five-Year Revenue Target: $1 Billion (18% market share)
Enterprise SLA: 99.99% uptime with automatic credits
ROI Guarantee: 300-500% productivity improvements
Market Position: Industry-leading AI multimedia platform

Enterprise Deployment Capabilities:

Multi-region Kubernetes deployment with auto-scaling
Enterprise-grade security with SOC2, ISO27001, GDPR, HIPAA compliance
24/7 platinum support with dedicated success managers
Strategic partnerships for global market expansion
Competitive intelligence and market positioning strategies

══════════════════════════════════════════════════════════════════════════════╗ ⚔️ END OF ORFEAS COPILOT INSTRUCTIONS ⚔️ â•' â•' GITHUB COPILOT: Use these instructions for all ORFEAS AI 2D→3D Studio work â•' AGENT BEHAVIOR: Autonomous, precise, project-specific code generation â•' â•' CODE QUALITY: Python best practices, GPU optimization, security-first â•' â•' â•' â•' ══════════════════════════════════════════════════════════════════════════════

ORFEAS AI 2D→3D STUDIO - GITHUB COPILOT INSTRUCTIONS

⚠️ CRITICAL FILE ORGANIZATION RULES

ALL .md FILES MUST BE PLACED IN THE

md\

DIRECTORY

✅ CORRECT:
```
md\SESSION_REPORT.md
```
❌ WRONG:
```
SESSION_REPORT.md
```
(root directory)

ALL .txt FILES MUST BE PLACED IN THE

txt\

DIRECTORY

✅ CORRECT:
```
txt\VISUAL_GUIDE.txt
```
❌ WRONG:
```
VISUAL_GUIDE.txt
```
(root directory)

[1] PROJECT IDENTITY

TECHNOLOGY STACK:

Backend: Python 3.10+ with Flask 2.3.3, FastAPI, Gunicorn
Web Technologies: HTML5, CSS3, JavaScript ES6+, PWA (Progressive Web App)
Security: SSL/TLS certificates, HTTPS enforcement, secure headers
Alternative Backend: PHP 8.0+ support for legacy integrations
AI Engine: Hunyuan3D-2.1 (shape + texture generation)
Video AI Engine: Sora-inspired video composition and generation
Code Development AI: GitHub Copilot, CodeT5+, StarCoder integration
Text-to-Image AI: DALL-E 3, Stable Diffusion XL, Midjourney API integration
Text-to-Speech AI: ElevenLabs, Azure Cognitive Speech, OpenAI TTS
Speech-to-Text AI: Whisper OpenAI, Azure Speech Services, Google Speech-to-Text
3D Processing: trimesh, Open3D, numpy-stl, PyMeshLab
Video Processing: OpenCV, FFmpeg, MediaPipe
GPU: PyTorch 2.0.1 with CUDA support (RTX 3090 optimized)
Frontend: HTML5 + Vanilla JS + WebGL/Three.js + BabylonJS
Web Server: Nginx with SSL termination, Apache mod_wsgi support
Container: Docker Compose with GPU acceleration + HTTPS, Kubernetes support
Cloud Platform: AWS, Azure, GCP compatible deployment
API: RESTful APIs, GraphQL, WebSocket real-time communication
Database: PostgreSQL, Redis, MongoDB support
Message Queue: RabbitMQ, Apache Kafka for enterprise workflows
Monitoring: Prometheus + Grafana + Redis + ELK Stack
CI/CD: GitHub Actions, Jenkins, GitLab CI integration
Testing: pytest with 26+ security tests, Selenium E2E testing

MULTI-ENCODING & INTERNATIONALIZATION STACK:

Character Encoding Support:
- UTF-8 (primary encoding with BOM detection)
- UTF-16 LE/BE (Windows and Unicode standard)
- UTF-32 LE/BE (full Unicode character set)
- ASCII (7-bit compatibility mode)
- Latin-1 (ISO-8859-1) for legacy systems
- CP1252 (Windows-1252) for Windows legacy
- GB2312 & GBK (Simplified Chinese)
- Big5 (Traditional Chinese)
- Shift-JIS (Japanese)
- EUC-KR (Korean)
- ISO-8859 series (European languages)
Encoding Detection & Conversion:
- chardet (automatic encoding detection)
- codecs (Python native encoding handlers)
- ftfy (fixes mojibake and encoding errors)
- unicodedata (Unicode normalization)
- charset-normalizer (advanced encoding detection)
Internationalization (i18n) Framework:
- Flask-Babel (internationalization for Flask)
- gettext (GNU internationalization utilities)
- babel (Python internationalization library)
- langdetect (language detection from text)
- polyglot (multilingual NLP toolkit)
- langcodes (language code standardization)

MULTI-CODE LANGUAGE PROGRAMMING STACK:

Primary Development Languages:
- Python 3.10+ (primary backend, AI/ML processing)
- JavaScript ES6+ (frontend, Node.js backend)
- TypeScript 4.9+ (type-safe JavaScript development)
- HTML5 + CSS3 (modern web interfaces)
- SQL (PostgreSQL, MySQL, SQLite)
Enterprise Programming Languages:
- PHP 8.0+ (legacy system integration)
- Java 17+ (enterprise backend services)
- C# .NET 6+ (Microsoft ecosystem integration)
- Go 1.19+ (high-performance microservices)
- Rust (system programming, WebAssembly)
- C++ (GPU computing, performance-critical code)
Specialized Programming Languages:
- R (statistical computing, data science)
- MATLAB (scientific computing, simulations)
- Swift (iOS/macOS native applications)
- Kotlin (Android native, JVM interop)
- Dart (Flutter cross-platform apps)
- WebAssembly (WASM) (high-performance web applications)
Infrastructure & DevOps Languages:
- Shell/Bash (Linux automation scripts)
- PowerShell (Windows automation)
- YAML (configuration files, CI/CD)
- JSON (data interchange, APIs)
- XML (enterprise data formats)
- TOML (configuration files)
Database Query Languages:
- SQL (ANSI SQL standard)
- NoSQL Query Languages (MongoDB, Cassandra)
- GraphQL (API query language)
- SPARQL (RDF query language)
Code Analysis & Cross-Language Integration:
- Language Server Protocol (LSP) integration
- Tree-sitter (universal syntax highlighting)
- ANTLR (cross-language parser generator)
- Protocol Buffers (cross-language serialization)
- gRPC (cross-language RPC framework)
- Apache Thrift (cross-language services)
Multi-Language AI Code Generation:
- GitHub Copilot (multi-language code completion)
- OpenAI Codex (cross-language code generation)
- CodeT5+ (code translation between languages)
- StarCoder (open-source multi-language model)
- DeepSeek-Coder (advanced multi-language understanding)
- Tabnine (AI-powered multi-language completion)

ENTERPRISE AI & ML ECOSYSTEM:

Primary AI Engine: Hunyuan3D-2.1 (Tencent's enterprise-grade 2D→3D generation)
Cinematic AI Engine: Sora-class enterprise video composition and cinematic generation
Intelligent Code Development Platform: Enterprise code writing, automated debugging, and performance optimization
Text-to-Image AI Engine: Enterprise-grade image generation from text prompts with DALL-E 3, Stable Diffusion XL, and Midjourney API
Text-to-Speech AI Engine: High-fidelity voice synthesis with ElevenLabs, Azure Cognitive Speech, and OpenAI TTS
Speech-to-Text AI Engine: Advanced speech recognition with Whisper OpenAI, Azure Speech Services, and Google Speech-to-Text

LARGE LANGUAGE MODEL (LLM) INTEGRATION:

Foundation Models Stack:
- GPT-4 Turbo & GPT-4o (OpenAI enterprise API integration)
- Claude 3.5 Sonnet & Claude 3 Opus (Anthropic enterprise models)
- Gemini Ultra & Gemini Pro (Google enterprise LLM suite)
- LLaMA 3.1 405B & 70B (Meta's open-source enterprise models)
- Mistral 8x22B & Mixtral 8x7B (Mistral AI enterprise deployment)
- Command R+ & Command (Cohere enterprise language models)
- PaLM 2 & Bard Enterprise (Google Cloud AI integration)
Specialized LLM Applications:
- Code Generation & Review (CodeLlama 34B, StarCoder2, DeepSeek-Coder V2)
- Technical Documentation (Claude 3 Haiku for rapid content generation)
- Multimodal Understanding (GPT-4 Vision, Gemini Pro Vision, Claude 3 Opus)
- Reasoning & Analysis (GPT-4o for complex problem solving)
- Conversational AI (ChatGPT Enterprise, Claude Enterprise)
LLM Optimization & Deployment:
- vLLM & Text Generation Inference (TGI) for high-throughput serving
- NVIDIA TensorRT-LLM for GPU-optimized inference
- Hugging Face Transformers with custom fine-tuning pipelines
- LoRA & QLoRA for efficient model adaptation
- Quantization (AWQ, GPTQ, SmoothQuant) for memory optimization
- Model parallelism & distributed inference across multiple GPUs
Enterprise LLM Features:
- Custom fine-tuning on proprietary datasets
- RAG (Retrieval-Augmented Generation) with vector databases
- Function calling & tool integration
- Chain-of-thought prompting & reasoning
- Multi-agent LLM orchestration
- Enterprise-grade safety & content filtering

ADVANCED MACHINE LEARNING STACK:

Deep Learning Frameworks:
- PyTorch 2.1+ Lightning (primary framework with advanced optimizations)
- TensorFlow Enterprise 2.15+ (production-grade deployment)
- JAX & Flax (Google's high-performance ML framework)
- Hugging Face Transformers 4.35+ (state-of-the-art NLP models)
- Diffusers Pro (advanced diffusion model implementations)
- Stable Baselines3 (reinforcement learning algorithms)
Computer Vision & Multimodal AI:
- OpenCV 4.8+ Professional (advanced computer vision operations)
- MediaPipe Pro (real-time perception pipelines)
- CLIP & ALIGN (vision-language understanding models)
- YOLO v8 & v9 (real-time object detection)
- Segment Anything Model (SAM) for universal segmentation
- REMBG Pro (advanced background removal)
- Detectron2 (Facebook's object detection platform)
Natural Language Processing:
- spaCy Enterprise (industrial-strength NLP)
- NLTK & TextBlob (comprehensive text processing)
- SentenceTransformers (semantic text embeddings)
- Named Entity Recognition (NER) with custom models
- Sentiment analysis & emotion detection
- Text summarization & generation pipelines
Audio & Speech Processing:
- Whisper Enterprise (OpenAI's speech recognition)
- Wav2Vec 2.0 (Facebook's speech representation learning)
- VALL-E X (voice synthesis and cloning)
- Real-time speech-to-text and text-to-speech
- Audio classification & sound event detection
- Music generation with MusicLM integration

ENTERPRISE AI AGENTS & AUTONOMOUS SYSTEMS:

Multi-Agent Frameworks:
- LangChain Enterprise (LLM application development framework)
- LlamaIndex Pro (data framework for LLM applications)
- AutoGen (Microsoft's multi-agent conversation framework)
- CrewAI (role-playing autonomous agent framework)
- Semantic Kernel (Microsoft's AI orchestration platform)
- LangGraph (advanced agent workflow orchestration)
- OpenAI Assistants API (enterprise assistant creation)
- Anthropic Claude Computer Use (computer interaction agents)
- Microsoft Copilot Studio (enterprise bot framework)
- Google Vertex AI Agent Builder (cloud-native agent platform)
Advanced Agent Capabilities:
- Autonomous Task Planning & Execution:
  - Dynamic goal decomposition and hierarchical planning
  - Multi-step workflow orchestration with conditional branching
  - Resource allocation and constraint satisfaction
  - Parallel task execution with dependency management
  - Real-time replanning and adaptation to changing conditions
  - Progress monitoring and milestone tracking
- Multi-Modal Perception & Reasoning:
  - Vision-language understanding and cross-modal reasoning
  - Audio processing and speech recognition/synthesis
  - Document analysis and structured data extraction
  - Real-time sensor data integration and fusion
  - Spatial reasoning and 3D environment understanding
  - Temporal pattern recognition and sequence analysis
- Advanced Tool Use & API Integration:
  - Dynamic tool discovery and capability assessment
  - API schema understanding and automatic integration
  - Function calling with parameter validation and error handling
  - Tool chaining and complex workflow automation
  - External system integration (databases, cloud services, IoT)
  - Custom tool creation and deployment
- Intelligent Memory Management & Knowledge Persistence:
  - Episodic memory for experience-based learning
  - Semantic memory with knowledge graph integration
  - Working memory optimization for real-time processing
  - Long-term memory consolidation and retrieval
  - Context-aware information retention and forgetting
  - Cross-session continuity and knowledge transfer
- Collaborative Problem-Solving & Team Coordination:
  - Multi-agent negotiation and consensus building
  - Role-based specialization and expertise sharing
  - Conflict resolution and decision arbitration
  - Load balancing and task distribution
  - Swarm intelligence and collective behavior
  - Inter-agent communication protocols
- Self-Improvement & Adaptive Learning:
  - Reinforcement learning from human feedback (RLHF)
  - Continuous learning from experience and performance metrics
  - Self-reflection and capability assessment
  - Automated hyperparameter optimization
  - Knowledge distillation and model compression
  - Meta-learning for rapid adaptation to new domains
- Enterprise Security & Compliance:
  - Role-based access control and permission management
  - Audit logging and compliance tracking
  - Data privacy and anonymization protocols
  - Secure communication and encryption
  - Threat detection and anomaly identification
  - Regulatory compliance automation (GDPR, HIPAA, SOX)
Specialized Enterprise Agent Types:
- Development & Engineering Agents:
  - Code review and optimization agents with security scanning
  - Automated testing and quality assurance agents
  - Documentation generation and maintenance agents
  - DevOps and CI/CD pipeline management agents
  - Architecture analysis and recommendation agents
  - Performance profiling and optimization agents
  - Dependency management and security vulnerability agents
- Business Intelligence & Analytics Agents:
  - Data analysis and insight generation agents
  - Report automation and dashboard creation agents
  - Predictive analytics and forecasting agents
  - Market research and competitive analysis agents
  - Customer behavior analysis and segmentation agents
  - Risk assessment and mitigation planning agents
- Operations & Infrastructure Agents:
  - System monitoring and alerting agents
  - Incident response and resolution agents
  - Capacity planning and resource optimization agents
  - Security monitoring and threat response agents
  - Backup and disaster recovery agents
  - Network optimization and troubleshooting agents
- Customer Experience & Support Agents:
  - Intelligent customer support and ticketing agents
  - Personalization and recommendation agents
  - Content creation and marketing automation agents
  - Voice of customer analysis and feedback agents
  - Multi-channel communication orchestration agents
  - Customer journey optimization agents
- Research & Innovation Agents:
  - Literature review and knowledge synthesis agents
  - Hypothesis generation and experimental design agents
  - Patent analysis and IP research agents
  - Technology trend analysis and forecasting agents
  - Innovation pipeline management agents
  - Scientific workflow automation agents
- Financial & Compliance Agents:
  - Financial analysis and reporting automation agents
  - Fraud detection and prevention agents
  - Regulatory compliance monitoring agents
  - Risk management and assessment agents
  - Audit trail and documentation agents
  - Contract analysis and management agents
Agent Orchestration Patterns:
- Hierarchical Agent Architecture:
  - Master agent coordinating specialized sub-agents
  - Chain of command with escalation protocols
  - Resource allocation and priority management
  - Cross-functional team coordination
- Peer-to-Peer Agent Networks:
  - Distributed problem-solving with consensus mechanisms
  - Expertise sharing and knowledge federation
  - Fault tolerance and redundancy management
  - Load balancing and workload distribution
- Event-Driven Agent Systems:
  - Reactive agents responding to system events
  - Event streaming and real-time processing
  - Complex event processing and pattern matching
  - Workflow triggers and automation chains
- Hybrid Human-AI Agent Teams:
  - Human-in-the-loop decision making
  - Augmented intelligence and collaborative workflows
  - Handoff protocols and escalation mechanisms
  - Human oversight and intervention capabilities
Agent Performance & Optimization:
- Performance Metrics & KPIs:
  - Task completion rate and accuracy metrics
  - Response time and throughput optimization
  - Resource utilization and efficiency tracking
  - User satisfaction and interaction quality
  - Learning curve and improvement trajectories
  - Cost-benefit analysis and ROI measurement
- Continuous Improvement Framework:
  - A/B testing for agent behavior optimization
  - Performance benchmarking against industry standards
  - Automated model retraining and deployment
  - Feedback loop integration and learning cycles
  - Version control and rollback capabilities
  - Quality gates and approval workflows

ENTERPRISE COMPUTER VISION STACK:

Core Vision Processing: REMBG, OpenCV 4.8+, PIL/Pillow, MediaPipe Pro
3D Vision & Reconstruction: Open3D, PyMeshLab, trimesh, Point Cloud Library (PCL)
Advanced Image Processing: Scikit-image, ImageIO, Albumentations (data augmentation)
Real-time Processing: NVIDIA DeepStream, Intel OpenVINO, TensorRT
Specialized Models: ESRGAN (super-resolution), Real-ESRGAN, EDSR, SwinIR

PRODUCTION ML FRAMEWORKS:

Core Frameworks: PyTorch 2.1+ Lightning, Transformers 4.35+, Diffusers Pro, TensorFlow Enterprise
Model Training: Accelerate (Hugging Face), DeepSpeed (Microsoft), FairScale (Facebook)
Distributed Training: Horovod, PyTorch Distributed, TensorFlow MultiWorkerMirroredStrategy
Experiment Tracking: Weights & Biases, MLflow, Neptune, TensorBoard Pro

ENTERPRISE AI AGENTS & MLOps:

Agent Infrastructure & Orchestration:
- Kubernetes-native agent orchestration with auto-scaling
- Docker containerized agent deployment
- Service mesh integration (Istio, Linkerd) for agent communication
- Enterprise authentication (OAuth2, SAML, Active Directory)
- Multi-cloud agent deployment (AWS EKS, Azure AKS, GCP GKE)
- Agent lifecycle management and versioning
- Health monitoring and failover mechanisms
- Resource quota management and cost optimization
Agent Communication & Coordination:
- Message bus integration (Apache Kafka, RabbitMQ, Azure Service Bus)
- Event-driven architecture with pub/sub patterns
- gRPC and REST API communication protocols
- GraphQL federation for distributed agent queries
- WebSocket real-time agent coordination
- Circuit breaker patterns for resilient communication
- Rate limiting and backpressure handling
- Distributed tracing and observability
Agent Development & Deployment:
- CI/CD for ML agents (GitHub Actions ML, Jenkins X, GitLab CI/CD, Azure DevOps)
- Automated agent testing and validation pipelines
- Blue-green and canary deployment strategies
- Configuration management and feature flags
- Environment promotion (dev → staging → production)
- Rollback and recovery mechanisms
- Performance testing and load validation
- Security scanning and vulnerability assessment
Agent Model Management:
- MLflow Enterprise for agent model lifecycle
- Kubeflow Pipelines for agent training workflows
- Model registry and versioning for agent brains
- A/B testing frameworks for agent behavior
- Model performance monitoring and drift detection
- Automated retraining and model updates
- Multi-model serving and routing
- Model compression and optimization for edge deployment
Agent Monitoring & Observability:
- Prometheus metrics collection for agent performance
- Grafana dashboards for agent health visualization
- ELK Stack (Elasticsearch, Logstash, Kibana) for log analysis
- Distributed tracing with Jaeger and Zipkin
- APM integration (Datadog, New Relic, AppDynamics)
- Custom agent KPIs and business metrics
- Alerting and incident management
- Performance profiling and bottleneck identification
Specialized Agent Monitoring Tools:
- Evidently AI for agent behavior drift detection
- WhyLabs for agent data quality monitoring
- Arize AI for agent performance observability
- Fiddler AI for agent explainability and fairness
- Weights & Biases for agent experiment tracking
- Neptune for agent metadata management
- TensorBoard for agent training visualization
- MLflow for agent artifact tracking
Agent Security & Compliance:
- Zero-trust security model for agent networks
- Mutual TLS (mTLS) for secure agent communication
- API gateway security and rate limiting
- Secret management (HashiCorp Vault, AWS Secrets Manager)
- RBAC (Role-Based Access Control) for agent permissions
- Audit logging and compliance reporting
- Data encryption at rest and in transit
- Vulnerability scanning and security patching
Agent Data Management:
- Vector databases for agent knowledge storage (Pinecone, Weaviate, Qdrant)
- Graph databases for agent relationship modeling (Neo4j, Amazon Neptune)
- Time-series databases for agent metrics (InfluxDB, TimescaleDB)
- Cache layers for agent response optimization (Redis, Memcached)
- Data lineage tracking for agent decisions
- GDPR and privacy compliance for agent data
- Data versioning and provenance tracking
- Distributed storage for large agent datasets
Agent Performance Optimization:
- GPU acceleration for compute-intensive agents
- Model quantization and pruning for efficiency
- Batching strategies for high-throughput agents
- Caching layers for frequently accessed data
- Load balancing across agent instances
- Horizontal and vertical scaling policies
- Resource allocation optimization
- Cost monitoring and optimization strategies
Enterprise Agent Patterns:
- Microservices architecture for agent components
- Event sourcing for agent state management
- CQRS (Command Query Responsibility Segregation) patterns
- Saga patterns for distributed agent transactions
- Bulkhead patterns for agent isolation
- Timeout and retry patterns for resilience
- Circuit breaker patterns for fault tolerance
- Strangler fig patterns for legacy system integration

MULTI-CLOUD AI ENGINES:

Stable Diffusion XL Enterprise (high-resolution text-to-image)
OpenAI CLIP Enterprise (multimodal understanding)
ControlNet Pro (precision-guided generation)
InstantMesh Enterprise (scalable 3D generation)
DALL-E Integration (premium image generation)
Midjourney API (artistic content generation)

ENTERPRISE CODE INTELLIGENCE PLATFORMS:

GitHub Copilot Enterprise (AI-powered development workflows)
CodeT5+ Enterprise (large-scale code generation and completion)
StarCoder Enterprise (enterprise open-source code generation)
DeepSeek-Coder Pro (advanced debugging and optimization)
Cursor AI Enterprise (intelligent enterprise code editing)
Amazon CodeWhisperer (AWS-native code intelligence)
Tabnine Enterprise (contextual code completion)

PRODUCTION ML OPTIMIZATION STACK:

NVIDIA TensorRT 8.6+ with DLA optimization
CUDA 12.0+ with multi-GPU acceleration and mixed precision (FP16/BF16/INT8)
Intel OpenVINO for CPU inference optimization
ONNX Runtime Enterprise for cross-platform deployment
Apache TVM for hardware-agnostic optimization
Google TPU integration for large-scale training

ENTERPRISE AI WORKFLOW MANAGEMENT:

Kubernetes-native batch inference pipelines with auto-scaling
MLflow Enterprise for model lifecycle management and A/B testing
Kubeflow Pipelines for enterprise ML workflows
Apache Airflow for complex AI workflow orchestration
Ray Serve for distributed model serving and scaling
NVIDIA Triton Inference Server for production model deployment
Automated model selection with reinforcement learning optimization
Enterprise model governance and compliance frameworks

VECTOR DATABASES & KNOWLEDGE MANAGEMENT:

Enterprise Vector Stores:
- Pinecone Enterprise (managed vector database service)
- Weaviate (open-source vector search engine)
- Qdrant (high-performance vector similarity search)
- Chroma (AI-native open-source embedding database)
- Milvus (cloud-native vector database)
- FAISS (Facebook AI Similarity Search) for local deployments
Knowledge Graph Integration:
- Neo4j Enterprise (graph database for connected data)
- Amazon Neptune (fully managed graph database)
- Microsoft Cosmos DB (multi-model database with graph support)
- Apache Jena (semantic web framework)
Retrieval-Augmented Generation (RAG):
- LangChain RAG pipelines with custom retrievers
- LlamaIndex advanced query engines
- Haystack (end-to-end NLP framework)
- Custom embedding models for domain-specific retrieval

REINFORCEMENT LEARNING & OPTIMIZATION:

RL Frameworks:
- Stable Baselines3 (high-quality RL algorithm implementations)
- Ray RLlib (scalable reinforcement learning library)
- OpenAI Gym & Gymnasium (RL environment standards)
- DeepMind Lab & dm_control (advanced RL environments)
Optimization Algorithms:
- Optuna (hyperparameter optimization framework)
- Ray Tune (distributed hyperparameter tuning)
- Weights & Biases Sweeps (experiment management)
- AutoML with Auto-sklearn & Auto-PyTorch

EDGE AI & MOBILE DEPLOYMENT:

Model Optimization:
- TensorFlow Lite (mobile and embedded deployment)
- ONNX Runtime Mobile (cross-platform inference)
- PyTorch Mobile (mobile-optimized PyTorch)
- OpenVINO (Intel's inference optimization toolkit)
Hardware Acceleration:
- NVIDIA Jetson integration (edge AI computing)
- Google Coral TPU (edge machine learning accelerator)
- Apple Core ML (iOS/macOS optimized inference)
- Qualcomm Neural Processing SDK

WEB TECHNOLOGIES & SECURITY STACK:

HTML5 Frontend Architecture:
- Progressive Web App (PWA) with offline support
- Responsive design with CSS Grid and Flexbox
- WebGL/Three.js for 3D model visualization
- BabylonJS for advanced 3D engine features
- Service Worker for caching and background sync
SSL/TLS Security:
- HTTPS enforcement with 301 redirects
- TLS 1.3 with perfect forward secrecy
- SSL certificate auto-renewal via Let's Encrypt
- HSTS headers and secure cookie settings
- CSP (Content Security Policy) implementation
PHP Integration (Legacy Support):
- PHP 8.0+ compatibility layer
- FastCGI/PHP-FPM integration
- Session management and authentication
- Database connectivity (MySQL/PostgreSQL)
- API endpoint compatibility
Web Server Configuration:
- Nginx as reverse proxy with SSL termination
- Apache mod_wsgi for Python WSGI applications
- Static file serving with compression
- Rate limiting and DDoS protection
- Health checks and monitoring endpoints
- Apache mod_wsgi for Python WSGI applications
- Static file serving with compression
- Rate limiting and DDoS protection
- Health checks and monitoring endpoints

âš¡ MULTI-AGENT SYSTEM OPTIMIZATION

AGENT ROLE SPECIALIZATION & COORDINATION:

The ORFEAS platform is optimized for multi-agent collaboration with intelligent task distribution and context sharing across specialized AI agents.

PRIMARY AGENT ROLES:

ORFEAS Platform Specialist: Expert in 3D generation, multimedia AI, and enterprise architecture
Frontend Development Agent: HTML5, JavaScript, CSS, PWA, and WebGL/Three.js specialist
Backend Development Agent: Python, Flask, PyTorch, GPU optimization, and API design expert
DevOps & Infrastructure Agent: Docker, Kubernetes, monitoring, and enterprise deployment specialist
Security & Compliance Agent: Zero-trust security, penetration testing, and regulatory compliance expert
Performance Optimization Agent: GPU memory management, model optimization, and benchmarking specialist
Quality Assurance Agent: TQM audits, testing frameworks, and continuous quality improvement expert

MULTI-AGENT COORDINATION PROTOCOLS:

# Agent communication standards
class AgentMessage:
    def __init__(self, sender_id: str, recipient_id: str, task_type: str, payload: Dict):
        self.message_id = generate_uuid()
        self.sender_id = sender_id
        self.recipient_id = recipient_id
        self.task_type = task_type
        self.payload = payload
        self.context_hash = self.calculate_context_hash()
        self.timestamp = datetime.utcnow()
        self.version = "1.0"

# Task distribution framework
AGENT_TASK_ROUTING = {
    'frontend_development': 'frontend_agent',
    'backend_api_design': 'backend_agent',
    'performance_optimization': 'performance_agent',
    'security_review': 'security_agent',
    'infrastructure_deployment': 'devops_agent',
    'quality_assurance': 'qa_agent',
    'multimedia_ai_integration': 'orfeas_specialist'
}

# Context sharing mechanism
class SharedContext:
    def __init__(self):
        self.project_state = {}
        self.active_tasks = {}
        self.agent_capabilities = {}
        self.performance_metrics = {}
        self.version_control = VersionController()

    async def synchronize_context(self, agent_id: str, local_context: Dict):
        """Synchronize context across all agents"""
        conflict_resolution = await self.detect_conflicts(local_context)
        merged_context = await self.merge_contexts(conflict_resolution)
        await self.broadcast_updates(merged_context, exclude=agent_id)

AGENT-SPECIFIC OPTIMIZATION RULES:

Code Generation Agents:
- Always include Python type hints and comprehensive docstrings
- Implement ORFEAS error handling patterns with automated problem detection
- Include GPU memory management for AI model operations
- Add Prometheus metrics and monitoring integration
- Follow enterprise security patterns with input validation
Testing & QA Agents:
- Generate pytest test suites with 80%+ code coverage
- Include security vulnerability tests and penetration testing scenarios
- Create performance benchmarks and load testing scripts
- Implement TQM audit procedures and quality gates
- Add integration tests for multi-agent coordination
Infrastructure & DevOps Agents:
- Use enterprise Kubernetes deployment patterns with auto-scaling
- Implement zero-downtime deployment strategies
- Configure comprehensive monitoring with Prometheus + Grafana
- Set up enterprise security with SSL/TLS and zero-trust architecture
- Enable disaster recovery and backup automation
Security & Compliance Agents:
- Implement comprehensive input validation and sanitization
- Add enterprise authentication (OAuth2, SAML, LDAP)
- Configure audit logging and compliance tracking
- Perform automated security scanning and vulnerability assessment
- Ensure GDPR, HIPAA, SOC2 compliance requirements

COLLABORATIVE WORKFLOW PATTERNS:

# Multi-agent feature development coordination
async def coordinate_feature_development(feature_spec: Dict) -> Dict:
    """Coordinate multi-agent development workflow"""

    # 1. Primary analysis and task decomposition
    analysis = await orfeas_specialist.analyze_feature_requirements(feature_spec)

    # 2. Intelligent task distribution
    task_assignments = {
        'backend_api': await backend_agent.estimate_effort(analysis['api_requirements']),
        'frontend_ui': await frontend_agent.estimate_effort(analysis['ui_requirements']),
        'security_review': await security_agent.assess_security_impact(analysis),
        'performance_impact': await performance_agent.analyze_performance_impact(analysis),
        'infrastructure_changes': await devops_agent.assess_deployment_impact(analysis)
    }

    # 3. Parallel execution with context sharing
    execution_results = await asyncio.gather(*[
        backend_agent.implement_api(task_assignments['backend_api'], shared_context),
        frontend_agent.implement_ui(task_assignments['frontend_ui'], shared_context),
        security_agent.perform_security_review(task_assignments['security_review'], shared_context),
        performance_agent.optimize_performance(task_assignments['performance_impact'], shared_context),
        devops_agent.prepare_deployment(task_assignments['infrastructure_changes'], shared_context)
    ])

    # 4. Integration and validation
    integrated_feature = await integration_agent.merge_implementations(execution_results)
    validation_results = await qa_agent.validate_feature(integrated_feature, feature_spec)

    # 5. Quality gates and deployment readiness
    if validation_results['quality_score'] >= 0.85:
        deployment_package = await devops_agent.prepare_deployment_package(integrated_feature)
        return {
            'status': 'ready_for_deployment',
            'feature': integrated_feature,
            'deployment_package': deployment_package,
            'quality_metrics': validation_results
        }
    else:
        improvement_plan = await qa_agent.generate_improvement_plan(validation_results)
        return {
            'status': 'requires_improvement',
            'improvement_plan': improvement_plan,
            'quality_issues': validation_results['issues']
        }

# Agent performance optimization
class MultiAgentPerformanceOptimizer:
    def __init__(self):
        self.agent_pool = AgentConnectionPool(max_connections=50)
        self.load_balancer = AgentLoadBalancer()
        self.circuit_breaker = AgentCircuitBreaker()
        self.cache_manager = AgentCacheManager()

    async def optimize_agent_communication(self):
        """Optimize inter-agent communication performance"""
        # Implement intelligent caching
        await self.cache_manager.enable_result_caching()

        # Use connection pooling
        await self.agent_pool.initialize_connections()

        # Apply load balancing
        await self.load_balancer.distribute_agent_load()

        # Enable circuit breakers for resilience
        await self.circuit_breaker.monitor_agent_health()

AGENT DEPLOYMENT & SCALING PATTERNS:

# Kubernetes multi-agent deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: orfeas-agent-cluster
spec:
  replicas: 5
  selector:
    matchLabels:
      app: orfeas-agents
  template:
    metadata:
      labels:
        app: orfeas-agents
    spec:
      containers:
        - name: orfeas-specialist
          image: orfeas/specialist-agent:latest
          resources:
            requests:
              memory: "2Gi"
              cpu: "1000m"
              nvidia.com/gpu: 1
            limits:
              memory: "4Gi"
              cpu: "2000m"
              nvidia.com/gpu: 1
        - name: backend-agent
          image: orfeas/backend-agent:latest
          resources:
            requests:
              memory: "1Gi"
              cpu: "500m"
        - name: frontend-agent
          image: orfeas/frontend-agent:latest
          resources:
            requests:
              memory: "512Mi"
              cpu: "250m"

PROJECT STRUCTURE:

orfeas/
â"œâ"€â"€ backend/                          # Python Flask API server
â"'   â"œâ"€â"€ main.py                       # Primary Flask application (2400+ lines)
â"'   â"œâ"€â"€ hunyuan_integration.py        # Hunyuan3D-2.1 model interface
â"'   â"œâ"€â"€ sora_video_integration.py     # Sora-inspired video composition AI
â"'   â"œâ"€â"€ video_processor.py            # AI video generation and processing
â"'   â"œâ"€â"€ cinematic_composer.py         # Cinematic video composition engine
â"'   â"œâ"€â"€ text_to_image_processor.py    # AI text-to-image generation (DALL-E, SDXL)
â"'   â"œâ"€â"€ text_to_speech_processor.py   # AI text-to-speech synthesis (ElevenLabs, Azure)
â"'   â"œâ"€â"€ speech_to_text_processor.py   # AI speech-to-text recognition (Whisper, Azure)
â"'   â"œâ"€â"€ multimedia_ai_manager.py      # Unified multimedia AI coordination
â"'   â"œâ"€â"€ audio_enhancement.py          # Audio quality enhancement and processing
â"'   â"œâ"€â"€ image_enhancement.py          # Image quality enhancement and upscaling
â"'   â"œâ"€â"€ code_writer.py                # AI-powered intelligent code generation
â"'   â"œâ"€â"€ code_debugger.py              # Automated code debugging and optimization
â"'   â"œâ"€â"€ code_analyzer.py              # Code quality analysis and suggestions
â"'   â"œâ"€â"€ agent_api.py                  # AI agent orchestration and workflows
â"'   â"œâ"€â"€ agent_auth.py                 # Authentication for autonomous agents
â"'   â"œâ"€â"€ gpu_manager.py                # GPU memory and resource management
â"'   â"œâ"€â"€ rtx_optimization.py           # RTX 3090 specific optimizations
â"'   â"œâ"€â"€ stl_processor.py              # Advanced STL mesh processing
â"'   â"œâ"€â"€ batch_processor.py            # Async job queue for concurrent tasks
â"'   â"œâ"€â"€ batch_inference_extension.py  # ML batch processing extensions
â"'   â"œâ"€â"€ huggingface_compat.py         # HuggingFace model compatibility layer
â"'   â"œâ"€â"€ ssl_manager.py                # SSL certificate management and HTTPS
â"'   â"œâ"€â"€ web_security.py               # Web security headers and CSP policies
â"'   â"œâ"€â"€ pwa_manager.py                # Progressive Web App functionality
â"'   â"œâ"€â"€ context_manager.py            # Intelligent context handling and analysis
â"'   â"œâ"€â"€ contextual_agent_coordinator.py # Multi-agent context coordination
â"'   â"œâ"€â"€ contextual_model_selector.py  # Context-aware model selection
â"'   â"œâ"€â"€ contextual_error_handler.py   # Context-based error recovery
â"'   â"œâ"€â"€ context_persistence.py        # Context learning and persistence
â"'   â"œâ"€â"€ tqm_audit_system.py          # Total Quality Management audit system
â"'   â"œâ"€â"€ continuous_quality_monitor.py # Real-time quality monitoring and improvement
â"'   â"œâ"€â"€ automated_audit_scheduler.py  # Automated quality audit scheduling
â"'   â"œâ"€â"€ quality_gateway_middleware.py # Quality gate enforcement middleware
â"'   â"œâ"€â"€ quality_metrics_collector.py  # Comprehensive quality metrics collection
â"'   â"œâ"€â"€ compliance_validator.py       # Standards compliance validation (ISO, SOC2, etc)
â"'   â"œâ"€â"€ quality_improvement_engine.py # Automated quality improvement actions
â"'   â"œâ"€â"€ llm_integration.py            # Enterprise LLM orchestration and management
â"'   â"œâ"€â"€ copilot_enterprise.py         # GitHub Copilot Enterprise integration
â"'   â"œâ"€â"€ llm_router.py                 # Intelligent LLM routing and selection
â"'   â"œâ"€â"€ multi_llm_orchestrator.py     # Multi-LLM task coordination
â"'   â"œâ"€â"€ rag_integration.py            # Retrieval-Augmented Generation system
â"'   â"œâ"€â"€ vector_database_manager.py    # Vector database operations (Pinecone, etc)
â"'   â"œâ"€â"€ knowledge_graph_manager.py    # Knowledge graph integration (Neo4j)
â"'   â"œâ"€â"€ prompt_engineering.py         # Advanced prompt optimization
â"'   â"œâ"€â"€ llm_performance_monitor.py    # LLM performance tracking and optimization
â"'   â"œâ"€â"€ llm_safety_filter.py          # Enterprise LLM safety and content filtering
â"'   â"œâ"€â"€ encoding_manager.py           # Multi-character encoding detection and conversion
â"'   â"œâ"€â"€ encoding_auto_detector.py     # Automatic encoding detection with fallback chains
â"'   â"œâ"€â"€ unicode_normalizer.py         # Unicode normalization and BOM handling
â"'   â"œâ"€â"€ i18n_manager.py               # Internationalization and localization manager
â"'   â"œâ"€â"€ language_detector.py          # Automatic language detection from text
â"'   â"œâ"€â"€ multilingual_processor.py     # Multi-language text processing and NLP
â"'   â"œâ"€â"€ polyglot_code_manager.py      # Multi-programming language code management
â"'   â"œâ"€â"€ code_language_detector.py     # Automatic programming language detection
â"'   â"œâ"€â"€ cross_language_translator.py  # Code translation between programming languages
â"'   â"œâ"€â"€ language_interop_engine.py    # Cross-language interoperability and integration
â"'   â"œâ"€â"€ syntax_validator_multi.py     # Multi-language syntax validation engine
â"'   â"œâ"€â"€ lsp_integration_manager.py    # Language Server Protocol integration
â"'   â"œâ"€â"€ tree_sitter_parser.py         # Universal syntax highlighting and parsing
â"'   â"œâ"€â"€ validation.py                 # Input validation and security
   ├ monitoring.py                 # Application metrics collection
   ├ prometheus_metrics.py         # Prometheus exporter
   ├ config.py                     # Configuration management
   ├ tests/                        # pytest test suite
   └ monitoring_stack/             # Docker monitoring infrastructure
       ├ docker-compose-monitoring.yml
       ├ prometheus.yml
       └ grafana-dashboard.json
├ Hunyuan3D-2.1/                    # Tencent's AI model (Git submodule)
   └ Hunyuan3D-2/
       └ hy3dgen/                  # Core generation pipelines
├ orfeas-studio.html                # Main web interface
├ orfeas-3d-engine-hybrid.js        # 3D viewer (Three.js/BabylonJS)
├ service-worker.js                 # PWA offline support
├ docker-compose.yml                # Multi-service orchestration
├ Dockerfile                        # Production container build
├ START_ORFEAS_AUTO.ps1             # Windows startup script
└ md/                               # Documentation directory

[2] CORE ARCHITECTURE PRINCIPLES

[2.1] HUNYUAN3D-2.1 INTEGRATION

CRITICAL MODEL LOADING:

First initialization: 30-36 seconds (loads 8GB+ models to GPU)
Singleton caching: Subsequent loads <1s (94% faster via
```
_model_cache
```
)
XFORMERS DISABLED:
```
os.environ["XFORMERS_DISABLED"] = "1"
```
prevents DLL crashes (0xc0000139)

Models:

shapegen_pipeline

texgen_pipeline

rembg

text2image_pipeline

CORRECT INITIALIZATION PATTERN:

# backend/hunyuan_integration.py
class Hunyuan3DProcessor:
    _model_cache = {'shapegen_pipeline': None, 'texgen_pipeline': None, 'initialized': False}
    _cache_lock = threading.Lock()

    def __init__(self, device=None):
        with Hunyuan3DProcessor._cache_lock:
            if Hunyuan3DProcessor._model_cache['initialized']:
                # 🔥 INSTANT: Use cached models (~1s)
                self.shapegen_pipeline = Hunyuan3DProcessor._model_cache['shapegen_pipeline']
                return
        # First time: Load models from Hunyuan3D-2.1 directory (30-36s)
        self.initialize_model()

GENERATION WORKFLOW:

# 1. Background removal (rembg)
processed_image = processor.rembg.remove_background(image)

# 2. Shape generation (Hunyuan3D-DiT-v2-1)
mesh = processor.shapegen_pipeline(image=processed_image, num_inference_steps=50)

# 3. Texture synthesis (Hunyuan3D-Paint-v2-1)
textured_mesh = processor.texgen_pipeline(mesh=mesh, image=processed_image)

# 4. Export to STL/OBJ/GLB
mesh.export('output.stl')

[2.1.1] AI VIDEO COMPOSITION (SORA-INSPIRED)

CINEMATIC VIDEO GENERATION:

The ORFEAS platform includes advanced AI video composition capabilities inspired by OpenAI's Sora, enabling the creation of cinematic video content from text prompts, images, or 3D models.

VIDEO PROCESSING PIPELINE:

# backend/sora_video_integration.py
class SoraVideoProcessor:
    """
    Sora-inspired video composition engine for cinematic content generation
    """

    def __init__(self, device=None):
        self.device = device or "cuda" if torch.cuda.is_available() else "cpu"
        self.video_models = {
            'text_to_video': None,
            'image_to_video': None,
            '3d_to_video': None,
            'style_transfer': None
        }
        self.motion_models = {
            'camera_motion': None,
            'object_motion': None,
            'lighting_dynamics': None
        }

    def generate_cinematic_video(self, input_data: Dict) -> Dict:
        """Generate cinematic video from various inputs"""

        # 1. Analyze input content
        content_analysis = self.analyze_content(input_data)

        # 2. Generate motion sequences
        motion_sequence = self.generate_motion_sequence(content_analysis)

        # 3. Apply cinematic techniques
        cinematic_frames = self.apply_cinematic_techniques(motion_sequence)

        # 4. Render final video
        video_output = self.render_video(cinematic_frames)

        return video_output

VIDEO GENERATION WORKFLOWS:

# 1. Text-to-Video Generation
video = processor.text_to_video(
    prompt="A futuristic city with flying cars at sunset",
    duration=10,  # seconds
    fps=24,
    resolution=(1920, 1080),
    style="cinematic"
)

# 2. 3D Model to Video Animation
video = processor.model_to_video(
    model_path="generated_model.stl",
    animation_type="turntable",
    lighting="studio",
    background="gradient"
)

# 3. Image to Video Expansion
video = processor.image_to_video(
    image="input.jpg",
    motion_type="camera_zoom",
    depth_estimation=True,
    parallax_effect=True
)

CINEMATIC FEATURES:

Camera Movements: Dolly, pan, tilt, zoom, orbit, tracking shots
Lighting Dynamics: Dynamic lighting, shadow animation, color grading
Motion Blur: Realistic motion blur and depth of field effects
Temporal Consistency: Frame-to-frame coherence and smooth transitions
Style Transfer: Apply artistic styles across video sequences
3D Integration: Seamless integration with generated 3D models

[2.1.2] AI CODE DEVELOPMENT & DEBUGGING

INTELLIGENT CODE GENERATION:

The ORFEAS platform includes advanced AI-powered code development capabilities for intelligent code writing, automated debugging, and code quality optimization.

CODE DEVELOPMENT PIPELINE:

# backend/code_writer.py
class IntelligentCodeWriter:
    """
    AI-powered code generation and development assistant
    """

    def __init__(self, model="github-copilot"):
        self.model = model
        self.code_models = {
            'github-copilot': None,
            'codeT5': None,
            'starcoder': None,
            'deepseek-coder': None
        }
        self.quality_analyzer = CodeQualityAnalyzer()

    def generate_code(self, prompt: str, language: str, context: Dict = None) -> Dict:
        """Generate code from natural language description"""

        # 1. Analyze requirements
        requirements = self.analyze_requirements(prompt, context)

        # 2. Generate code structure
        code_structure = self.generate_code_structure(requirements, language)

        # 3. Generate implementation
        code_implementation = self.generate_implementation(code_structure, requirements)

        # 4. Apply code optimization
        optimized_code = self.optimize_code(code_implementation, language)

        return {
            'code': optimized_code,
            'quality_score': self.quality_analyzer.analyze(optimized_code),
            'documentation': self.generate_documentation(optimized_code),
            'tests': self.generate_tests(optimized_code) if context.get('generate_tests') else None
        }

CODE DEBUGGING WORKFLOW:

# backend/code_debugger.py
class AutomatedCodeDebugger:
    """
    AI-powered code debugging and error resolution
    """

    def __init__(self):
        self.error_patterns = self.load_error_patterns()
        self.fix_strategies = self.load_fix_strategies()

    def debug_code(self, code: str, error_trace: str, language: str) -> Dict:
        """Automatically debug and fix code issues"""

        # 1. Analyze error
        error_analysis = self.analyze_error(error_trace, code)

        # 2. Identify root cause
        root_cause = self.identify_root_cause(error_analysis, code)

        # 3. Generate fix
        fix_suggestion = self.generate_fix(root_cause, code, language)

        # 4. Validate fix
        validated_fix = self.validate_fix(fix_suggestion, code)

        return {
            'error_type': error_analysis['type'],
            'root_cause': root_cause,
            'fix_suggestion': validated_fix,
            'confidence': error_analysis['confidence'],
            'explanation': self.generate_explanation(root_cause, validated_fix)
        }

CODE DEVELOPMENT FEATURES:

Natural Language to Code: Generate code from plain English descriptions
Code Completion: Intelligent autocomplete and suggestion system
Bug Detection: Automated identification of potential issues
Code Optimization: Performance and quality improvements
Test Generation: Automatic unit test creation
Documentation: Auto-generated code documentation
Code Review: AI-powered code review and suggestions
Refactoring: Intelligent code restructuring and improvement

[2.2] GPU MEMORY MANAGEMENT

RTX 3090 OPTIMIZATION (24GB VRAM):

Model cache: ~8GB (persistent)
Active generation: ~6-10GB
Safety margin: 80% limit via
```
GPU_MEMORY_LIMIT=0.8
```
Auto-cleanup: torch.cuda.empty_cache() after each generation

CORRECT GPU MANAGER USAGE:

# backend/gpu_manager.py
gpu_mgr = get_gpu_manager()

if gpu_mgr.can_process_job(estimated_vram=6000):  # 6GB estimate
    with gpu_mgr.allocate_job(job_id):
        result = generate_3d_model(image)
        gpu_mgr.cleanup_after_job()  # Force CUDA cache clear
else:
    # Queue job for later or reject
    raise ResourceError("GPU memory insufficient")

NEVER:

Load models without checking available VRAM
Skip
```
torch.cuda.empty_cache()
```
between generations
Process concurrent jobs without queue management

[2.3] FLASK API ARCHITECTURE

ENDPOINT PATTERN:

@app.route('/api/generate-3d', methods=['POST'])
@track_request_metrics('generate_3d')  # Prometheus tracking
@security_headers  # CORS and security headers
def generate_3d():
    """
    Text/Image → 3D STL generation

    Request: multipart/form-data with 'image' file or 'prompt' text
    Response: Binary STL file or JSON with job_id for async processing
    Metrics: Tracked via prometheus_metrics.py
    """
    # 1. Validate input (validation.py)
    validator = FileUploadValidator()
    image = validator.validate_image(request.files.get('image'))

    # 2. Check rate limits
    if not get_rate_limiter().check_limit(request.remote_addr):
        return jsonify({'error': 'Rate limit exceeded'}), 429

    # 3. Queue job (batch_processor.py)
    job = async_queue.submit_job('3d_generation', image=image)

    # 4. Return job ID or wait for completion
    return jsonify({'job_id': job.id, 'status': 'processing'})

WEBSOCKET PROGRESS UPDATES:

# Use Flask-SocketIO for real-time progress
socketio.emit('generation_progress', {
    'job_id': job_id,
    'stage': 'shape_generation',
    'progress': 45,
    'eta_seconds': 20
}, room=client_sid)

[2.4] STL PROCESSING PIPELINE

ADVANCED MESH OPERATIONS:

# backend/stl_processor.py
processor = AdvancedSTLProcessor()

# Analysis
stats = processor.analyze_stl(mesh_path)
# Returns: triangle_count, surface_area, volume, bounds, manifold_status

# Repair non-manifold geometry
repaired_mesh = processor.repair_stl(mesh)

# Optimize for 3D printing
optimized_mesh = processor.optimize_stl_for_printing(
    mesh,
    target_size_mm=100,
    wall_thickness_mm=2.0,
    supports=True
)

3D EXPORT FORMATS:

STL: Binary format (production default)
OBJ: With material .mtl files
GLB: Embedded textures for web
PLY: Point cloud or mesh with colors

[2.4.1] HTML5 FRONTEND ARCHITECTURE

PROGRESSIVE WEB APP (PWA) FEATURES:

# backend/pwa_manager.py
class PWAManager:
    """
    Progressive Web App functionality manager
    """

    def __init__(self):
        # Define color constants using RGB values to avoid parser issues
        THEME_COLOR = "rgb(30, 64, 175)"  # Blue theme
        BACKGROUND_COLOR = "rgb(255, 255, 255)"  # White background

        self.manifest_config = {
            "name": "ORFEAS AI 3D Studio",
            "short_name": "ORFEAS",
            "description": "AI-powered 2D to 3D model generation",
            "start_url": "/",
            "display": "standalone",
            "theme_color": THEME_COLOR,
            "background_color": BACKGROUND_COLOR
        }    def generate_manifest(self):
        """Generate manifest.json for PWA installation"""
        return json.dumps(self.manifest_config, indent=2)

    def register_service_worker(self):
        """Setup service worker for offline functionality"""
        return """
        if ('serviceWorker' in navigator) {
            navigator.serviceWorker.register('/service-worker.js')
                .then(() => console.log('SW registered'))
                .catch(() => console.log('SW registration failed'));
        }
        """

HTML5 FEATURES INTEGRATION:

<!-- orfeas-studio.html - Main interface -->
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <meta name="theme-color" content="rgb(30, 64, 175)" />
    <link rel="manifest" href="/manifest.json" />
    <link rel="icon" href="/static/icons/icon-192x192.png" />
    <!-- PWA iOS meta tags -->
    <meta name="apple-mobile-web-app-capable" content="yes" />
    <meta
      name="apple-mobile-web-app-status-bar-style"
      content="black-translucent"
    />
    <link rel="apple-touch-icon" href="/static/icons/icon-192x192.png" />
  </head>
</html>

[2.4.2] SSL/TLS SECURITY IMPLEMENTATION

SSL CERTIFICATE MANAGEMENT:

# backend/ssl_manager.py
class SSLManager:
    """
    SSL certificate management and HTTPS enforcement
    """

    def __init__(self):
        self.cert_path = "ssl/orfeas.crt"
        self.key_path = "ssl/orfeas.key"
        self.lets_encrypt_path = "ssl/lets-encrypt/"

    def enforce_https(self, app):
        """Force HTTPS redirects for all requests"""
        @app.before_request
        def force_https():
            if not request.is_secure and app.env != 'development':
                return redirect(request.url.replace('http://', 'https://'), 301)

    def setup_ssl_context(self):
        """Configure SSL context for Flask application"""
        context = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)
        context.load_cert_chain(self.cert_path, self.key_path)
        return context

    def auto_renew_certificates(self):
        """Auto-renewal setup for Let's Encrypt certificates"""
        # Certbot integration for automatic renewal
        return subprocess.run([
            'certbot', 'renew', '--quiet',
            '--deploy-hook', 'systemctl reload nginx'
        ])

WEB SECURITY HEADERS:

# backend/web_security.py
class WebSecurityManager:
    """
    Web security headers and Content Security Policy
    """

    def __init__(self):
        self.security_headers = {
            'Strict-Transport-Security': 'max-age=31536000; includeSubDomains',
            'X-Content-Type-Options': 'nosniff',
            'X-Frame-Options': 'DENY',
            'X-XSS-Protection': '1; mode=block',
            'Referrer-Policy': 'strict-origin-when-cross-origin'
        }

    def apply_security_headers(self, app):
        """Apply security headers to all responses"""
        @app.after_request
        def set_security_headers(response):
            for header, value in self.security_headers.items():
                response.headers[header] = value
            return response

    def setup_csp(self):
        """Content Security Policy configuration"""
        csp_policy = (
            "default-src 'self'; "
            "script-src 'self' 'unsafe-inline' https://cdnjs.cloudflare.com; "
            "style-src 'self' 'unsafe-inline'; "
            "img-src 'self' data: blob:; "
            "connect-src 'self' wss: ws:; "
            "worker-src 'self' blob:;"
        )
        return {'Content-Security-Policy': csp_policy}

[2.4.3] PHP COMPATIBILITY LAYER

PHP INTEGRATION SUPPORT:

<?php
// php/api.php - PHP API endpoints for legacy systems
class OrfeasPHPAPI {
    private $python_api_url = 'http://localhost:5000/api';

    public function generate3D($image_data, $quality = 7) {
        // Proxy requests to Python backend
        $curl = curl_init();
        curl_setopt_array($curl, [
            CURLOPT_URL => $this->python_api_url . '/generate-3d',
            CURLOPT_RETURNTRANSFER => true,
            CURLOPT_POST => true,
            CURLOPT_POSTFIELDS => [
                'image' => new CURLFile($image_data),
                'quality' => $quality
            ],
            CURLOPT_HTTPHEADER => [
                'Authorization: Bearer ' . $_ENV['API_TOKEN'],
                'Content-Type: multipart/form-data'
            ]
        ]);

        $response = curl_exec($curl);
        curl_close($curl);

        return json_decode($response, true);
    }

    public function checkJobStatus($job_id) {
        // Check generation job status
        $curl = curl_init();
        curl_setopt_array($curl, [
            CURLOPT_URL => $this->python_api_url . '/job-status/' . $job_id,
            CURLOPT_RETURNTRANSFER => true
        ]);

        $response = curl_exec($curl);
        curl_close($curl);

        return json_decode($response, true);
    }
}
?>

[2.5] AI AGENTS & AUTONOMOUS WORKFLOWS

AI AGENT ARCHITECTURE:

The ORFEAS platform includes autonomous AI agents for workflow automation, quality assurance, and intelligent processing decisions.

# backend/agent_api.py - Core agent framework
class OrfeasAIAgent:
    """
    Autonomous AI agent for workflow orchestration

    Features:
    - Intelligent model selection based on input analysis
    - Quality assessment and optimization recommendations
    - Automated error recovery and fallback strategies
    - Performance monitoring and optimization
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        self.agent_type = agent_type  # 'quality_agent', 'workflow_agent', 'optimization_agent'
        self.capabilities = capabilities
        self.decision_tree = self.load_decision_model()

    async def analyze_input(self, input_data: Dict) -> Dict[str, Any]:
        """Analyze input and recommend processing strategy"""
        analysis = {
            'input_type': self.classify_input(input_data),
            'complexity_score': self.assess_complexity(input_data),
            'recommended_pipeline': self.select_pipeline(input_data),
            'estimated_resources': self.estimate_resources(input_data)
        }
        return analysis

    def select_optimal_model(self, analysis: Dict) -> str:
        """Select best AI model based on input analysis"""
        if analysis['complexity_score'] > 0.8:
            return 'hunyuan3d_high_quality'
        elif analysis['input_type'] == 'simple_object':
            return 'hunyuan3d_fast'
        else:
            return 'hunyuan3d_balanced'

AGENT TYPES:

Quality Assessment Agent
- Analyzes input image quality and complexity
- Recommends preprocessing steps
- Predicts generation success probability
- Suggests quality improvements
Workflow Orchestration Agent
- Manages multi-step generation pipelines
- Handles error recovery and retries
- Optimizes resource allocation
- Coordinates between different AI models
Performance Optimization Agent
- Monitors GPU usage and performance
- Adjusts generation parameters for optimal speed/quality
- Manages model caching and memory optimization
- Provides performance recommendations

AGENT AUTHENTICATION & SECURITY:

# backend/agent_auth.py
class AgentAuthManager:
    """
    Secure authentication for autonomous agents
    """

    def __init__(self):
        self.agent_keys = self.load_agent_credentials()
        self.permission_matrix = self.load_permissions()

    def authenticate_agent(self, agent_id: str, api_key: str) -> bool:
        """Verify agent credentials and permissions"""
        if agent_id not in self.agent_keys:
            return False
        return self.verify_key(agent_id, api_key)

    def check_permissions(self, agent_id: str, action: str) -> bool:
        """Check if agent has permission for specific action"""
        return action in self.permission_matrix.get(agent_id, [])

[2.6] MACHINE LEARNING OPTIMIZATION

MODEL PERFORMANCE OPTIMIZATION:

# backend/ml_optimizer.py
class MLPerformanceOptimizer:
    """
    Advanced ML optimization for production inference
    """

    def __init__(self):
        self.optimization_strategies = {
            'tensorrt': self.apply_tensorrt_optimization,
            'quantization': self.apply_quantization,
            'mixed_precision': self.enable_mixed_precision,
            'model_pruning': self.apply_model_pruning
        }

    def optimize_model_for_inference(self, model, strategy: str):
        """Apply production optimizations to ML models"""
        if strategy == 'tensorrt':
            # Convert PyTorch model to TensorRT for 2-5x speedup
            optimized_model = self.convert_to_tensorrt(model)
        elif strategy == 'quantization':
            # INT8 quantization for 4x memory reduction
            optimized_model = torch.quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
        return optimized_model

    def enable_mixed_precision(self, model):
        """Enable FP16/BF16 mixed precision for 40% speedup"""
        model = model.half()  # Convert to FP16
        torch.backends.cudnn.benchmark = True
        return model

BATCH INFERENCE EXTENSIONS:

# backend/batch_inference_extension.py
class BatchInferenceManager:
    """
    Advanced batch processing for ML workloads
    """

    def __init__(self, max_batch_size: int = 4):
        self.max_batch_size = max_batch_size
        self.batch_queue = asyncio.Queue()
        self.processing_strategies = {
            'dynamic_batching': self.dynamic_batch_processing,
            'pipeline_parallel': self.pipeline_parallel_processing,
            'model_parallel': self.model_parallel_processing
        }

    async def dynamic_batch_processing(self, requests: List[Dict]):
        """Group requests into optimal batch sizes"""
        batches = []
        current_batch = []

        for request in requests:
            if len(current_batch) >= self.max_batch_size:
                batches.append(current_batch)
                current_batch = [request]
            else:
                current_batch.append(request)

        if current_batch:
            batches.append(current_batch)

        results = []
        for batch in batches:
            batch_result = await self.process_batch(batch)
            results.extend(batch_result)

        return results

HUGGINGFACE COMPATIBILITY LAYER:

# backend/huggingface_compat.py
class HuggingFaceModelManager:
    """
    Integration layer for HuggingFace models and pipelines
    """

    def __init__(self):
        self.model_cache = {}
        self.supported_models = {
            'stable_diffusion': 'runwayml/stable-diffusion-v1-5',
            'clip': 'openai/clip-vit-base-patch32',
            'controlnet': 'lllyasviel/sd-controlnet-canny',
            'instant_mesh': 'TencentARC/InstantMesh'
        }

    def load_huggingface_model(self, model_name: str, optimization: str = 'none'):
        """Load and optimize HuggingFace models"""
        if model_name in self.model_cache:
            return self.model_cache[model_name]

        if model_name not in self.supported_models:
            raise ValueError(f"Unsupported model: {model_name}")

        model_id = self.supported_models[model_name]

        if optimization == 'tensorrt':
            model = self.load_with_tensorrt(model_id)
        elif optimization == 'onnx':
            model = self.load_with_onnx(model_id)
        else:
            model = transformers.pipeline('text-to-image', model=model_id)

        self.model_cache[model_name] = model
        return model

    def fallback_generation(self, prompt: str, fallback_model: str = 'stable_diffusion'):
        """Fallback generation when primary model fails"""
        try:
            model = self.load_huggingface_model(fallback_model)
            result = model(prompt)
            return result
        except Exception as e:
            logger.error(f"Fallback generation failed: {e}")
            raise RuntimeError("All generation methods failed")

[2.7] INTELLIGENT CONTEXT HANDLING

CONTEXT-AWARE AI PROCESSING:

The ORFEAS platform implements intelligent context handling to enhance AI decision-making, optimize processing workflows, and provide contextual awareness across all components.

# backend/context_manager.py
class IntelligentContextManager:
    """
    Advanced context handling for AI-driven decision making
    """

    def __init__(self):
        self.context_store = {}
        self.session_contexts = {}
        self.global_context = {
            'system_performance': {},
            'user_preferences': {},
            'model_performance_history': {},
            'resource_availability': {}
        }

    def build_processing_context(self, request_data: Dict) -> Dict[str, Any]:
        """Build comprehensive context for AI processing decisions"""
        context = {
            'input_analysis': self.analyze_input_characteristics(request_data),
            'user_context': self.get_user_context(request_data.get('user_id')),
            'system_context': self.get_system_context(),
            'historical_context': self.get_historical_performance(),
            'resource_context': self.get_resource_availability(),
            'quality_context': self.get_quality_requirements(request_data)
        }
        return context

    def analyze_input_characteristics(self, request_data: Dict) -> Dict[str, Any]:
        """Analyze input to determine processing requirements"""
        analysis = {
            'input_type': self.classify_input_type(request_data),
            'complexity_score': self.calculate_complexity(request_data),
            'quality_requirements': self.extract_quality_requirements(request_data),
            'processing_priority': self.determine_priority(request_data),
            'estimated_resources': self.estimate_resource_needs(request_data)
        }
        return analysis

    def get_contextual_recommendations(self, context: Dict) -> Dict[str, Any]:
        """Generate intelligent recommendations based on context"""
        recommendations = {
            'optimal_model': self.recommend_model(context),
            'processing_parameters': self.recommend_parameters(context),
            'quality_settings': self.recommend_quality_settings(context),
            'resource_allocation': self.recommend_resources(context),
            'fallback_strategies': self.recommend_fallbacks(context)
        }
        return recommendations

CONTEXTUAL AI AGENT COORDINATION:

# backend/contextual_agent_coordinator.py
class ContextualAgentCoordinator:
    """
    Coordinates AI agents with intelligent context sharing
    """

    def __init__(self, context_manager: IntelligentContextManager):
        self.context_manager = context_manager
        self.agent_registry = {}
        self.context_sharing_graph = {}

    async def coordinate_with_context(self, task: Dict, agents: List[str]) -> Dict:
        """Coordinate multiple agents with shared context"""
        # Build comprehensive context
        context = self.context_manager.build_processing_context(task)

        # Share relevant context with each agent
        agent_contexts = {}
        for agent_id in agents:
            agent_contexts[agent_id] = self.filter_context_for_agent(context, agent_id)

        # Execute coordinated workflow
        results = {}
        for agent_id in agents:
            agent = await self.get_agent(agent_id)
            results[agent_id] = await agent.execute_with_context(
                task, agent_contexts[agent_id]
            )

            # Update shared context with results
            self.update_shared_context(agent_id, results[agent_id], context)

        return self.synthesize_results(results, context)

    def filter_context_for_agent(self, context: Dict, agent_id: str) -> Dict:
        """Filter context to relevant information for specific agent"""
        agent_type = self.agent_registry[agent_id]['type']

        if agent_type == 'quality_agent':
            return {
                'input_analysis': context['input_analysis'],
                'quality_context': context['quality_context'],
                'historical_context': context['historical_context']
            }
        elif agent_type == 'workflow_agent':
            return {
                'system_context': context['system_context'],
                'resource_context': context['resource_context'],
                'input_analysis': context['input_analysis']
            }
        elif agent_type == 'optimization_agent':
            return {
                'system_context': context['system_context'],
                'resource_context': context['resource_context'],
                'historical_context': context['historical_context']
            }
        else:
            return context  # Full context for unknown agent types

CONTEXT-AWARE MODEL SELECTION:

# backend/contextual_model_selector.py
class ContextualModelSelector:
    """
    Intelligent model selection based on comprehensive context analysis
    """

    def __init__(self):
        self.model_performance_matrix = {}
        self.context_model_mapping = {}
        self.learning_history = {}

    def select_optimal_model(self, context: Dict) -> Dict[str, Any]:
        """Select best model based on context analysis"""
        # Analyze context dimensions
        context_signature = self.generate_context_signature(context)

        # Check for similar historical contexts
        similar_contexts = self.find_similar_contexts(context_signature)

        # Calculate model scores based on context
        model_scores = {}
        for model_name in self.available_models:
            score = self.calculate_model_score(model_name, context, similar_contexts)
            model_scores[model_name] = score

        # Select best model
        optimal_model = max(model_scores, key=model_scores.get)

        # Generate selection reasoning
        selection_reasoning = self.generate_selection_reasoning(
            optimal_model, context, model_scores
        )

        return {
            'selected_model': optimal_model,
            'confidence_score': model_scores[optimal_model],
            'reasoning': selection_reasoning,
            'fallback_models': self.get_fallback_sequence(model_scores),
            'context_signature': context_signature
        }

    def generate_context_signature(self, context: Dict) -> str:
        """Generate unique signature for context matching"""
        key_features = [
            context['input_analysis']['complexity_score'],
            context['quality_context']['target_quality'],
            context['resource_context']['available_vram'],
            context['system_context']['current_load']
        ]
        return hashlib.md5(str(key_features).encode()).hexdigest()[:16]

    def learn_from_result(self, context: Dict, model: str, result: Dict):
        """Update model performance based on actual results"""
        context_sig = self.generate_context_signature(context)

        if context_sig not in self.learning_history:
            self.learning_history[context_sig] = {}

        self.learning_history[context_sig][model] = {
            'quality_score': result.get('quality_score', 0),
            'processing_time': result.get('processing_time', 0),
            'resource_usage': result.get('resource_usage', {}),
            'success': result.get('success', False),
            'timestamp': time.time()
        }

CONTEXTUAL ERROR HANDLING & RECOVERY:

# backend/contextual_error_handler.py
class ContextualErrorHandler:
    """
    Context-aware error handling and recovery strategies
    """

    def __init__(self, context_manager: IntelligentContextManager):
        self.context_manager = context_manager
        self.error_patterns = {}
        self.recovery_strategies = {}

    async def handle_error_with_context(self, error: Exception, context: Dict) -> Dict:
        """Handle errors using contextual information for smart recovery"""
        # Analyze error in context
        error_analysis = self.analyze_error_context(error, context)

        # Determine recovery strategy
        recovery_strategy = self.select_recovery_strategy(error_analysis, context)

        # Execute recovery
        recovery_result = await self.execute_recovery(recovery_strategy, context)

        # Learn from the error and recovery
        self.learn_from_error_recovery(error, context, recovery_strategy, recovery_result)

        return recovery_result

    def analyze_error_context(self, error: Exception, context: Dict) -> Dict:
        """Analyze error within the broader context"""
        return {
            'error_type': type(error).__name__,
            'error_message': str(error),
            'system_state': context['system_context'],
            'input_characteristics': context['input_analysis'],
            'resource_state': context['resource_context'],
            'similar_errors': self.find_similar_error_patterns(error, context),
            'recovery_probability': self.estimate_recovery_probability(error, context)
        }

    def select_recovery_strategy(self, error_analysis: Dict, context: Dict) -> Dict:
        """Select optimal recovery strategy based on context"""
        if error_analysis['error_type'] == 'OutOfMemoryError':
            if context['resource_context']['available_vram'] < 2000:  # < 2GB
                return {'type': 'reduce_quality', 'fallback_model': 'lightweight'}
            else:
                return {'type': 'clear_cache_retry', 'max_retries': 2}

        elif error_analysis['error_type'] == 'ModelLoadError':
            return {'type': 'fallback_model', 'fallback_sequence': ['instant_mesh', 'stable_diffusion']}

        elif error_analysis['recovery_probability'] > 0.7:
            return {'type': 'intelligent_retry', 'context_adjustment': True}
        else:
            return {'type': 'graceful_failure', 'user_notification': True}

CONTEXT PERSISTENCE & LEARNING:

# backend/context_persistence.py
class ContextPersistenceManager:
    """
    Manages context persistence and learning across sessions
    """

    def __init__(self):
        self.context_db = self.initialize_context_database()
        self.learning_models = self.load_learning_models()

    def persist_context(self, session_id: str, context: Dict):
        """Save context for future learning and optimization"""
        context_record = {
            'session_id': session_id,
            'timestamp': time.time(),
            'context_data': context,
            'context_hash': self.hash_context(context)
        }
        self.context_db.insert(context_record)

    def learn_patterns(self):
        """Analyze historical contexts to learn patterns"""
        # Analyze successful processing patterns
        successful_patterns = self.analyze_successful_contexts()

        # Identify failure patterns
        failure_patterns = self.analyze_failed_contexts()

        # Update recommendation models
        self.update_recommendation_models(successful_patterns, failure_patterns)

        # Update context similarity models
        self.update_similarity_models()

    def get_contextual_insights(self, current_context: Dict) -> Dict:
        """Provide insights based on historical context analysis"""
        similar_contexts = self.find_similar_historical_contexts(current_context)

        insights = {
            'success_probability': self.predict_success_probability(current_context),
            'optimal_parameters': self.recommend_parameters(current_context),
            'potential_issues': self.predict_potential_issues(current_context),
            'processing_time_estimate': self.estimate_processing_time(current_context),
            'resource_requirements': self.estimate_resource_requirements(current_context)
        }

        return insights

[2.8] PERFORMANCE & SECURITY OPTIMIZATION

SPEED OPTIMIZATION STRATEGIES:

# backend/performance_optimizer.py
class PerformanceOptimizer:
    """
    Comprehensive performance optimization for maximum speed
    """

    def __init__(self):
        self.cache_layers = {
            'model_cache': {},      # Model instance caching
            'result_cache': {},     # Generation result caching
            'context_cache': {},    # Context analysis caching
            'precompute_cache': {}  # Pre-computed optimizations
        }
        self.optimization_config = self.load_optimization_config()

    def apply_speed_optimizations(self, model, processing_type: str):
        """Apply all speed optimizations for maximum performance"""

        # 1. Model-level optimizations
        optimized_model = self.optimize_model_inference(model)

        # 2. Memory optimization
        optimized_model = self.apply_memory_optimization(optimized_model)

        # 3. Compilation optimization
        if self.optimization_config.get('torch_compile', False):
            optimized_model = torch.compile(optimized_model, mode='max-autotune')

        # 4. GPU-specific optimizations
        optimized_model = self.apply_gpu_optimizations(optimized_model)

        return optimized_model

    def optimize_model_inference(self, model):
        """Apply inference-specific optimizations"""
        # Enable inference mode
        model.eval()
        torch.set_grad_enabled(False)

        # Apply quantization if configured
        if self.optimization_config.get('quantization', False):
            model = torch.quantization.quantize_dynamic(
                model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8
            )

        # Apply pruning for faster inference
        if self.optimization_config.get('pruning', False):
            model = self.apply_structured_pruning(model, sparsity=0.3)

        return model

    def apply_memory_optimization(self, model):
        """Optimize memory usage for better performance"""
        # Enable memory efficient attention
        try:
            from xformers.ops import memory_efficient_attention
            model.use_memory_efficient_attention = True
        except ImportError:
            pass

        # Enable gradient checkpointing for large models
        if hasattr(model, 'gradient_checkpointing_enable'):
            model.gradient_checkpointing_enable()

        # Optimize CUDA memory allocation
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

        return model

    def apply_gpu_optimizations(self, model):
        """Apply GPU-specific performance optimizations"""
        # Enable TensorFloat-32 for RTX GPUs
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True

        # Enable cuDNN benchmark mode
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = False

        # Enable mixed precision training
        if self.optimization_config.get('mixed_precision', True):
            model = model.half()  # Convert to FP16

        return model

ACCURACY ENHANCEMENT SYSTEMS:

# backend/accuracy_enhancer.py
class AccuracyEnhancer:
    """
    Advanced accuracy enhancement through intelligent processing
    """

    def __init__(self):
        self.quality_metrics = {}
        self.accuracy_thresholds = {
            'minimum_quality': 0.7,
            'target_quality': 0.85,
            'excellence_quality': 0.95
        }
        self.enhancement_strategies = self.load_enhancement_strategies()

    def enhance_generation_accuracy(self, input_data: Dict, context: Dict) -> Dict:
        """Apply multi-layer accuracy enhancement"""

        # 1. Input preprocessing for accuracy
        preprocessed_input = self.preprocess_for_accuracy(input_data, context)

        # 2. Multi-model ensemble for higher accuracy
        if context.get('accuracy_priority', False):
            result = self.ensemble_generation(preprocessed_input, context)
        else:
            result = self.single_model_generation(preprocessed_input, context)

        # 3. Post-processing quality enhancement
        enhanced_result = self.post_process_for_quality(result, context)

        # 4. Quality validation and refinement
        final_result = self.validate_and_refine(enhanced_result, context)

        return final_result

    def preprocess_for_accuracy(self, input_data: Dict, context: Dict) -> Dict:
        """Intelligent preprocessing to maximize generation accuracy"""

        # Analyze input quality
        quality_analysis = self.analyze_input_quality(input_data)

        # Apply enhancement based on analysis
        if quality_analysis['needs_enhancement']:
            # Image denoising
            if 'noise_level' in quality_analysis and quality_analysis['noise_level'] > 0.3:
                input_data['image'] = self.denoise_image(input_data['image'])

            # Contrast enhancement
            if quality_analysis.get('low_contrast', False):
                input_data['image'] = self.enhance_contrast(input_data['image'])

            # Resolution upsampling if needed
            if quality_analysis.get('resolution_too_low', False):
                input_data['image'] = self.upsample_image(input_data['image'])

        return input_data

    def ensemble_generation(self, input_data: Dict, context: Dict) -> Dict:
        """Use multiple models for higher accuracy"""

        models = ['hunyuan3d_primary', 'hunyuan3d_alternative', 'instant_mesh']
        results = []

        for model_name in models:
            try:
                model_result = self.generate_with_model(model_name, input_data)
                results.append({
                    'model': model_name,
                    'result': model_result,
                    'quality_score': self.calculate_quality_score(model_result)
                })
            except Exception as e:
                logger.warning(f"Model {model_name} failed: {e}")
                continue

        # Select best result or combine results
        return self.select_best_or_combine(results, context)

    def validate_and_refine(self, result: Dict, context: Dict) -> Dict:
        """Final validation and refinement for accuracy"""

        quality_score = self.calculate_comprehensive_quality(result)

        if quality_score < self.accuracy_thresholds['minimum_quality']:
            # Apply refinement techniques
            result = self.apply_mesh_refinement(result)
            result = self.apply_texture_enhancement(result)

            # Recalculate quality
            quality_score = self.calculate_comprehensive_quality(result)

        result['accuracy_metrics'] = {
            'quality_score': quality_score,
            'accuracy_grade': self.get_accuracy_grade(quality_score),
            'enhancement_applied': quality_score >= self.accuracy_thresholds['target_quality']
        }

        return result

SECURITY HARDENING FRAMEWORK:

# backend/security_hardening.py
class SecurityHardening:
    """
    Comprehensive security hardening for production deployment
    """

    def __init__(self):
        self.security_config = self.load_security_config()
        self.threat_detector = ThreatDetector()
        self.access_monitor = AccessMonitor()

    def apply_security_hardening(self, app):
        """Apply comprehensive security hardening"""

        # 1. Input validation and sanitization
        self.setup_input_validation(app)

        # 2. Authentication and authorization
        self.setup_authentication_security(app)

        # 3. API security
        self.setup_api_security(app)

        # 4. File upload security
        self.setup_file_upload_security(app)

        # 5. Rate limiting and DoS protection
        self.setup_rate_limiting(app)

        # 6. Security monitoring
        self.setup_security_monitoring(app)

    def setup_input_validation(self, app):
        """Comprehensive input validation and sanitization"""

        @app.before_request
        def validate_all_inputs():
            # SQL injection protection
            if self.detect_sql_injection(request):
                abort(400, "Invalid input detected")

            # XSS protection
            if self.detect_xss_attempt(request):
                abort(400, "Malicious script detected")

            # Path traversal protection
            if self.detect_path_traversal(request):
                abort(400, "Path traversal attempt detected")

            # Command injection protection
            if self.detect_command_injection(request):
                abort(400, "Command injection attempt detected")

    def setup_api_security(self, app):
        """API-specific security measures"""

        # API key validation
        @app.before_request
        def validate_api_access():
            if request.endpoint and request.endpoint.startswith('api/'):
                api_key = request.headers.get('X-API-Key')
                if not self.validate_api_key(api_key):
                    abort(401, "Invalid API key")

        # Request signing for critical operations
        @app.before_request
        def validate_request_signature():
            if request.endpoint in self.security_config['signed_endpoints']:
                signature = request.headers.get('X-Request-Signature')
                if not self.validate_request_signature(request, signature):
                    abort(401, "Invalid request signature")

    def setup_file_upload_security(self, app):
        """Secure file upload handling"""

        def secure_file_validation(file):
            # File type validation
            if not self.validate_file_type(file):
                raise SecurityError("Invalid file type")

            # File size validation
            if not self.validate_file_size(file):
                raise SecurityError("File too large")

            # Malware scanning
            if self.detect_malware(file):
                raise SecurityError("Malicious file detected")

            # Content validation
            if not self.validate_file_content(file):
                raise SecurityError("Invalid file content")

            return True

    def setup_security_monitoring(self, app):
        """Real-time security monitoring"""

        @app.after_request
        def monitor_security_events(response):
            # Log security events
            self.log_security_event(request, response)

            # Detect anomalies
            if self.detect_anomaly(request, response):
                self.alert_security_team(request, response)

            # Update threat intelligence
            self.update_threat_intelligence(request, response)

            return response

AI/ML INTEGRATION OPTIMIZATION:

# backend/ml_integration_optimizer.py
class MLIntegrationOptimizer:
    """
    Optimized machine learning integration for maximum efficiency
    """

    def __init__(self):
        self.model_registry = ModelRegistry()
        self.inference_scheduler = InferenceScheduler()
        self.resource_optimizer = ResourceOptimizer()

    def optimize_ml_pipeline(self, pipeline_config: Dict) -> Dict:
        """Optimize entire ML pipeline for speed and accuracy"""

        # 1. Model selection optimization
        optimal_models = self.select_optimal_models(pipeline_config)

        # 2. Inference optimization
        optimized_inference = self.optimize_inference_pipeline(optimal_models)

        # 3. Resource allocation optimization
        optimized_resources = self.optimize_resource_allocation(optimized_inference)

        # 4. Caching strategy optimization
        optimized_caching = self.optimize_caching_strategy(optimized_resources)

        return optimized_caching

    def select_optimal_models(self, config: Dict) -> List[Dict]:
        """Select optimal model combination based on requirements"""

        requirements = {
            'speed_priority': config.get('speed_priority', 0.5),
            'accuracy_priority': config.get('accuracy_priority', 0.5),
            'resource_constraints': config.get('resource_constraints', {}),
            'quality_requirements': config.get('quality_requirements', {})
        }

        # Multi-objective optimization for model selection
        optimal_combination = self.multi_objective_model_selection(requirements)

        return optimal_combination

    def optimize_inference_pipeline(self, models: List[Dict]) -> Dict:
        """Optimize inference pipeline for maximum throughput"""

        # Pipeline parallelization
        parallel_stages = self.identify_parallel_stages(models)

        # Batch optimization
        optimal_batch_config = self.optimize_batch_processing(models)

        # Memory optimization
        memory_config = self.optimize_memory_usage(models)

        # GPU utilization optimization
        gpu_config = self.optimize_gpu_utilization(models)

        return {
            'parallel_stages': parallel_stages,
            'batch_config': optimal_batch_config,
            'memory_config': memory_config,
            'gpu_config': gpu_config
        }

    def optimize_caching_strategy(self, pipeline_config: Dict) -> Dict:
        """Intelligent caching for maximum performance"""

        caching_layers = {
            'model_cache': {
                'strategy': 'LRU',
                'size_limit': '8GB',
                'persistence': True
            },
            'inference_cache': {
                'strategy': 'Adaptive',
                'ttl': 3600,  # 1 hour
                'compression': True
            },
            'result_cache': {
                'strategy': 'Context-aware',
                'size_limit': '4GB',
                'deduplication': True
            }
        }

        return {
            **pipeline_config,
            'caching_layers': caching_layers,
            'cache_warming': self.setup_cache_warming(),
            'cache_invalidation': self.setup_smart_invalidation()
        }

[2.8] MULTI-ENCODING & INTERNATIONALIZATION IMPLEMENTATION

INTELLIGENT ENCODING DETECTION & CONVERSION:

# backend/encoding_manager.py - Core encoding management
class EncodingManager:
    """
    Advanced multi-character encoding detection and conversion system
    """

    def __init__(self):
        self.supported_encodings = [
            'utf-8', 'utf-16-le', 'utf-16-be', 'utf-32-le', 'utf-32-be',
            'ascii', 'latin-1', 'cp1252', 'gb2312', 'gbk', 'big5',
            'shift-jis', 'euc-kr', 'iso-8859-1', 'iso-8859-2', 'iso-8859-15'
        ]
        self.encoding_detectors = self.initialize_detectors()

    def detect_encoding_with_fallback(self, file_path: str) -> Dict[str, Any]:
        """Detect file encoding with intelligent fallback chain"""

        # Read file bytes for analysis
        with open(file_path, 'rb') as f:
            raw_data = f.read()

        # 1. BOM detection (highest priority)
        bom_encoding = self.detect_bom(raw_data)
        if bom_encoding:
            return {
                'encoding': bom_encoding,
                'confidence': 1.0,
                'method': 'bom_detection',
                'bom_present': True
            }

        # 2. Automatic detection using multiple libraries
        detection_results = []

        # chardet detection
        chardet_result = chardet.detect(raw_data)
        if chardet_result['encoding']:
            detection_results.append({
                'encoding': chardet_result['encoding'].lower(),
                'confidence': chardet_result['confidence'],
                'method': 'chardet'
            })

        # charset-normalizer detection
        normalizer_result = from_bytes(raw_data).best()
        if normalizer_result:
            detection_results.append({
                'encoding': str(normalizer_result.encoding).lower(),
                'confidence': normalizer_result.coherence,
                'method': 'charset_normalizer'
            })

        # 3. Select best detection result
        if detection_results:
            best_result = max(detection_results, key=lambda x: x['confidence'])
            if best_result['confidence'] > 0.7:
                return best_result

        # 4. Fallback chain validation
        fallback_chain = os.getenv('ENCODING_FALLBACK_CHAIN', 'utf-8,latin-1,cp1252,ascii').split(',')

        for encoding in fallback_chain:
            if self.validate_encoding(raw_data, encoding.strip()):
                return {
                    'encoding': encoding.strip(),
                    'confidence': 0.5,
                    'method': 'fallback_validation',
                    'fallback_position': fallback_chain.index(encoding)
                }

        # 5. Default to UTF-8 with error handling
        return {
            'encoding': 'utf-8',
            'confidence': 0.1,
            'method': 'default_fallback',
            'error_handling': 'replace'
        }

    def convert_file_encoding(self, input_path: str, output_path: str, target_encoding: str = 'utf-8') -> Dict[str, Any]:
        """Convert file from detected encoding to target encoding"""

        # Detect source encoding
        source_info = self.detect_encoding_with_fallback(input_path)
        source_encoding = source_info['encoding']

        try:
            # Read with detected encoding
            with open(input_path, 'r', encoding=source_encoding, errors='replace') as f:
                content = f.read()

            # Unicode normalization
            normalized_content = self.normalize_unicode(content)

            # Write with target encoding
            with open(output_path, 'w', encoding=target_encoding, errors='replace') as f:
                f.write(normalized_content)

            return {
                'success': True,
                'source_encoding': source_encoding,
                'target_encoding': target_encoding,
                'source_confidence': source_info['confidence'],
                'normalization_applied': True,
                'file_size_bytes': len(normalized_content.encode(target_encoding))
            }

        except Exception as e:
            return {
                'success': False,
                'error': str(e),
                'source_encoding': source_encoding,
                'target_encoding': target_encoding
            }

    def normalize_unicode(self, text: str, form: str = None) -> str:
        """Apply Unicode normalization to text"""

        normalization_form = form or os.getenv('UNICODE_NORMALIZATION', 'NFC')

        if normalization_form.upper() in ['NFC', 'NFD', 'NFKC', 'NFKD']:
            return unicodedata.normalize(normalization_form.upper(), text)
        else:
            return text

# backend/i18n_manager.py - Internationalization management
class InternationalizationManager:
    """
    Comprehensive internationalization and localization management
    """

    def __init__(self):
        self.supported_languages = self.load_supported_languages()
        self.language_detector = self.initialize_language_detector()
        self.translation_engine = self.setup_translation_engine()

    def detect_content_language(self, text: str) -> Dict[str, Any]:
        """Detect language of text content"""

        try:
            # Primary detection with langdetect
            detected_lang = detect(text)
            confidence = detect_langs(text)[0].prob

            # Validate against supported languages
            if detected_lang in self.supported_languages:
                return {
                    'language': detected_lang,
                    'confidence': confidence,
                    'method': 'langdetect',
                    'supported': True
                }
            else:
                # Find closest supported language
                closest_lang = self.find_closest_language(detected_lang)
                return {
                    'language': closest_lang,
                    'original_detection': detected_lang,
                    'confidence': confidence * 0.8,  # Reduced confidence
                    'method': 'langdetect_mapped',
                    'supported': True
                }

        except Exception as e:
            # Fallback to default language
            default_lang = os.getenv('DEFAULT_LANGUAGE', 'en-US')
            return {
                'language': default_lang,
                'confidence': 0.1,
                'method': 'fallback',
                'error': str(e),
                'supported': True
            }

    def setup_multilingual_processing(self, content: str, target_languages: List[str] = None) -> Dict[str, Any]:
        """Setup multilingual processing pipeline"""

        # Detect source language
        source_detection = self.detect_content_language(content)

        # Determine target languages
        if not target_languages:
            target_languages = os.getenv('SUPPORTED_LANGUAGES', 'en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi').split(',')

        # Create processing pipeline
        pipeline = {
            'source_language': source_detection['language'],
            'target_languages': target_languages,
            'processing_steps': [
                'encoding_normalization',
                'language_detection',
                'text_segmentation',
                'nlp_processing',
                'translation_preparation'
            ],
            'language_specific_processors': {}
        }

        # Setup language-specific processors
        for lang in target_languages:
            processor_config = self.get_language_processor_config(lang.strip())
            pipeline['language_specific_processors'][lang.strip()] = processor_config

        return pipeline

MULTI-PROGRAMMING LANGUAGE SUPPORT:

# backend/polyglot_code_manager.py - Multi-language code management
class PolyglotCodeManager:
    """
    Advanced multi-programming language code management and analysis
    """

    def __init__(self):
        self.supported_languages = {
            'python': {'extensions': ['.py', '.pyw'], 'keywords': ['def', 'class', 'import'], 'priority': 1},
            'javascript': {'extensions': ['.js', '.mjs'], 'keywords': ['function', 'const', 'let'], 'priority': 2},
            'typescript': {'extensions': ['.ts', '.tsx'], 'keywords': ['interface', 'type', 'enum'], 'priority': 3},
            'java': {'extensions': ['.java'], 'keywords': ['public class', 'private', 'protected'], 'priority': 4},
            'csharp': {'extensions': ['.cs'], 'keywords': ['namespace', 'using', 'public class'], 'priority': 5},
            'go': {'extensions': ['.go'], 'keywords': ['package', 'func', 'import'], 'priority': 6},
            'rust': {'extensions': ['.rs'], 'keywords': ['fn', 'struct', 'impl'], 'priority': 7},
            'php': {'extensions': ['.php'], 'keywords': ['<?php', 'function', 'class'], 'priority': 8},
            'cpp': {'extensions': ['.cpp', '.cc', '.cxx'], 'keywords': ['include_directive', 'namespace', 'class'], 'priority': 9},
            'html': {'extensions': ['.html', '.htm'], 'keywords': ['<!DOCTYPE', '<html>', '<head>'], 'priority': 10},
            'css': {'extensions': ['.css'], 'keywords': ['{', '}', ':'], 'priority': 11},
            'sql': {'extensions': ['.sql'], 'keywords': ['SELECT', 'FROM', 'WHERE'], 'priority': 12}
        }
        self.language_parsers = self.initialize_parsers()

    def detect_programming_language(self, code_content: str, file_path: str = None) -> Dict[str, Any]:
        """Intelligent programming language detection"""

        detection_methods = []

        # 1. File extension detection
        if file_path:
            ext_detection = self.detect_by_extension(file_path)
            if ext_detection:
                detection_methods.append({
                    'method': 'file_extension',
                    'language': ext_detection,
                    'confidence': 0.9
                })

        # 2. Keyword analysis
        keyword_detection = self.detect_by_keywords(code_content)
        detection_methods.extend(keyword_detection)

        # 3. Syntax pattern analysis
        syntax_detection = self.detect_by_syntax_patterns(code_content)
        detection_methods.extend(syntax_detection)

        # 4. Tree-sitter parsing (if available)
        if self.tree_sitter_available():
            tree_detection = self.detect_by_tree_sitter(code_content)
            detection_methods.extend(tree_detection)

        # Select best detection
        if detection_methods:
            best_detection = max(detection_methods, key=lambda x: x['confidence'])

            return {
                'detected_language': best_detection['language'],
                'confidence': best_detection['confidence'],
                'method': best_detection['method'],
                'all_detections': detection_methods,
                'supported': best_detection['language'] in self.supported_languages
            }
        else:
            return {
                'detected_language': 'unknown',
                'confidence': 0.0,
                'method': 'none',
                'supported': False
            }

    def create_polyglot_project_structure(self, languages: List[str], project_name: str) -> Dict[str, Any]:
        """Create multi-language project structure"""

        project_structure = {
            'project_name': project_name,
            'languages': languages,
            'directories': {},
            'config_files': {},
            'build_systems': {},
            'dependencies': {}
        }

        for lang in languages:
            lang_config = self.get_language_config(lang)

            # Create language-specific directories
            project_structure['directories'][lang] = {
                'source': f'src/{lang}',
                'tests': f'tests/{lang}',
                'docs': f'docs/{lang}',
                'build': f'build/{lang}'
            }

            # Language-specific configuration files
            project_structure['config_files'][lang] = lang_config.get('config_files', [])

            # Build system configuration
            project_structure['build_systems'][lang] = lang_config.get('build_system', 'generic')

            # Dependency management
            project_structure['dependencies'][lang] = lang_config.get('dependency_manager', 'manual')

        # Cross-language integration
        project_structure['integration'] = {
            'api_contracts': 'contracts/',
            'shared_schemas': 'schemas/',
            'documentation': 'docs/integration/',
            'communication_protocols': ['rest_api', 'grpc', 'graphql']
        }

        return project_structure

    def setup_cross_language_interop(self, source_lang: str, target_lang: str) -> Dict[str, Any]:
        """Setup interoperability between different programming languages"""

        interop_config = {
            'source_language': source_lang,
            'target_language': target_lang,
            'communication_methods': [],
            'data_serialization': [],
            'build_integration': {},
            'testing_strategy': {}
        }

        # Determine communication methods
        if source_lang == 'python' and target_lang == 'javascript':
            interop_config['communication_methods'] = ['rest_api', 'websocket', 'json_rpc']
            interop_config['data_serialization'] = ['json', 'msgpack']
        elif source_lang == 'java' and target_lang == 'python':
            interop_config['communication_methods'] = ['grpc', 'rest_api', 'jni']
            interop_config['data_serialization'] = ['protobuf', 'json', 'avro']
        elif 'cpp' in [source_lang, target_lang]:
            interop_config['communication_methods'] = ['ffi', 'shared_library', 'c_api']
            interop_config['data_serialization'] = ['protobuf', 'flatbuffers', 'binary']

        # Build integration setup
        interop_config['build_integration'] = self.setup_build_integration(source_lang, target_lang)

        return interop_config

# backend/cross_language_translator.py - Code translation engine
class CrossLanguageTranslator:
    """
    AI-powered code translation between programming languages
    """

    def __init__(self):
        self.translation_models = {
            'ast_based': self.setup_ast_translator(),
            'ai_powered': self.setup_ai_translator(),
            'pattern_based': self.setup_pattern_translator()
        }

    def translate_code(self, source_code: str, source_lang: str, target_lang: str, translation_method: str = 'ai_powered') -> Dict[str, Any]:
        """Translate code from one language to another"""

        if translation_method not in self.translation_models:
            translation_method = 'ai_powered'  # Default fallback

        try:
            # Pre-processing
            preprocessed_code = self.preprocess_for_translation(source_code, source_lang)

            # Translation
            translator = self.translation_models[translation_method]
            translated_code = translator.translate(preprocessed_code, source_lang, target_lang)

            # Post-processing
            final_code = self.postprocess_translation(translated_code, target_lang)

            # Validation
            validation_result = self.validate_translated_code(final_code, target_lang)

            return {
                'translated_code': final_code,
                'source_language': source_lang,
                'target_language': target_lang,
                'translation_method': translation_method,
                'validation': validation_result,
                'confidence': validation_result.get('syntax_valid', False) * 0.8 + 0.2
            }

        except Exception as e:
            return {
                'error': str(e),
                'source_language': source_lang,
                'target_language': target_lang,
                'translation_method': translation_method,
                'success': False
            }

    def validate_translated_code(self, code: str, language: str) -> Dict[str, Any]:
        """Validate translated code for syntax and basic structure"""

        validation = {
            'syntax_valid': False,
            'structure_valid': False,
            'warnings': [],
            'errors': []
        }

        try:
            # Language-specific syntax validation
            if language == 'python':
                validation = self.validate_python_syntax(code)
            elif language == 'javascript':
                validation = self.validate_javascript_syntax(code)
            elif language == 'java':
                validation = self.validate_java_syntax(code)
            # Add more languages as needed

            return validation

        except Exception as e:
            validation['errors'].append(f"Validation error: {str(e)}")
            return validation

DEBUGGING PATTERNS FOR MULTI-LANGUAGE SUPPORT:

# Enhanced debugging for multi-encoding and multi-language issues
def debug_encoding_issues(file_path: str, error: Exception) -> Dict[str, Any]:
    """Debug encoding-related issues with comprehensive analysis"""

    debug_info = {
        'file_path': file_path,
        'error_type': type(error).__name__,
        'error_message': str(error),
        'encoding_analysis': {},
        'suggested_fixes': []
    }

    try:
        # Analyze file encoding
        encoding_mgr = EncodingManager()
        detection_result = encoding_mgr.detect_encoding_with_fallback(file_path)
        debug_info['encoding_analysis'] = detection_result

        # Generate specific fixes based on error type
        if 'UnicodeDecodeError' in str(error):
            debug_info['suggested_fixes'].extend([
                f"Try reading with detected encoding: {detection_result['encoding']}",
                "Enable error handling: errors='replace' or errors='ignore'",
                "Convert file to UTF-8 using encoding_manager.convert_file_encoding()"
            ])
        elif 'UnicodeEncodeError' in str(error):
            debug_info['suggested_fixes'].extend([
                "Normalize Unicode text using unicodedata.normalize()",
                "Use UTF-8 encoding for output",
                "Apply Unicode escape sequences for problematic characters"
            ])

        return debug_info

    except Exception as debug_error:
        debug_info['debug_error'] = str(debug_error)
        return debug_info

def debug_language_detection_issues(code: str, expected_lang: str = None) -> Dict[str, Any]:
    """Debug programming language detection issues"""

    debug_info = {
        'code_sample': code[:200] + '...' if len(code) > 200 else code,
        'expected_language': expected_lang,
        'detection_results': {},
        'suggested_improvements': []
    }

    try:
        # Multi-method language detection
        polyglot_mgr = PolyglotCodeManager()
        detection_result = polyglot_mgr.detect_programming_language(code)
        debug_info['detection_results'] = detection_result

        # Analyze detection accuracy
        if expected_lang and detection_result['detected_language'] != expected_lang:
            debug_info['suggested_improvements'].extend([
                f"Add more {expected_lang}-specific keywords to improve detection",
                "Provide file extension hint for better accuracy",
                "Use Tree-sitter parser for more accurate syntax analysis",
                "Enable multi-language detection for polyglot code"
            ])

        return debug_info

    except Exception as debug_error:
        debug_info['debug_error'] = str(debug_error)
        return debug_info

[3] CODING STANDARDS

[3.1] PYTHON REQUIREMENTS

CODING PATTERNS:

# backend/main.py structure
"""
ORFEAS AI 2D→3D Studio - Module Name
==================================================
ORFEAS AI

Features:
- Feature 1
- Feature 2
"""

# ALWAYS: Type hints for function signatures
def generate_3d_model(
    image: Union[str, Image.Image],
    quality: int = 7,
    format: str = 'stl'
) -> Dict[str, Any]:
    """
    Generate 3D model from 2D image

    Args:
        image: PIL Image or file path
        quality: Model quality 1-10 (default: 7)
        format: Output format (stl, obj, glb)

    Returns:
        Dict with 'mesh_path', 'stats', 'metadata'

    Raises:
        ValueError: Invalid quality or format
        RuntimeError: GPU memory insufficient
    """
    pass

# ALWAYS: Comprehensive error handling
try:
    mesh = processor.generate_shape(image)
except torch.cuda.OutOfMemoryError:
    logger.error("GPU OOM - clearing cache and retrying")
    torch.cuda.empty_cache()
    mesh = processor.generate_shape(image, low_vram_mode=True)
except Exception as e:
    logger.error(f"Generation failed: {traceback.format_exc()}")
    raise RuntimeError(f"3D generation failed: {str(e)}")

# ALWAYS: Use consistent logger format
logger = logging.getLogger(__name__)
logger.info("[ORFEAS] Feature initialized successfully")
logger.warning("[ORFEAS] GPU memory at 85% - consider cleanup")
logger.error("[ORFEAS] Critical failure - reverting to fallback")

[3.2] CONFIGURATION MANAGEMENT

Environment Variables (.env):

# backend/.env
ORFEAS_PORT=5000
DEVICE=cuda                    # auto, cuda, cpu
MAX_CONCURRENT_JOBS=3
GPU_MEMORY_LIMIT=0.8           # 80% of total VRAM
HUNYUAN3D_PATH=../Hunyuan3D-2.1
MODEL_CACHE_DIR=./models
DEFAULT_STEPS=50
MAX_STEPS=100
LOG_LEVEL=INFO

# AI Video Composition Settings
ENABLE_VIDEO_GENERATION=true
SORA_MODEL_PATH=./models/sora
VIDEO_OUTPUT_DIR=./outputs/videos
DEFAULT_VIDEO_FPS=24
DEFAULT_VIDEO_RESOLUTION=1920x1080
MAX_VIDEO_DURATION=60          # seconds
ENABLE_CINEMATIC_MODE=true
VIDEO_QUALITY_PRESET=high      # low, medium, high, ultra
ENABLE_3D_TO_VIDEO=true
ENABLE_TEXT_TO_VIDEO=true
ENABLE_IMAGE_TO_VIDEO=true
VIDEO_CACHE_SIZE=10            # GB for video caching
TEMPORAL_CONSISTENCY_ENABLED=true

# AI Code Development Settings
ENABLE_CODE_WRITING=true
ENABLE_CODE_DEBUGGING=true
ENABLE_CODE_ANALYSIS=true
CODE_AI_MODEL=github-copilot   # github-copilot, codeT5, starcoder, deepseek-coder
CODE_OUTPUT_DIR=./outputs/code
CODE_QUALITY_THRESHOLD=0.8     # Minimum code quality score
ENABLE_AUTO_FORMATTING=true
ENABLE_SYNTAX_VALIDATION=true
ENABLE_PERFORMANCE_ANALYSIS=true
CODE_GENERATION_TIMEOUT=30     # seconds
ENABLE_CODE_DOCUMENTATION=true
ENABLE_TEST_GENERATION=true

# AI Multimedia Generation Settings
ENABLE_TEXT_TO_IMAGE=true
ENABLE_TEXT_TO_SPEECH=true
ENABLE_SPEECH_TO_TEXT=true
TEXT_TO_IMAGE_MODEL=dalle3        # dalle3, stable_diffusion_xl, midjourney
TEXT_TO_IMAGE_QUALITY=high        # low, medium, high, ultra
TEXT_TO_IMAGE_OUTPUT_DIR=./outputs/images
TEXT_TO_IMAGE_CACHE_SIZE=5        # GB for image caching
SPEECH_MODEL=whisper_large        # whisper_large, azure_speech, google_speech
TTS_MODEL=elevenlabs              # elevenlabs, azure_tts, openai_tts
TTS_VOICE_ID=default              # Voice ID for text-to-speech
TTS_OUTPUT_DIR=./outputs/audio
AUDIO_QUALITY=high                # low, medium, high
MAX_AUDIO_DURATION=300            # seconds (5 minutes)
ENABLE_REAL_TIME_TRANSCRIPTION=true
ENABLE_VOICE_CLONING=false        # Advanced voice synthesis
SPEECH_LANGUAGE=en-US             # Language for speech processing

# Enterprise LLM Integration Settings
ENABLE_LLM_INTEGRATION=true
LLM_PRIMARY_MODEL=gpt4_turbo    # gpt4_turbo, claude_3_5_sonnet, gemini_ultra
LLM_FALLBACK_MODEL=claude_3_5_sonnet
LLM_API_TIMEOUT=30              # seconds
LLM_MAX_TOKENS=4000             # Maximum tokens per request
LLM_TEMPERATURE=0.3             # Creativity vs determinism (0.0-1.0)
LLM_CONTEXT_WINDOW=32000        # Context window size

# Multi-LLM Orchestration
ENABLE_MULTI_LLM_ORCHESTRATION=true
LLM_TASK_DECOMPOSITION=true     # Break complex tasks into subtasks
LLM_RESULT_SYNTHESIS=true       # Combine results from multiple LLMs
LLM_QUALITY_VALIDATION=true     # Validate LLM responses for quality
LLM_RESPONSE_CACHING=true       # Cache frequent responses
LLM_CACHE_TTL=3600              # Cache time-to-live in seconds

# RAG (Retrieval-Augmented Generation) Settings
ENABLE_RAG_SYSTEM=true
VECTOR_DATABASE_TYPE=pinecone   # pinecone, weaviate, qdrant, chroma
VECTOR_DIMENSION=1536           # Embedding dimension
KNOWLEDGE_BASE_PATH=./knowledge
RAG_RETRIEVAL_TOP_K=10          # Number of relevant documents to retrieve
RAG_SIMILARITY_THRESHOLD=0.7    # Minimum similarity score for retrieval
ENABLE_KNOWLEDGE_GRAPH=true     # Neo4j knowledge graph integration

# GitHub Copilot Enterprise Integration
GITHUB_COPILOT_ENABLED=true
GITHUB_COPILOT_MODEL=copilot-chat    # copilot-chat, copilot-code
COPILOT_SUGGESTIONS_COUNT=3     # Number of code suggestions
COPILOT_QUALITY_THRESHOLD=0.8   # Minimum quality score for suggestions
ENABLE_COPILOT_CONTEXT=true     # Include project context in requests
COPILOT_SECURITY_SCANNING=true  # Scan generated code for security issues

# LLM Performance & Optimization
LLM_BATCH_PROCESSING=true       # Enable batch processing for multiple requests
LLM_PARALLEL_REQUESTS=4         # Maximum parallel LLM requests
ENABLE_LLM_COMPRESSION=true     # Compress prompts for efficiency
LLM_RESPONSE_STREAMING=true     # Enable streaming responses
LLM_LOAD_BALANCING=true         # Distribute requests across multiple models
LLM_FAILOVER_ENABLED=true       # Automatic failover to backup models

# AI Agents & ML Configuration
ENABLE_AI_AGENTS=true
AGENT_AUTH_ENABLED=true
QUALITY_AGENT_ENABLED=true
WORKFLOW_AGENT_ENABLED=true
OPTIMIZATION_AGENT_ENABLED=true

# ML Optimization Settings
ENABLE_TENSORRT=false          # Requires TensorRT installation
ENABLE_MIXED_PRECISION=true    # FP16 optimization
ENABLE_MODEL_QUANTIZATION=false # INT8 quantization
BATCH_INFERENCE_SIZE=4         # Max batch size for inference
AUTO_MODEL_SELECTION=true      # Let agents choose optimal models

# Alternative AI Models
ENABLE_STABLE_DIFFUSION=true
ENABLE_COMFYUI_INTEGRATION=true
ENABLE_HUGGINGFACE_FALLBACK=true
CLIP_MODEL_NAME=openai/clip-vit-base-patch32
STABLE_DIFFUSION_MODEL=runwayml/stable-diffusion-v1-5

# Performance Monitoring
ENABLE_ML_METRICS=true
TRACK_AGENT_PERFORMANCE=true
AUTO_OPTIMIZATION=true
PERFORMANCE_THRESHOLD=0.8      # Trigger optimization below this score

# Web Technologies & Security
ENABLE_HTTPS=true
SSL_CERT_PATH=ssl/orfeas.crt
SSL_KEY_PATH=ssl/orfeas.key
ENABLE_HSTS=true
CSP_ENABLED=true
PWA_ENABLED=true
SERVICE_WORKER_ENABLED=true

# PHP Integration (Optional)
ENABLE_PHP_COMPATIBILITY=false
PHP_API_ENDPOINT=/php/api.php
PHP_SESSION_ENABLED=false

# Web Server Configuration
NGINX_SSL_ENABLED=true
STATIC_FILE_COMPRESSION=true
RATE_LIMITING_ENABLED=true
MAX_REQUESTS_PER_MINUTE=60

# Intelligent Context & Learning
ENABLE_CONTEXT_MANAGER=true
CONTEXT_PERSISTENCE_ENABLED=true
CONTEXT_LEARNING_ENABLED=true
CONTEXT_DB_PATH=data/context.db
AUTO_CONTEXT_OPTIMIZATION=true
CONTEXT_CACHE_SIZE=1000
CONTEXT_SIMILARITY_THRESHOLD=0.8
ENABLE_PREDICTIVE_INSIGHTS=true
CONTEXT_RETENTION_DAYS=90

# Performance & Security Optimization
ENABLE_PERFORMANCE_OPTIMIZER=true
AUTO_PERFORMANCE_TUNING=true
PERFORMANCE_MONITORING_ENABLED=true
PERFORMANCE_THRESHOLD=0.8          # Trigger optimization below this score
SPEED_OPTIMIZATION_ENABLED=true
ACCURACY_ENHANCEMENT_ENABLED=true
SECURITY_HARDENING_ENABLED=true

# Speed Optimization Settings
ENABLE_MODEL_COMPILATION=true      # torch.compile() optimization
ENABLE_CACHE_WARMING=true          # Pre-warm model caches
FAST_MODE_QUALITY_THRESHOLD=5      # Quality level for fast mode
BALANCED_MODE_QUALITY_THRESHOLD=7  # Quality level for balanced mode
QUALITY_MODE_QUALITY_THRESHOLD=9   # Quality level for quality mode

# Accuracy Enhancement Settings
ENABLE_ENSEMBLE_GENERATION=false   # Multi-model ensemble (resource intensive)
QUALITY_VALIDATION_THRESHOLD=0.85  # Minimum quality score threshold
ENABLE_AUTOMATIC_REFINEMENT=true   # Auto-refine low quality results
ENABLE_FEEDBACK_LEARNING=true      # Learn from user quality feedback
QUALITY_ASSESSMENT_ENABLED=true    # Pre-generation quality assessment

# TQM (Total Quality Management) Audit System
ENABLE_TQM_AUDITS=true              # Enable comprehensive quality audits
TQM_AUDIT_LEVEL=enterprise          # basic, professional, enterprise
AUTOMATED_AUDIT_SCHEDULING=true     # Schedule automatic audits
CONTINUOUS_QUALITY_MONITORING=true  # Real-time quality monitoring
QUALITY_GATE_ENFORCEMENT=true       # Enforce quality gates for requests

# Quality Audit Scheduling
DAILY_AUDITS_ENABLED=true           # Enable daily performance audits
WEEKLY_AUDITS_ENABLED=true          # Enable weekly process audits
MONTHLY_AUDITS_ENABLED=true         # Enable monthly comprehensive audits
QUARTERLY_AUDITS_ENABLED=true       # Enable quarterly strategic reviews
ANNUAL_AUDITS_ENABLED=true          # Enable annual certification audits

# Quality Standards & Compliance
ISO_9001_COMPLIANCE=true            # ISO 9001:2015 Quality Management
ISO_27001_COMPLIANCE=true           # ISO 27001:2022 Information Security
SOC2_TYPE2_COMPLIANCE=true          # SOC2 Type 2 Security Controls
SIX_SIGMA_METHODOLOGY=true          # Six Sigma process improvement
CMMI_LEVEL5_TARGET=true             # CMMI Level 5 capability maturity
LEAN_MANUFACTURING_PRINCIPLES=true  # Lean waste elimination

# Quality Metrics & Thresholds
OVERALL_QUALITY_THRESHOLD=0.85      # Overall quality score threshold
PERFORMANCE_QUALITY_THRESHOLD=0.90  # Performance quality threshold
RELIABILITY_QUALITY_THRESHOLD=0.95  # Reliability quality threshold
SECURITY_QUALITY_THRESHOLD=0.90     # Security quality threshold
COMPLIANCE_QUALITY_THRESHOLD=0.98   # Compliance quality threshold
USER_SATISFACTION_THRESHOLD=0.85    # User satisfaction threshold

# Quality Audit Reporting
AUTO_AUDIT_REPORT_GENERATION=true   # Automatically generate audit reports
EXECUTIVE_DASHBOARD_ENABLED=true    # Quality executive dashboard
STAKEHOLDER_NOTIFICATIONS=true      # Notify stakeholders of audit results
QUALITY_TREND_ANALYSIS=true         # Analyze quality trends over time
BENCHMARK_COMPARISONS=true          # Compare against industry benchmarks

# Quality Improvement Actions
AUTO_QUALITY_CORRECTION=true        # Automatic quality corrections
QUALITY_ALERT_SYSTEM=true           # Real-time quality alerts
PROACTIVE_QUALITY_MANAGEMENT=true   # Proactive quality issue prevention
QUALITY_TRAINING_RECOMMENDATIONS=true # Recommend quality training
BEST_PRACTICES_ENFORCEMENT=true     # Enforce quality best practices

# Advanced Quality Analytics
AI_POWERED_QUALITY_ANALYSIS=true    # AI-driven quality analysis
PREDICTIVE_QUALITY_MODELING=true    # Predict quality issues before they occur
QUALITY_PATTERN_RECOGNITION=true    # Recognize quality improvement patterns
CUSTOMER_FEEDBACK_INTEGRATION=true  # Integrate customer feedback into quality metrics
QUALITY_ROI_TRACKING=true           # Track return on investment for quality initiatives

# Security Hardening Settings
THREAT_DETECTION_ENABLED=true      # Real-time threat detection
SECURITY_AUDIT_LOGGING=true        # Comprehensive security logging
FILE_MALWARE_SCANNING=true         # Scan uploaded files for malware
REQUEST_SIGNATURE_VALIDATION=false # Validate signed requests
SECURITY_MONITORING_LEVEL=MEDIUM   # LOW, MEDIUM, HIGH, PARANOID

# ML Integration Optimization
ADAPTIVE_MODEL_SELECTION=true      # Intelligent model selection
PIPELINE_OPTIMIZATION_ENABLED=true # Optimize ML pipelines
RESOURCE_ALLOCATION_OPTIMIZATION=true # Optimize resource allocation
MODEL_PERFORMANCE_TRACKING=true    # Track model performance metrics
AUTO_MODEL_SWITCHING=true          # Switch models based on performance

# Enterprise Cloud Deployment & Scaling
CLOUD_PROVIDER=aws                  # aws, azure, gcp, hybrid
KUBERNETES_ENABLED=true             # Enable Kubernetes deployment
KUBERNETES_NAMESPACE=orfeas-prod    # K8s namespace for production
AUTO_SCALING_ENABLED=true           # Horizontal pod autoscaling
MIN_REPLICAS=2                      # Minimum pod replicas
MAX_REPLICAS=20                     # Maximum pod replicas
CPU_TARGET_UTILIZATION=70           # CPU target for autoscaling
MEMORY_TARGET_UTILIZATION=80        # Memory target for autoscaling

# Enterprise Monitoring & Observability
PROMETHEUS_ENABLED=true             # Enable Prometheus metrics
GRAFANA_ENABLED=true                # Enable Grafana dashboards
JAEGER_TRACING_ENABLED=true         # Distributed tracing
ELK_STACK_ENABLED=true              # Elasticsearch, Logstash, Kibana
DATADOG_INTEGRATION=false           # Datadog APM integration
NEW_RELIC_INTEGRATION=false         # New Relic monitoring

# Enterprise Database & Caching
DATABASE_TYPE=postgresql            # postgresql, mysql, mongodb
DATABASE_CLUSTER_ENABLED=true       # Database clustering
REDIS_CLUSTER_ENABLED=true          # Redis clustering for caching
DATABASE_BACKUP_ENABLED=true        # Automated database backups
DATABASE_ENCRYPTION_ENABLED=true    # Database encryption at rest

# Enterprise CI/CD & DevOps
GITHUB_ACTIONS_ENABLED=true         # GitHub Actions CI/CD
JENKINS_INTEGRATION=false           # Jenkins CI/CD
GITLAB_CI_INTEGRATION=false         # GitLab CI integration
DOCKER_REGISTRY=ghcr.io             # Container registry
HELM_CHARTS_ENABLED=true            # Helm for Kubernetes deployments
TERRAFORM_ENABLED=true              # Infrastructure as Code

# Enterprise API & Microservices
API_GATEWAY_ENABLED=true            # API Gateway for microservices
LOAD_BALANCER_TYPE=nginx            # nginx, haproxy, aws-alb
SERVICE_MESH_ENABLED=false          # Istio service mesh
CIRCUIT_BREAKER_ENABLED=true        # Circuit breaker pattern
API_RATE_LIMITING_ENABLED=true      # Enterprise API rate limiting
API_KEY_MANAGEMENT_ENABLED=true     # Enterprise API key management

# Enterprise Security & Compliance
OAUTH2_ENABLED=true                 # OAuth2 authentication
SAML_SSO_ENABLED=false             # SAML Single Sign-On
LDAP_INTEGRATION=false             # LDAP authentication
RBAC_ENABLED=true                  # Role-based access control
AUDIT_LOGGING_ENABLED=true         # Comprehensive audit logging
COMPLIANCE_FRAMEWORK=SOC2          # SOC2, ISO27001, GDPR, HIPAA
VULNERABILITY_SCANNING=true        # Automated vulnerability scanning
PENETRATION_TESTING=quarterly      # Penetration testing schedule

# Advanced Problem Identification & Automated Fixing
ENABLE_PROBLEM_DETECTION=true      # Enable intelligent problem detection
ENABLE_AUTOMATED_FIXING=true       # Enable automated problem resolution
PROBLEM_DETECTION_INTERVAL=30      # Problem detection check interval (seconds)
AUTO_FIX_CONFIDENCE_THRESHOLD=0.8  # Minimum confidence for automated fixes
ENABLE_PROACTIVE_MONITORING=true   # Enable proactive problem prevention
HEALTH_MONITORING_ENABLED=true     # Enable continuous health monitoring
ALERT_SYSTEM_ENABLED=true          # Enable alerting system
PROBLEM_LEARNING_ENABLED=true      # Enable learning from problem patterns
DIAGNOSTIC_LOGGING_ENABLED=true    # Enable detailed diagnostic logging
ERROR_RECOVERY_ENABLED=true        # Enable intelligent error recovery
MANUAL_INTERVENTION_THRESHOLD=3    # Max auto-fix attempts before manual intervention

# Multi-Encoding & Internationalization Settings
ENABLE_MULTI_ENCODING=true         # Enable multi-character encoding support
DEFAULT_ENCODING=utf-8             # Primary character encoding
ENCODING_AUTO_DETECTION=true       # Automatic encoding detection for files
ENCODING_FALLBACK_CHAIN=utf-8,latin-1,cp1252,ascii  # Fallback encoding order
UNICODE_NORMALIZATION=NFC          # Unicode normalization form (NFC, NFD, NFKC, NFKD)
BOM_DETECTION=true                 # Byte Order Mark detection
ENCODING_ERROR_HANDLING=replace    # strict, ignore, replace, xmlcharrefreplace
LANGUAGE_DETECTION_ENABLED=true    # Automatic language detection
DEFAULT_LANGUAGE=en-US             # Default language for processing
SUPPORTED_LANGUAGES=en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi  # Supported languages
I18N_LOCALE_PATH=./locales         # Path to internationalization files
I18N_DOMAIN=orfeas                 # Translation domain name
BABEL_CONFIG_FILE=babel.cfg        # Babel configuration file
GETTEXT_ENABLED=true               # Enable GNU gettext support
POLYGLOT_NLP_ENABLED=true          # Enable polyglot NLP processing
LANGCODES_NORMALIZATION=true       # Language code standardization

# Multi-Code Language Programming Settings
ENABLE_MULTI_LANGUAGE_SUPPORT=true    # Enable multi-programming language support
PRIMARY_LANGUAGES=python,javascript,typescript,html,css,sql  # Primary development languages
ENTERPRISE_LANGUAGES=php,java,csharp,go,rust,cpp  # Enterprise programming languages
SPECIALIZED_LANGUAGES=r,matlab,swift,kotlin,dart,wasm  # Specialized programming languages
INFRASTRUCTURE_LANGUAGES=bash,powershell,yaml,json,xml,toml  # Infrastructure & DevOps languages
DATABASE_LANGUAGES=sql,nosql,graphql,sparql  # Database query languages
LANGUAGE_SERVER_PROTOCOL=true     # Enable LSP integration
TREE_SITTER_ENABLED=true          # Universal syntax highlighting
ANTLR_PARSER_ENABLED=true         # Cross-language parser generator
PROTOBUF_ENABLED=true             # Protocol Buffers cross-language serialization
GRPC_ENABLED=true                 # Cross-language RPC framework
THRIFT_ENABLED=true               # Apache Thrift cross-language services
MULTI_LANGUAGE_AI_ENABLED=true    # AI-powered multi-language code generation
CODE_TRANSLATION_ENABLED=true     # Code translation between languages
CROSS_LANGUAGE_REFACTORING=true   # Cross-language refactoring support
LANGUAGE_INTEROP_ENABLED=true     # Language interoperability features
CODE_ANALYSIS_MULTI_LANG=true     # Multi-language code analysis
SYNTAX_VALIDATION_MULTI_LANG=true # Multi-language syntax validation
AUTO_LANGUAGE_DETECTION=true      # Automatic programming language detection
LANGUAGE_SPECIFIC_OPTIMIZATION=true # Language-specific performance optimizations
POLYGLOT_PROJECT_SUPPORT=true     # Support for polyglot programming projects
LANGUAGE_MIGRATION_TOOLS=true     # Tools for migrating code between languages

Config Access Pattern:

# backend/config.py
class Config:
    def __init__(self):
        self.config = {
            "processing": {
                "device": os.getenv("DEVICE", "auto"),
                "max_concurrent_jobs": int(os.getenv("MAX_CONCURRENT_JOBS", 3))
            }
        }

# Usage in modules
from config import Config
config = Config()
device = config.get("processing.device")  # auto, cuda, or cpu

[3.3] ERROR HANDLING PATTERNS

ERROR HANDLING PHILOSOPHY:

Log comprehensive error context
Intelligent problem detection and classification
Attempt automated fixes when confidence is high
Graceful recovery with fallback strategies
Proactive problem prevention
Emit WebSocket error notification with diagnostics
Return user-friendly error message with support guidance

ENHANCED PATTERN WITH AUTOMATED PROBLEM DETECTION:

@app.route('/api/generate-3d')
def generate_3d():
    try:
        # Primary path: Hunyuan3D-2.1
        mesh = hunyuan_processor.generate_shape(image)
    except torch.cuda.OutOfMemoryError as e:
        # Automatic problem detection and fixing
        error_context = {
            'error_type': 'GPU_MEMORY',
            'error_message': str(e),
            'system_state': get_gpu_stats()
        }

        problem_detector = IntelligentProblemDetector()
        fix_result = problem_detector.auto_fix_problems([{
            'category': 'GPU_MEMORY',
            'automated_fix': True,
            'solutions': ['clear_cache', 'reduce_batch_size']
        }])

        if fix_result['successful_fixes']:
            logger.info("[ORFEAS] GPU OOM auto-fixed, retrying generation")
            mesh = hunyuan_processor.generate_shape(image)
        else:
            logger.warning("[ORFEAS] GPU OOM - using fallback processor")
            mesh = FallbackProcessor().generate_shape(image)

    except FileNotFoundError as e:
        # Auto-detect and fix model file issues
        error_context = {
            'error_type': 'MODEL_FILES',
            'error_message': str(e),
            'file_path': getattr(e, 'filename', 'unknown')
        }

        problem_detector = IntelligentProblemDetector()
        detection_result = problem_detector.detect_and_classify_problems(error_context)

        if detection_result['automated_fixes_available']:
            fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])
            if fix_result['successful_fixes']:
                logger.info("[ORFEAS] Model files auto-fixed, retrying generation")
                mesh = hunyuan_processor.generate_shape(image)
            else:
                return jsonify({
                    'error': 'AI model not initialized',
                    'details': 'Please run setup_full_ai.py first',
                    'diagnostic_info': detection_result,
                    'auto_fix_attempted': True
                }), 500
        else:
            logger.error(f"[ORFEAS] Model files missing: {e}")
            return jsonify({
                'error': 'AI model not initialized',
                'details': 'Please run setup_full_ai.py first'
            }), 500

    except Exception as e:
        # Comprehensive error handling with problem detection
        error_context = {
            'error_type': type(e).__name__,
            'error_message': str(e),
            'traceback': traceback.format_exc(),
            'request_data': request.get_json() if request.is_json else None,
            'system_state': get_system_state(),
            'timestamp': datetime.utcnow().isoformat()
        }

        # Attempt automated problem detection and fixing
        problem_detector = IntelligentProblemDetector()
        detection_result = problem_detector.detect_and_classify_problems(error_context)

        fix_result = None
        if detection_result['automated_fixes_available']:
            fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])

            if fix_result['successful_fixes']:
                logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}")
                try:
                    # Retry the operation after fix
                    mesh = hunyuan_processor.generate_shape(image)
                except Exception as retry_error:
                    logger.error(f"[ORFEAS] Retry after fix failed: {retry_error}")

        logger.error(f"[ORFEAS] Unexpected error: {traceback.format_exc()}")
        socketio.emit('generation_error', {
            'message': 'Generation failed',
            'recoverable': bool(fix_result and fix_result['successful_fixes']),
            'diagnostic_info': detection_result,
            'auto_fix_attempted': fix_result is not None
        })

        return jsonify({
            'error': str(e),
            'error_id': generate_error_id(),
            'diagnostic_info': detection_result,
            'auto_fix_result': fix_result,
            'support_guidance': generate_user_guidance(detection_result)
        }), 500

[3.4] ASYNC JOB QUEUE USAGE

Batch Processing for Long Operations:

# backend/batch_processor.py
from batch_processor import AsyncJobQueue

async_queue = AsyncJobQueue(max_workers=3, gpu_mgr=gpu_manager)

# Submit job
job = async_queue.submit_job(
    job_type='3d_generation',
    image=image_data,
    quality=7,
    callback=on_complete
)

# Track progress
@socketio.on('check_job_status')
def check_status(job_id):
    status = async_queue.get_job_status(job_id)
    emit('job_status', {
        'progress': status.progress,
        'stage': status.current_stage,
        'eta': status.eta_seconds
    })

[4] DEVELOPER WORKFLOWS

[4.1] SETUP & INSTALLATION

Initial Setup:

# 1. Clone repository
git clone --recursive https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1.git
cd orfeas

# 2. Create Python virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1  # Windows
source .venv/bin/activate   # Linux/Mac

# 3. Install dependencies
cd backend
pip install -r requirements.txt
pip install -r requirements-production.txt  # For production features

# 4. Download Hunyuan3D-2.1 models
python download_models.py

# 5. Configure environment
copy .env.example .env
# Edit .env with your settings

# 6. Test installation
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

[4.2] DEVELOPMENT COMMANDS

Backend Server:

# Start backend (development)
cd backend
python main.py

# Start backend (production mode)
$env:FLASK_ENV="production"
python main.py

# Start with monitoring
docker-compose -f monitoring_stack/docker-compose-monitoring.yml up -d
python main.py

# Run tests
pytest                    # All tests
pytest -m unit           # Unit tests only
pytest -m integration    # Integration tests
pytest --cov=.           # With coverage

Docker Deployment:

# Full stack with GPU support
docker-compose up -d

# Build and restart
docker-compose up -d --build

# View logs
docker-compose logs -f backend
docker-compose logs -f frontend

# Stop all services
docker-compose down

# Monitor GPU usage
docker exec orfeas-backend-production nvidia-smi

[4.3] TESTING WORKFLOWS

Test Organization:

backend/tests/
├ conftest.py                    # Shared fixtures
├ unit/                          # Fast isolated tests
   ├ test_validation.py         # Input validation
   ├ test_config.py             # Configuration
   └ test_utils.py              # Utility functions
├ integration/                   # API integration tests
   ├ test_api_endpoints.py      # All endpoints
   ├ test_workflow.py           # Complete workflows
   └ test_formats.py            # Format conversion
├ security/                      # Security tests
   ├ test_security_fixes.py     # Security fixes (26 tests)
   └ test_vulnerabilities.py    # Attack vectors
└ performance/                   # Load/benchmark tests
    ├ test_load.py               # Concurrent requests
    └ test_benchmarks.py         # Performance metrics

Run Specific Tests:

# Security validation
pytest tests/security/test_security_fixes.py -v

# API integration
pytest tests/integration/test_api_endpoints.py

# Load testing
pytest tests/performance/test_load.py -v -s

# Coverage report
pytest --cov=backend --cov-report=html
start htmlcov/index.html  # View coverage

[4.3.1] TQM (TOTAL QUALITY MANAGEMENT) AUDIT TECHNIQUES

COMPREHENSIVE QUALITY AUDIT FRAMEWORK:

# backend/tqm_audit_system.py
class TQMAuditSystem:
    """
    Advanced Total Quality Management audit system for enterprise operations
    """

    def __init__(self):
        self.audit_standards = {
            'iso_9001_2015': 'Quality Management Systems',
            'iso_27001_2022': 'Information Security Management',
            'soc2_type2': 'Security and Availability Controls',
            'six_sigma': 'Process Improvement and Defect Reduction',
            'cmmi_level5': 'Capability Maturity Model Integration',
            'lean_manufacturing': 'Waste Elimination and Efficiency'
        }

        self.quality_dimensions = {
            'performance': 'Speed, accuracy, and reliability metrics',
            'features': 'Functionality and capability assessment',
            'reliability': 'Consistency and error rate analysis',
            'conformance': 'Standards compliance verification',
            'durability': 'Long-term operation stability',
            'serviceability': 'Support and maintenance quality',
            'aesthetics': 'User experience and interface quality',
            'perceived_quality': 'Customer satisfaction metrics'
        }

    def conduct_comprehensive_quality_audit(self, audit_scope: str) -> Dict[str, Any]:
        """Execute comprehensive TQM audit across all quality dimensions"""

        audit_results = {
            'audit_metadata': {
                'audit_id': self.generate_audit_id(),
                'audit_scope': audit_scope,
                'audit_date': datetime.utcnow().isoformat(),
                'auditor_team': self.get_audit_team(),
                'audit_standards': list(self.audit_standards.keys())
            },
            'quality_assessments': {},
            'compliance_verification': {},
            'performance_benchmarks': {},
            'improvement_recommendations': {},
            'risk_assessments': {}
        }

        # 1. Performance Quality Audit
        audit_results['quality_assessments']['performance'] = self.audit_performance_quality()

        # 2. Process Quality Audit
        audit_results['quality_assessments']['process'] = self.audit_process_quality()

        # 3. Product Quality Audit
        audit_results['quality_assessments']['product'] = self.audit_product_quality()

        # 4. Service Quality Audit
        audit_results['quality_assessments']['service'] = self.audit_service_quality()

        # 5. Compliance Quality Audit
        audit_results['compliance_verification'] = self.audit_compliance_status()

        # 6. Security Quality Audit
        audit_results['quality_assessments']['security'] = self.audit_security_quality()

        # 7. AI Model Quality Audit
        audit_results['quality_assessments']['ai_models'] = self.audit_ai_model_quality()

        return audit_results

    def audit_performance_quality(self) -> Dict[str, Any]:
        """Audit system performance against quality benchmarks"""

        performance_metrics = {
            'response_time_analysis': {
                'api_endpoints': self.measure_api_response_times(),
                'ai_generation_speed': self.measure_generation_performance(),
                'database_query_performance': self.measure_db_performance(),
                'quality_targets': {
                    'api_p95_response_time': '<500ms',
                    'generation_completion_time': '<60s',
                    'database_query_time': '<100ms'
                }
            },
            'throughput_analysis': {
                'concurrent_users_supported': self.measure_concurrent_capacity(),
                'requests_per_second': self.measure_rps_capacity(),
                'resource_utilization': self.measure_resource_efficiency(),
                'quality_targets': {
                    'concurrent_users': '>1000',
                    'requests_per_second': '>100',
                    'cpu_utilization_efficiency': '>80%'
                }
            },
            'reliability_metrics': {
                'uptime_percentage': self.calculate_system_uptime(),
                'error_rates': self.analyze_error_patterns(),
                'mean_time_to_recovery': self.calculate_mttr(),
                'quality_targets': {
                    'system_uptime': '>99.95%',
                    'error_rate': '<0.1%',
                    'mttr': '<15min'
                }
            }
        }

        return {
            'metrics': performance_metrics,
            'quality_score': self.calculate_performance_quality_score(performance_metrics),
            'compliance_status': self.verify_performance_compliance(performance_metrics),
            'improvement_areas': self.identify_performance_improvements(performance_metrics)
        }

    def audit_process_quality(self) -> Dict[str, Any]:
        """Audit business processes for quality, efficiency, and compliance"""

        process_audit = {
            'development_processes': {
                'code_review_compliance': self.audit_code_review_process(),
                'testing_coverage_analysis': self.audit_testing_processes(),
                'deployment_process_quality': self.audit_deployment_practices(),
                'documentation_standards': self.audit_documentation_quality()
            },
            'operational_processes': {
                'incident_management': self.audit_incident_processes(),
                'change_management': self.audit_change_control(),
                'monitoring_effectiveness': self.audit_monitoring_processes(),
                'backup_recovery_procedures': self.audit_disaster_recovery()
            },
            'quality_control_processes': {
                'input_validation_procedures': self.audit_input_validation(),
                'output_quality_verification': self.audit_output_quality(),
                'automated_testing_pipeline': self.audit_ci_cd_quality(),
                'user_acceptance_testing': self.audit_uat_processes()
            }
        }

        return {
            'process_assessments': process_audit,
            'process_maturity_level': self.assess_process_maturity(),
            'compliance_gaps': self.identify_process_gaps(),
            'optimization_opportunities': self.identify_process_optimizations()
        }

    def audit_ai_model_quality(self) -> Dict[str, Any]:
        """Comprehensive audit of AI model quality and performance"""

        ai_model_audit = {
            'model_accuracy_assessment': {
                'hunyuan3d_accuracy': self.measure_3d_generation_accuracy(),
                'video_generation_quality': self.measure_video_quality(),
                'code_generation_accuracy': self.measure_code_quality(),
                'benchmark_comparisons': self.compare_against_benchmarks()
            },
            'model_bias_analysis': {
                'demographic_bias_testing': self.test_demographic_bias(),
                'cultural_bias_assessment': self.assess_cultural_bias(),
                'fairness_metrics': self.calculate_fairness_metrics(),
                'bias_mitigation_effectiveness': self.assess_bias_mitigation()
            },
            'model_robustness_testing': {
                'adversarial_input_resistance': self.test_adversarial_robustness(),
                'edge_case_handling': self.test_edge_cases(),
                'input_variation_stability': self.test_input_stability(),
                'error_handling_quality': self.test_error_scenarios()
            },
            'model_explainability': {
                'decision_transparency': self.assess_model_transparency(),
                'output_justification': self.test_explanation_quality(),
                'interpretability_scores': self.measure_interpretability(),
                'user_understanding_metrics': self.measure_user_comprehension()
            }
        }

        return {
            'ai_quality_metrics': ai_model_audit,
            'overall_ai_quality_score': self.calculate_ai_quality_score(ai_model_audit),
            'regulatory_compliance': self.verify_ai_compliance(),
            'ethical_ai_assessment': self.assess_ethical_compliance()
        }

    def generate_quality_audit_report(self, audit_results: Dict) -> Dict[str, Any]:
        """Generate comprehensive TQM audit report with actionable insights"""

        executive_summary = {
            'overall_quality_score': self.calculate_overall_quality_score(audit_results),
            'compliance_status': self.summarize_compliance_status(audit_results),
            'critical_findings': self.identify_critical_issues(audit_results),
            'improvement_priorities': self.prioritize_improvements(audit_results),
            'investment_recommendations': self.recommend_quality_investments(audit_results)
        }

        detailed_findings = {
            'quality_dimension_scores': self.score_quality_dimensions(audit_results),
            'process_maturity_assessment': self.assess_process_maturity_levels(audit_results),
            'risk_analysis': self.analyze_quality_risks(audit_results),
            'benchmark_comparisons': self.compare_industry_benchmarks(audit_results),
            'trend_analysis': self.analyze_quality_trends(audit_results)
        }

        action_plan = {
            'immediate_actions': self.define_immediate_actions(audit_results),
            'short_term_improvements': self.plan_short_term_improvements(audit_results),
            'long_term_strategic_initiatives': self.plan_strategic_initiatives(audit_results),
            'resource_requirements': self.estimate_resource_needs(audit_results),
            'success_metrics': self.define_success_metrics(audit_results)
        }

        return {
            'report_metadata': {
                'report_id': self.generate_report_id(),
                'generation_date': datetime.utcnow().isoformat(),
                'report_version': '1.0',
                'next_audit_due': self.calculate_next_audit_date()
            },
            'executive_summary': executive_summary,
            'detailed_findings': detailed_findings,
            'action_plan': action_plan,
            'appendices': {
                'raw_audit_data': audit_results,
                'compliance_evidence': self.compile_compliance_evidence(),
                'quality_metrics_history': self.retrieve_historical_metrics()
            }
        }

CONTINUOUS QUALITY MONITORING:

# backend/continuous_quality_monitor.py
class ContinuousQualityMonitor:
    """
    Real-time quality monitoring and continuous improvement system
    """

    def __init__(self):
        self.quality_thresholds = {
            'performance': {'warning': 0.8, 'critical': 0.6},
            'reliability': {'warning': 0.95, 'critical': 0.90},
            'security': {'warning': 0.9, 'critical': 0.8},
            'compliance': {'warning': 0.98, 'critical': 0.95}
        }

    def real_time_quality_monitoring(self):
        """Continuous monitoring of quality metrics with real-time alerting"""

        while True:
            try:
                # Collect real-time quality metrics
                current_metrics = {
                    'performance_metrics': self.collect_performance_metrics(),
                    'reliability_metrics': self.collect_reliability_metrics(),
                    'security_metrics': self.collect_security_metrics(),
                    'compliance_metrics': self.collect_compliance_metrics(),
                    'user_satisfaction_metrics': self.collect_satisfaction_metrics()
                }

                # Analyze quality trends
                quality_trends = self.analyze_quality_trends(current_metrics)

                # Detect quality degradation
                quality_issues = self.detect_quality_degradation(current_metrics)

                # Trigger automated responses
                if quality_issues:
                    self.trigger_quality_response(quality_issues)

                # Generate quality alerts
                alerts = self.generate_quality_alerts(current_metrics, quality_trends)

                if alerts:
                    self.send_quality_alerts(alerts)

                # Update quality dashboard
                self.update_quality_dashboard(current_metrics, quality_trends)

                time.sleep(30)  # Monitor every 30 seconds

            except Exception as e:
                logger.error(f"[TQM] Quality monitoring error: {e}")
                time.sleep(60)

    def automated_quality_improvement(self, quality_metrics: Dict) -> Dict:
        """Automated quality improvement based on continuous monitoring"""

        improvement_actions = {
            'performance_optimizations': [],
            'reliability_enhancements': [],
            'security_strengthening': [],
            'compliance_adjustments': []
        }

        # Performance improvements
        if quality_metrics['performance_score'] < self.quality_thresholds['performance']['warning']:
            improvement_actions['performance_optimizations'].extend([
                'enable_additional_caching',
                'optimize_database_queries',
                'scale_horizontal_instances',
                'implement_connection_pooling'
            ])

        # Reliability improvements
        if quality_metrics['reliability_score'] < self.quality_thresholds['reliability']['warning']:
            improvement_actions['reliability_enhancements'].extend([
                'implement_circuit_breakers',
                'enhance_error_handling',
                'improve_health_checks',
                'strengthen_monitoring'
            ])

        # Security improvements
        if quality_metrics['security_score'] < self.quality_thresholds['security']['warning']:
            improvement_actions['security_strengthening'].extend([
                'update_security_policies',
                'enhance_access_controls',
                'strengthen_encryption',
                'improve_audit_logging'
            ])

        return improvement_actions

QUALITY AUDIT AUTOMATION:

# backend/automated_audit_scheduler.py
class AutomatedAuditScheduler:
    """
    Automated scheduling and execution of quality audits
    """

    def __init__(self):
        self.audit_schedule = {
            'daily_audits': ['performance', 'security_baseline'],
            'weekly_audits': ['process_compliance', 'ai_model_quality'],
            'monthly_audits': ['comprehensive_tqm', 'regulatory_compliance'],
            'quarterly_audits': ['strategic_quality_review', 'external_audit_prep'],
            'annual_audits': ['full_certification_audit', 'quality_system_review']
        }

    def schedule_automated_audits(self):
        """Schedule and execute automated quality audits"""

        scheduler = BackgroundScheduler()

        # Daily audits
        scheduler.add_job(
            func=self.execute_performance_audit,
            trigger='cron',
            hour=2,  # 2 AM daily
            minute=0,
            id='daily_performance_audit'
        )

        # Weekly audits
        scheduler.add_job(
            func=self.execute_comprehensive_audit,
            trigger='cron',
            day_of_week='sun',
            hour=3,  # 3 AM Sunday
            minute=0,
            id='weekly_comprehensive_audit'
        )

        # Monthly audits
        scheduler.add_job(
            func=self.execute_tqm_audit,
            trigger='cron',
            day=1,  # First day of month
            hour=4,  # 4 AM
            minute=0,
            id='monthly_tqm_audit'
        )

        scheduler.start()

[4.4] DEBUGGING

Enable Debug Logging:

# backend/main.py
logging.basicConfig(level=logging.DEBUG)

# Or via environment
$env:LOG_LEVEL="DEBUG"
python main.py

GPU Debugging:

# Check GPU availability
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU device: {torch.cuda.get_device_name(0)}")
print(f"VRAM total: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")

# Monitor GPU memory
from gpu_manager import get_gpu_manager
gpu_mgr = get_gpu_manager()
print(gpu_mgr.get_gpu_stats())

Common Issues with Automated Detection:

# Issue: XFORMERS DLL crash (0xc0000139)
# Fix: Already handled via XFORMERS_DISABLED=1 in hunyuan_integration.py
# Auto-Detection: Pattern 'xformers.*DLL.*0xc0000139' triggers automatic XFORMERS disabling

# Issue: GPU out of memory
# Fix: Reduce concurrent jobs or enable low_vram_mode
$env:MAX_CONCURRENT_JOBS=1
$env:GPU_MEMORY_LIMIT=0.7
# Auto-Detection: OutOfMemoryError triggers automatic cache clearing and job reduction

# Issue: Models not found
# Fix: Verify Hunyuan3D-2.1 path
python backend/download_models.py
# Auto-Detection: FileNotFoundError with 'hunyuan3d' triggers automatic model download

# Issue: WebSocket disconnects
# Fix: Check CORS origins and ping timeout
$env:CORS_ORIGINS="*"
$env:WS_PING_TIMEOUT=60
# Auto-Detection: ConnectionError with 'websocket' triggers automatic CORS fix and restart

# Issue: Context processing failures
# Fix: Clear context cache and reset learning system
$env:CONTEXT_CACHE_SIZE=500
$env:CONTEXT_SIMILARITY_THRESHOLD=0.9
# Auto-Detection: Context-related errors trigger cache cleanup and threshold adjustment

# Issue: AI agent coordination failures
# Fix: Restart agent coordinator and verify authentication
$env:AGENT_AUTH_ENABLED=true
$env:QUALITY_AGENT_ENABLED=true
# Auto-Detection: Agent communication errors trigger automatic agent restart

# Issue: Text-to-Image generation failures
# Fix: Switch to alternative model and verify API keys
$env:TEXT_TO_IMAGE_MODEL=stable_diffusion_xl
$env:DALLE3_API_KEY=verify_key
$env:TEXT_TO_IMAGE_QUALITY=medium
# Auto-Detection: Image generation errors trigger model switching

# Issue: Text-to-Speech synthesis failures
# Fix: Switch TTS engine and verify voice models
$env:TTS_MODEL=azure_tts
$env:TTS_VOICE_ID=default
$env:AUDIO_QUALITY=medium
# Auto-Detection: TTS errors trigger engine switching and voice validation

# Issue: Speech-to-Text transcription errors
# Fix: Switch recognition engine and adjust audio quality
$env:SPEECH_MODEL=azure_speech
$env:SPEECH_LANGUAGE=en-US
$env:ENABLE_REAL_TIME_TRANSCRIPTION=false
# Auto-Detection: STT errors trigger engine switching and language detection

# Issue: Audio/Image file format compatibility
# Fix: Enable format conversion and validate file types
$env:ENABLE_AUDIO_CONVERSION=true
$env:ENABLE_IMAGE_CONVERSION=true
$env:MAX_AUDIO_DURATION=180
# Auto-Detection: Format errors trigger automatic conversion

# Issue: Multimedia model loading failures
# Fix: Download missing models and verify storage space
python backend/download_multimedia_models.py
$env:MULTIMEDIA_CACHE_SIZE=10
$env:MODEL_DOWNLOAD_RETRY=3
# Auto-Detection: Model loading errors trigger automatic download

# Issue: Model performance degradation
# Fix: Trigger model optimization and selection
$env:AUTO_MODEL_SWITCHING=true
$env:ADAPTIVE_MODEL_SELECTION=true
# Auto-Detection: Performance metrics below threshold trigger model optimization

# Issue: Security policy violations
# Fix: Update security configuration and audit logs
$env:SECURITY_MONITORING_LEVEL=HIGH
$env:AUDIT_LOGGING_ENABLED=true
# Auto-Detection: Security violations trigger automatic policy enforcement

AGENT-SPECIFIC DEBUGGING PATTERNS:

# Issue: Agent authentication failures
# Fix: Verify agent credentials and regenerate API keys
$env:AGENT_AUTH_ENABLED=true
$env:AGENT_API_KEY_REGENERATION=true
$env:AGENT_SECURITY_LEVEL="enterprise"
# Auto-Detection: 401/403 errors from agent endpoints trigger authentication debugging

# Issue: Agent communication timeouts
# Fix: Adjust timeout settings and check network connectivity
$env:AGENT_COMMUNICATION_TIMEOUT=30
$env:AGENT_RETRY_ATTEMPTS=3
$env:AGENT_HEARTBEAT_INTERVAL=10
# Auto-Detection: Communication timeout errors trigger automatic retry with extended timeout

# Issue: Agent load balancing failures
# Fix: Restart load balancer and redistribute agent instances
$env:AGENT_LOAD_BALANCING=true
$env:AGENT_HEALTH_CHECK_INTERVAL=5
$env:AGENT_FAILOVER_ENABLED=true
# Auto-Detection: Agent health check failures trigger automatic load redistribution

# Issue: Agent performance degradation
# Fix: Scale agent instances and optimize resource allocation
$env:AGENT_AUTO_SCALING=true
$env:AGENT_PERFORMANCE_THRESHOLD=0.8
$env:AGENT_RESOURCE_OPTIMIZATION=true
# Auto-Detection: Agent performance below 80% triggers automatic scaling

# Issue: Agent workflow orchestration failures
# Fix: Reset workflow state and restart orchestration engine
$env:WORKFLOW_ORCHESTRATION_ENABLED=true
$env:WORKFLOW_STATE_RECOVERY=true
$env:WORKFLOW_CHECKPOINT_INTERVAL=30
# Auto-Detection: Workflow interruption triggers automatic state recovery

# Issue: Agent memory leaks or resource exhaustion
# Fix: Restart agents and implement resource cleanup
$env:AGENT_MEMORY_LIMIT="2Gi"
$env:AGENT_RESOURCE_CLEANUP_INTERVAL=300
$env:AGENT_GRACEFUL_SHUTDOWN_TIMEOUT=30
# Auto-Detection: Agent memory usage above threshold triggers restart

# Issue: Agent learning system failures
# Fix: Reset learning state and rebuild experience buffer
$env:AGENT_LEARNING_ENABLED=true
$env:AGENT_EXPERIENCE_BUFFER_SIZE=1000
$env:AGENT_LEARNING_CHECKPOINT_INTERVAL=60
# Auto-Detection: Learning system errors trigger experience buffer reset

# Issue: Agent deployment failures in Kubernetes
# Fix: Check deployment manifests and resource availability
$env:KUBERNETES_AGENT_NAMESPACE="orfeas-agents"
$env:KUBERNETES_AGENT_REPLICAS=3
$env:KUBERNETES_AGENT_AUTO_RECOVERY=true
# Auto-Detection: Kubernetes deployment errors trigger manifest validation

# Issue: Agent service mesh connectivity issues
# Fix: Restart service mesh components and verify configuration
$env:AGENT_SERVICE_MESH_ENABLED=true
$env:ISTIO_SIDECAR_INJECTION=true
$env:SERVICE_MESH_TELEMETRY=true
# Auto-Detection: Service mesh errors trigger sidecar restart

# Issue: Agent monitoring and observability failures
# Fix: Restart monitoring agents and verify metric collection
$env:AGENT_MONITORING_ENABLED=true
$env:PROMETHEUS_AGENT_METRICS=true
$env:GRAFANA_AGENT_DASHBOARDS=true
# Auto-Detection: Missing agent metrics trigger monitoring system restart

AGENT DEBUGGING INTEGRATION:

# Enable agent-specific debugging in development
from agent_api import OrfeasAIAgent
from agent_monitoring import AgentPerformanceMonitor
from agent_discovery import AgentServiceDiscovery
from problem_detector import IntelligentProblemDetector

# Initialize agent debugging system
agent_monitor = AgentPerformanceMonitor()
agent_discovery = AgentServiceDiscovery()
problem_detector = IntelligentProblemDetector()

# Manual agent debugging
def debug_with_agent_analysis(agent_issue_or_exception):
    """Debug with comprehensive agent analysis"""

    # Build agent context
    agent_context = {
        'issue_type': type(agent_issue_or_exception).__name__,
        'issue_message': str(agent_issue_or_exception),
        'agent_states': agent_monitor.get_agent_states(),
        'agent_health': agent_discovery.check_agent_health(),
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Analyze agent performance
    performance_analysis = agent_monitor.analyze_agent_performance()

    print(f"[AGENT-DEBUG] Agent Performance Analysis:")
    print(f"  - Total Agents: {performance_analysis['total_agents']}")
    print(f"  - Healthy Agents: {performance_analysis['healthy_agents']}")
    print(f"  - Average Response Time: {performance_analysis['avg_response_time']:.2f}ms")
    print(f"  - Success Rate: {performance_analysis['success_rate']:.2f}%")

    # Check agent discovery
    agent_status = agent_discovery.get_discovery_status()
    print(f"[AGENT-DEBUG] Agent Discovery Status:")
    for agent_type, agents in agent_status.items():
        print(f"  - {agent_type}: {len(agents)} instances")
        for agent in agents:
            print(f"    * {agent['id']}: {agent['status']} ({agent['last_seen']})")

    # Detect agent-specific problems
    agent_problems = problem_detector.detect_agent_problems(agent_context)
    print(f"[AGENT-DEBUG] Detected Agent Problems: {len(agent_problems)}")
    for problem in agent_problems:
        print(f"  - {problem['category']}: {problem['description']}")

    # Apply automated agent fixes
    if agent_problems:
        print("[AGENT-DEBUG] Applying automated agent fixes...")
        fix_results = problem_detector.auto_fix_agent_problems(agent_problems)
        print(f"[AGENT-DEBUG] Applied fixes: {fix_results}")

    return {
        'performance_analysis': performance_analysis,
        'agent_status': agent_status,
        'detected_problems': agent_problems,
        'fix_results': fix_results if 'fix_results' in locals() else None
    }

# Agent-specific problem detection patterns
def detect_agent_specific_issues():
    """Detect agent-specific issues with automated patterns"""

    agent_issues = []

    # Check agent authentication status
    auth_status = agent_discovery.check_agent_authentication()
    for agent_id, auth_result in auth_status.items():
        if not auth_result['authenticated']:
            agent_issues.append({
                'category': 'AGENT_AUTHENTICATION',
                'severity': 'HIGH',
                'description': f"Agent {agent_id} authentication failed",
                'automated_fix': True,
                'solutions': ['regenerate_api_key', 'verify_credentials']
            })

    # Check agent communication
    comm_status = agent_discovery.check_agent_communication()
    for agent_id, comm_result in comm_status.items():
        if comm_result['response_time'] > 5000:  # > 5 seconds
            agent_issues.append({
                'category': 'AGENT_COMMUNICATION',
                'severity': 'MEDIUM',
                'description': f"Agent {agent_id} slow communication: {comm_result['response_time']}ms",
                'automated_fix': True,
                'solutions': ['restart_agent', 'optimize_network']
            })

    # Check agent resource usage
    resource_status = agent_monitor.check_agent_resources()
    for agent_id, resource_usage in resource_status.items():
        if resource_usage['memory_percent'] > 90:
            agent_issues.append({
                'category': 'AGENT_RESOURCE',
                'severity': 'HIGH',
                'description': f"Agent {agent_id} high memory usage: {resource_usage['memory_percent']}%",
                'automated_fix': True,
                'solutions': ['restart_agent', 'increase_memory_limit']
            })

    return agent_issues

# Auto-fix agent issues
def auto_fix_agent_issues(agent_issues: List[Dict]) -> Dict[str, Any]:
    """Automatically fix agent-specific issues"""

    fix_results = {
        'attempted_fixes': [],
        'successful_fixes': [],
        'failed_fixes': []
    }

    for issue in agent_issues:
        if issue.get('automated_fix', False):
            try:
                category = issue['category']
                solutions = issue.get('solutions', [])

                for solution in solutions:
                    if category == 'AGENT_AUTHENTICATION':
                        result = fix_agent_authentication_issue(solution, issue)
                    elif category == 'AGENT_COMMUNICATION':
                        result = fix_agent_communication_issue(solution, issue)
                    elif category == 'AGENT_RESOURCE':
                        result = fix_agent_resource_issue(solution, issue)
                    else:
                        result = fix_generic_agent_issue(solution, issue)

                    if result['success']:
                        fix_results['successful_fixes'].append({
                            'issue': issue,
                            'solution': solution,
                            'result': result
                        })
                        break  # Success, no need to try other solutions

                if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']):
                    fix_results['failed_fixes'].append(issue)

            except Exception as e:
                logger.error(f"[AGENT-DEBUG] Failed to fix agent issue {issue['category']}: {e}")
                fix_results['failed_fixes'].append(issue)

    return fix_results

def fix_agent_authentication_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent authentication issues"""

    if solution == 'regenerate_api_key':
        # Regenerate API key for agent
        agent_id = issue.get('agent_id')
        new_api_key = agent_discovery.regenerate_agent_api_key(agent_id)
        return {'success': True, 'details': f'New API key generated for {agent_id}'}

    elif solution == 'verify_credentials':
        # Verify and update agent credentials
        agent_id = issue.get('agent_id')
        verification_result = agent_discovery.verify_agent_credentials(agent_id)
        return {'success': verification_result, 'details': 'Credentials verified and updated'}

    return {'success': False, 'details': f'Unknown authentication solution: {solution}'}

def fix_agent_communication_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent communication issues"""

    if solution == 'restart_agent':
        # Restart specific agent
        agent_id = issue.get('agent_id')
        restart_result = agent_discovery.restart_agent(agent_id)
        return {'success': restart_result, 'details': f'Agent {agent_id} restarted'}

    elif solution == 'optimize_network':
        # Optimize network configuration for agent
        network_optimization = agent_discovery.optimize_agent_network()
        return {'success': True, 'details': 'Agent network optimized'}

    return {'success': False, 'details': f'Unknown communication solution: {solution}'}

def fix_agent_resource_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix agent resource issues"""

    if solution == 'restart_agent':
        # Restart agent to free resources
        agent_id = issue.get('agent_id')
        restart_result = agent_discovery.restart_agent(agent_id)
        return {'success': restart_result, 'details': f'Agent {agent_id} restarted for resource cleanup'}

    elif solution == 'increase_memory_limit':
        # Increase memory limit for agent
        agent_id = issue.get('agent_id')
        limit_result = agent_discovery.increase_agent_memory_limit(agent_id)
        return {'success': limit_result, 'details': f'Memory limit increased for {agent_id}'}

    return {'success': False, 'details': f'Unknown resource solution: {solution}'}

Problem Detection Integration:

# Enable automated problem detection in development
from problem_detector import IntelligentProblemDetector
from problem_prevention import ProactiveProblemPrevention

# Initialize problem detection system
problem_detector = IntelligentProblemDetector()
prevention_system = ProactiveProblemPrevention()

# Start proactive monitoring
if os.getenv('ENABLE_PROACTIVE_MONITORING', 'true').lower() == 'true':
    prevention_system.start_continuous_monitoring()

# Manual problem detection for debugging
def debug_with_problem_detection(error_or_exception):
    """Debug with intelligent problem detection"""
    error_context = {
        'error_type': type(error_or_exception).__name__,
        'error_message': str(error_or_exception),
        'traceback': traceback.format_exc() if hasattr(error_or_exception, '__traceback__') else None,
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Detect problems
    detection_result = problem_detector.detect_and_classify_problems(error_context)

    print(f"[DEBUG] Detected Problems: {len(detection_result['detected_problems'])}")
    for problem in detection_result['detected_problems']:
        print(f"  - {problem['category']}: {problem['pattern']} (Confidence: {problem.get('confidence', 0):.2f})")

    # Attempt fixes
    if detection_result['automated_fixes_available']:
        fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])
        print(f"[DEBUG] Fix Results: {fix_result}")

    return detection_result

[4.5] ADVANCED PROBLEM IDENTIFICATION & AUTOMATED FIXING

INTELLIGENT PROBLEM DETECTION SYSTEM:

# backend/problem_detector.py
class IntelligentProblemDetector:
    """
    Advanced problem identification and automated fixing system
    """

    def __init__(self):
        self.problem_patterns = self.load_problem_database()
        self.solution_engine = AutomatedSolutionEngine()
        self.learning_system = ProblemLearningSystem()

    def detect_and_classify_problems(self, error_context: Dict) -> Dict[str, Any]:
        """Detect and classify problems with confidence scoring"""

        detected_problems = []

        # 1. Error pattern matching
        pattern_matches = self.match_error_patterns(error_context)

        # 2. System state analysis
        system_issues = self.analyze_system_state()

        # 3. Performance degradation detection
        performance_issues = self.detect_performance_issues()

        # 4. Resource constraint analysis
        resource_issues = self.analyze_resource_constraints()

        # 5. Integration point failures
        integration_issues = self.check_integration_points()

        all_issues = {
            'error_patterns': pattern_matches,
            'system_state': system_issues,
            'performance': performance_issues,
            'resources': resource_issues,
            'integrations': integration_issues
        }

        # Classify and prioritize problems
        classified_problems = self.classify_problems(all_issues)

        return {
            'detected_problems': classified_problems,
            'confidence_scores': self.calculate_confidence_scores(classified_problems),
            'severity_levels': self.assess_severity(classified_problems),
            'automated_fixes_available': self.check_automated_fixes(classified_problems)
        }

    def match_error_patterns(self, error_context: Dict) -> List[Dict]:
        """Match errors against known problem patterns"""

        patterns = [
            {
                'pattern': 'torch.cuda.OutOfMemoryError',
                'category': 'GPU_MEMORY',
                'severity': 'HIGH',
                'automated_fix': True,
                'solutions': ['reduce_batch_size', 'enable_cpu_fallback', 'clear_cache']
            },
            {
                'pattern': 'FileNotFoundError.*hunyuan3d',
                'category': 'MODEL_FILES',
                'severity': 'CRITICAL',
                'automated_fix': True,
                'solutions': ['download_models', 'verify_paths', 'check_permissions']
            },
            {
                'pattern': 'ConnectionError.*websocket',
                'category': 'NETWORK',
                'severity': 'MEDIUM',
                'automated_fix': True,
                'solutions': ['restart_websocket', 'check_cors', 'verify_ports']
            },
            {
                'pattern': 'xformers.*DLL.*0xc0000139',
                'category': 'DEPENDENCY',
                'severity': 'HIGH',
                'automated_fix': True,
                'solutions': ['disable_xformers', 'reinstall_dependencies']
            }
        ]

        matches = []
        error_message = error_context.get('error_message', '')

        for pattern in patterns:
            if re.search(pattern['pattern'], error_message, re.IGNORECASE):
                matches.append({
                    **pattern,
                    'matched_text': error_message,
                    'confidence': self.calculate_pattern_confidence(pattern, error_context)
                })

        return matches

    def auto_fix_problems(self, detected_problems: List[Dict]) -> Dict[str, Any]:
        """Automatically fix detected problems"""

        fix_results = {
            'attempted_fixes': [],
            'successful_fixes': [],
            'failed_fixes': [],
            'manual_intervention_required': []
        }

        for problem in detected_problems:
            if problem.get('automated_fix', False):
                fix_result = self.attempt_automated_fix(problem)
                fix_results['attempted_fixes'].append({
                    'problem': problem,
                    'result': fix_result
                })

                if fix_result['success']:
                    fix_results['successful_fixes'].append(problem)
                else:
                    fix_results['failed_fixes'].append(problem)
            else:
                fix_results['manual_intervention_required'].append(problem)

        return fix_results

    def attempt_automated_fix(self, problem: Dict) -> Dict[str, Any]:
        """Attempt to automatically fix a specific problem"""

        category = problem['category']
        solutions = problem.get('solutions', [])

        for solution in solutions:
            try:
                if category == 'GPU_MEMORY':
                    result = self.fix_gpu_memory_issue(solution)
                elif category == 'MODEL_FILES':
                    result = self.fix_model_files_issue(solution)
                elif category == 'NETWORK':
                    result = self.fix_network_issue(solution)
                elif category == 'DEPENDENCY':
                    result = self.fix_dependency_issue(solution)
                else:
                    result = self.fix_generic_issue(solution, problem)

                if result['success']:
                    return {
                        'success': True,
                        'solution_applied': solution,
                        'details': result['details']
                    }

            except Exception as e:
                logger.warning(f"[ORFEAS] Failed to apply solution {solution}: {e}")
                continue

        return {
            'success': False,
            'attempted_solutions': solutions,
            'error': 'All automated solutions failed'
        }

    def fix_gpu_memory_issue(self, solution: str) -> Dict[str, Any]:
        """Fix GPU memory related issues"""

        if solution == 'reduce_batch_size':
            # Reduce concurrent processing
            os.environ['MAX_CONCURRENT_JOBS'] = '1'
            return {'success': True, 'details': 'Reduced concurrent jobs to 1'}

        elif solution == 'enable_cpu_fallback':
            # Enable CPU fallback mode
            os.environ['ENABLE_CPU_FALLBACK'] = 'true'
            return {'success': True, 'details': 'Enabled CPU fallback processing'}

        elif solution == 'clear_cache':
            # Clear GPU cache
            import torch
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
                torch.cuda.synchronize()
            return {'success': True, 'details': 'Cleared GPU cache'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_model_files_issue(self, solution: str) -> Dict[str, Any]:
        """Fix model files related issues"""

        if solution == 'download_models':
            # Attempt to download missing models
            try:
                subprocess.run(['python', 'backend/download_models.py'], check=True)
                return {'success': True, 'details': 'Models downloaded successfully'}
            except subprocess.CalledProcessError as e:
                return {'success': False, 'details': f'Model download failed: {e}'}

        elif solution == 'verify_paths':
            # Verify and fix model paths
            hunyuan_path = os.getenv('HUNYUAN3D_PATH', '../Hunyuan3D-2.1')
            if os.path.exists(hunyuan_path):
                return {'success': True, 'details': 'Model paths verified'}
            else:
                return {'success': False, 'details': f'Model path not found: {hunyuan_path}'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_network_issue(self, solution: str) -> Dict[str, Any]:
        """Fix network and connectivity issues"""

        if solution == 'restart_websocket':
            # Restart WebSocket connections
            try:
                # Implementation depends on your WebSocket framework
                return {'success': True, 'details': 'WebSocket connections restarted'}
            except Exception as e:
                return {'success': False, 'details': f'WebSocket restart failed: {e}'}

        elif solution == 'check_cors':
            # Fix CORS configuration
            os.environ['CORS_ORIGINS'] = '*'
            return {'success': True, 'details': 'CORS origins updated'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

    def fix_dependency_issue(self, solution: str) -> Dict[str, Any]:
        """Fix dependency and environment issues"""

        if solution == 'disable_xformers':
            # Disable XFORMERS to prevent DLL crashes
            os.environ['XFORMERS_DISABLED'] = '1'
            return {'success': True, 'details': 'XFORMERS disabled'}

        elif solution == 'reinstall_dependencies':
            # Attempt to reinstall problematic dependencies
            try:
                subprocess.run(['pip', 'install', '--force-reinstall', 'torch', 'torchvision'], check=True)
                return {'success': True, 'details': 'Dependencies reinstalled'}
            except subprocess.CalledProcessError as e:
                return {'success': False, 'details': f'Dependency reinstall failed: {e}'}

        return {'success': False, 'details': f'Unknown solution: {solution}'}

PROACTIVE PROBLEM PREVENTION:

# backend/problem_prevention.py
class ProactiveProblemPrevention:
    """
    Proactive problem prevention and system health monitoring
    """

    def __init__(self):
        self.health_monitors = self.setup_health_monitors()
        self.prevention_rules = self.load_prevention_rules()
        self.alert_system = AlertSystem()

    def continuous_health_monitoring(self):
        """Continuously monitor system health and prevent problems"""

        while True:
            try:
                # 1. Monitor GPU health
                gpu_health = self.monitor_gpu_health()
                if gpu_health['risk_level'] > 0.7:
                    self.prevent_gpu_issues(gpu_health)

                # 2. Monitor memory usage
                memory_health = self.monitor_memory_usage()
                if memory_health['usage_percent'] > 85:
                    self.prevent_memory_issues(memory_health)

                # 3. Monitor model performance
                model_health = self.monitor_model_performance()
                if model_health['degradation_detected']:
                    self.prevent_performance_degradation(model_health)

                # 4. Monitor file system
                fs_health = self.monitor_file_system()
                if fs_health['issues_detected']:
                    self.prevent_file_system_issues(fs_health)

                # 5. Monitor network connectivity
                network_health = self.monitor_network_health()
                if network_health['connectivity_issues']:
                    self.prevent_network_issues(network_health)

                time.sleep(30)  # Check every 30 seconds

            except Exception as e:
                logger.error(f"[ORFEAS] Health monitoring error: {e}")
                time.sleep(60)  # Wait longer on error

    def prevent_gpu_issues(self, gpu_health: Dict):
        """Prevent GPU-related problems before they occur"""

        if gpu_health['memory_usage'] > 0.9:
            # Preemptively clear cache
            import torch
            torch.cuda.empty_cache()
            logger.info("[ORFEAS] Preemptive GPU cache clear performed")

        if gpu_health['temperature'] > 80:
            # Reduce processing intensity
            os.environ['GPU_INTENSITY_LIMIT'] = '0.7'
            self.alert_system.send_alert('GPU temperature high', 'warning')

        if gpu_health['utilization'] > 0.95:
            # Queue new requests instead of processing immediately
            self.activate_request_queuing()

    def smart_error_recovery(self, error: Exception, context: Dict) -> Dict[str, Any]:
        """Intelligent error recovery with learning capabilities"""

        # 1. Analyze error context
        error_analysis = self.analyze_error_context(error, context)

        # 2. Check historical recovery patterns
        historical_solutions = self.get_historical_solutions(error_analysis)

        # 3. Apply most successful historical solution first
        if historical_solutions:
            recovery_result = self.apply_historical_solution(
                historical_solutions[0], error, context
            )
            if recovery_result['success']:
                return recovery_result

        # 4. Attempt intelligent recovery strategies
        recovery_strategies = self.generate_recovery_strategies(error_analysis)

        for strategy in recovery_strategies:
            try:
                recovery_result = self.execute_recovery_strategy(strategy, error, context)
                if recovery_result['success']:
                    # Learn from successful recovery
                    self.learn_from_recovery(error_analysis, strategy, recovery_result)
                    return recovery_result
            except Exception as recovery_error:
                logger.warning(f"[ORFEAS] Recovery strategy failed: {recovery_error}")
                continue

        # 5. Escalate to manual intervention
        return {
            'success': False,
            'recovery_attempted': True,
            'escalation_required': True,
            'manual_intervention_steps': self.generate_manual_steps(error_analysis)
        }

AUTOMATED DIAGNOSTICS INTEGRATION:

# Integration with existing error handling
@app.errorhandler(Exception)
def handle_exception(error):
    """Enhanced error handler with automated problem detection and fixing"""

    # 1. Capture comprehensive error context
    error_context = {
        'error_type': type(error).__name__,
        'error_message': str(error),
        'traceback': traceback.format_exc(),
        'request_data': request.get_json() if request.is_json else None,
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # 2. Detect and classify problems
    problem_detector = IntelligentProblemDetector()
    detection_result = problem_detector.detect_and_classify_problems(error_context)

    # 3. Attempt automated fixes
    if detection_result['automated_fixes_available']:
        fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems'])

        if fix_result['successful_fixes']:
            logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}")
            # Retry the operation if fix was successful
            return retry_operation_after_fix(fix_result)

    # 4. Smart error recovery
    prevention_system = ProactiveProblemPrevention()
    recovery_result = prevention_system.smart_error_recovery(error, error_context)

    if recovery_result['success']:
        return recovery_result['response']

    # 5. Enhanced error response with diagnostic information
    return jsonify({
        'error': str(error),
        'error_id': generate_error_id(),
        'diagnostic_info': {
            'detected_problems': detection_result['detected_problems'],
            'attempted_fixes': fix_result if 'fix_result' in locals() else None,
            'recovery_attempted': recovery_result['recovery_attempted'],
            'escalation_required': recovery_result.get('escalation_required', False)
        },
        'user_guidance': generate_user_guidance(detection_result),
        'support_info': generate_support_information(error_context)
    }), 500

TQM-SPECIFIC DEBUGGING COMMANDS:

# TQM Quality Issue Detection and Resolution

# Issue: Quality audit failures or low quality scores
# Fix: Trigger comprehensive quality audit and optimization
$env:ENABLE_TQM_AUDITS="true"
$env:TQM_AUDIT_LEVEL="enterprise"
$env:CONTINUOUS_QUALITY_MONITORING="true"
# Auto-Detection: Quality scores below threshold trigger automatic audit and improvement

# Issue: Performance quality degradation
# Fix: Enable performance optimization and monitoring
$env:ENABLE_PERFORMANCE_OPTIMIZER="true"
$env:AUTO_PERFORMANCE_TUNING="true"
$env:PERFORMANCE_THRESHOLD="0.8"
# Auto-Detection: Performance metrics below 80% trigger automatic optimization

# Issue: Reliability quality issues
# Fix: Enhance error handling and monitoring
$env:RELIABILITY_QUALITY_THRESHOLD="0.95"
$env:ERROR_RECOVERY_ENABLED="true"
$env:HEALTH_MONITORING_ENABLED="true"
# Auto-Detection: Error rates above 5% trigger reliability enhancement

# Issue: Security quality violations
# Fix: Strengthen security controls and audit logging
$env:SECURITY_HARDENING_ENABLED="true"
$env:SECURITY_MONITORING_LEVEL="HIGH"
$env:AUDIT_LOGGING_ENABLED="true"
# Auto-Detection: Security violations trigger automatic hardening

# Issue: Compliance quality failures
# Fix: Update compliance framework and validation
$env:ISO_9001_COMPLIANCE="true"
$env:SOC2_TYPE2_COMPLIANCE="true"
$env:COMPLIANCE_QUALITY_THRESHOLD="0.98"
# Auto-Detection: Compliance failures trigger automatic validation update

# Issue: User satisfaction quality issues
# Fix: Enable user feedback integration and satisfaction monitoring
$env:USER_SATISFACTION_THRESHOLD="0.85"
$env:CUSTOMER_FEEDBACK_INTEGRATION="true"
$env:QUALITY_ROI_TRACKING="true"
# Auto-Detection: User satisfaction below 85% trigger improvement actions

# Issue: Quality audit scheduling failures
# Fix: Reset audit scheduler and verify configuration
$env:AUTOMATED_AUDIT_SCHEDULING="true"
$env:DAILY_AUDITS_ENABLED="true"
$env:QUALITY_ALERT_SYSTEM="true"
# Auto-Detection: Audit scheduling errors trigger automatic scheduler restart

# Issue: Quality metrics collection failures
# Fix: Restart metrics collector and verify database connection
$env:QUALITY_METRICS_COLLECTOR="enabled"
$env:QUALITY_TREND_ANALYSIS="true"
$env:BENCHMARK_COMPARISONS="true"
# Auto-Detection: Metrics collection errors trigger collector restart

# Issue: Quality improvement actions not applied
# Fix: Enable automated quality correction and alert system
$env:AUTO_QUALITY_CORRECTION="true"
$env:PROACTIVE_QUALITY_MANAGEMENT="true"
$env:QUALITY_IMPROVEMENT_ENGINE="enabled"
# Auto-Detection: Quality degradation triggers automatic improvement actions

# Issue: TQM audit report generation failures
# Fix: Reset report generator and verify template access
$env:AUTO_AUDIT_REPORT_GENERATION="true"
$env:EXECUTIVE_DASHBOARD_ENABLED="true"
$env:STAKEHOLDER_NOTIFICATIONS="true"
# Auto-Detection: Report generation errors trigger template verification

# Issue: Quality standards compliance violations
# Fix: Update compliance validation and trigger corrective actions
$env:SIX_SIGMA_METHODOLOGY="true"
$env:CMMI_LEVEL5_TARGET="true"
$env:LEAN_MANUFACTURING_PRINCIPLES="true"
# Auto-Detection: Standards violations trigger compliance update

# Issue: Quality gate enforcement failures
# Fix: Reset quality gateway middleware and verify thresholds
$env:QUALITY_GATE_ENFORCEMENT="true"
$env:OVERALL_QUALITY_THRESHOLD="0.85"
$env:QUALITY_ALERT_SYSTEM="true"
# Auto-Detection: Gate enforcement errors trigger middleware restart

# Issue: Predictive quality modeling failures
# Fix: Retrain models and update prediction algorithms
$env:AI_POWERED_QUALITY_ANALYSIS="true"
$env:PREDICTIVE_QUALITY_MODELING="true"
$env:QUALITY_PATTERN_RECOGNITION="true"
# Auto-Detection: Modeling failures trigger automatic model retraining

TQM Quality Debugging Integration:

# Enable TQM debugging in development
from tqm_audit_system import TQMAuditSystem
from continuous_quality_monitor import ContinuousQualityMonitor
from problem_detector import IntelligentProblemDetector

# Initialize TQM debugging system
tqm_audit = TQMAuditSystem()
quality_monitor = ContinuousQualityMonitor()
problem_detector = IntelligentProblemDetector()

# Manual TQM quality debugging
def debug_with_tqm_quality_analysis(quality_issue_or_exception):
    """Debug with comprehensive TQM quality analysis"""

    # Build quality context
    quality_context = {
        'issue_type': type(quality_issue_or_exception).__name__,
        'issue_message': str(quality_issue_or_exception),
        'quality_metrics': quality_monitor.collect_current_metrics(),
        'audit_status': tqm_audit.get_audit_status(),
        'system_state': get_system_state(),
        'timestamp': datetime.utcnow().isoformat()
    }

    # Conduct comprehensive quality audit
    audit_results = tqm_audit.conduct_comprehensive_quality_audit('debug_session')

    print(f"[TQM-DEBUG] Quality Audit Results:")
    print(f"  - Overall Quality Score: {audit_results['executive_summary']['overall_quality_score']:.2f}")
    print(f"  - Performance Quality: {audit_results['quality_assessments']['performance']['quality_score']:.2f}")
    print(f"  - Reliability Quality: {audit_results['quality_assessments']['reliability']['quality_score']:.2f}")
    print(f"  - Security Quality: {audit_results['quality_assessments']['security']['quality_score']:.2f}")

    # Identify quality improvement opportunities
    improvements = audit_results['improvement_recommendations']
    print(f"[TQM-DEBUG] Quality Improvement Opportunities: {len(improvements)}")
    for improvement in improvements[:5]:  # Show top 5
        print(f"  - {improvement['category']}: {improvement['description']}")

    # Apply automated quality corrections
    if audit_results['executive_summary']['overall_quality_score'] < 0.8:
        print("[TQM-DEBUG] Applying automated quality corrections...")
        quality_corrections = quality_monitor.automated_quality_improvement(quality_context)
        print(f"[TQM-DEBUG] Applied corrections: {quality_corrections}")

    return audit_results

# TQM quality issue detection patterns
def detect_tqm_quality_issues():
    """Detect TQM-specific quality issues"""

    quality_issues = []

    # Check audit compliance
    audit_status = tqm_audit.get_audit_status()
    if audit_status.get('overdue_audits', 0) > 0:
        quality_issues.append({
            'category': 'AUDIT_COMPLIANCE',
            'severity': 'HIGH',
            'description': f"{audit_status['overdue_audits']} audits overdue",
            'automated_fix': True,
            'solutions': ['schedule_immediate_audit', 'update_audit_calendar']
        })

    # Check quality thresholds
    current_quality = quality_monitor.assess_current_quality()
    for dimension, score in current_quality.items():
        if score < 0.8:  # Below quality threshold
            quality_issues.append({
                'category': f'QUALITY_{dimension.upper()}',
                'severity': 'MEDIUM' if score > 0.6 else 'HIGH',
                'description': f"{dimension} quality below threshold: {score:.2f}",
                'automated_fix': True,
                'solutions': [f'improve_{dimension}_quality', 'trigger_quality_review']
            })

    # Check compliance status
    compliance_status = tqm_audit.check_compliance_status()
    for standard, status in compliance_status.items():
        if not status.get('compliant', True):
            quality_issues.append({
                'category': 'COMPLIANCE_VIOLATION',
                'severity': 'CRITICAL',
                'description': f"{standard} compliance violation detected",
                'automated_fix': True,
                'solutions': ['update_compliance_controls', 'schedule_compliance_audit']
            })

    return quality_issues

# Auto-fix TQM quality issues
def auto_fix_tqm_quality_issues(quality_issues: List[Dict]) -> Dict[str, Any]:
    """Automatically fix TQM quality issues"""

    fix_results = {
        'attempted_fixes': [],
        'successful_fixes': [],
        'failed_fixes': []
    }

    for issue in quality_issues:
        if issue.get('automated_fix', False):
            try:
                category = issue['category']
                solutions = issue.get('solutions', [])

                for solution in solutions:
                    if category.startswith('AUDIT_'):
                        result = fix_audit_issue(solution, issue)
                    elif category.startswith('QUALITY_'):
                        result = fix_quality_issue(solution, issue)
                    elif category.startswith('COMPLIANCE_'):
                        result = fix_compliance_issue(solution, issue)
                    else:
                        result = fix_generic_quality_issue(solution, issue)

                    if result['success']:
                        fix_results['successful_fixes'].append({
                            'issue': issue,
                            'solution': solution,
                            'result': result
                        })
                        break  # Success, no need to try other solutions

                if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']):
                    fix_results['failed_fixes'].append(issue)

            except Exception as e:
                logger.error(f"[TQM-DEBUG] Failed to fix quality issue {issue['category']}: {e}")
                fix_results['failed_fixes'].append(issue)

    return fix_results

def fix_audit_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix audit-related quality issues"""

    if solution == 'schedule_immediate_audit':
        # Schedule immediate audit
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.schedule_immediate_audit('comprehensive_tqm')
        return {'success': True, 'details': 'Immediate audit scheduled'}

    elif solution == 'update_audit_calendar':
        # Update audit calendar
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.update_audit_schedule()
        return {'success': True, 'details': 'Audit calendar updated'}

    return {'success': False, 'details': f'Unknown audit solution: {solution}'}

def fix_quality_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix quality dimension issues"""

    if solution.startswith('improve_') and solution.endswith('_quality'):
        dimension = solution.replace('improve_', '').replace('_quality', '')

        # Apply dimension-specific improvements
        improvement_actions = quality_monitor.automated_quality_improvement({
            f'{dimension}_score': issue.get('current_score', 0.5)
        })

        return {'success': True, 'details': f'Quality improvement applied for {dimension}'}

    elif solution == 'trigger_quality_review':
        # Trigger comprehensive quality review
        review_result = tqm_audit.conduct_quality_review(issue)
        return {'success': True, 'details': 'Quality review triggered'}

    return {'success': False, 'details': f'Unknown quality solution: {solution}'}

def fix_compliance_issue(solution: str, issue: Dict) -> Dict[str, Any]:
    """Fix compliance-related quality issues"""

    if solution == 'update_compliance_controls':
        # Update compliance controls
        compliance_manager = ComplianceManager()
        compliance_manager.update_controls_for_standard(issue.get('standard'))
        return {'success': True, 'details': 'Compliance controls updated'}

    elif solution == 'schedule_compliance_audit':
        # Schedule compliance-specific audit
        audit_scheduler = AutomatedAuditScheduler()
        audit_scheduler.schedule_compliance_audit(issue.get('standard'))
        return {'success': True, 'details': 'Compliance audit scheduled'}

    return {'success': False, 'details': f'Unknown compliance solution: {solution}'}

[5] PROJECT-SPECIFIC PATTERNS

[5.1] HUNYUAN3D MODEL CACHING

CRITICAL PERFORMANCE OPTIMIZATION:

Hunyuan3D models take 30-36 seconds to load initially. The

_model_cache

singleton pattern reduces subsequent loads to <1 second (94% speed improvement).

# CORRECT: Check cache before loading
class Hunyuan3DProcessor:
    _model_cache = {'shapegen_pipeline': None, 'initialized': False}
    _cache_lock = threading.Lock()

    def __init__(self, device=None):
        with Hunyuan3DProcessor._cache_lock:
            if Hunyuan3DProcessor._model_cache['initialized']:
                # Instant load from cache
                self.shapegen_pipeline = self._model_cache['shapegen_pipeline']
                return
        self.initialize_model()  # First-time load

# WRONG: Loading models every time
def generate_3d():
    processor = Hunyuan3DProcessor()  # 30s load every request!
    return processor.generate(image)

[5.2] GPU MEMORY PATTERNS

ALWAYS clean up GPU memory after generation:

# CORRECT: Proper cleanup
def generate_3d_model(image):
    try:
        mesh = processor.generate_shape(image)
        return mesh
    finally:
        torch.cuda.empty_cache()  # CRITICAL: Free unused memory

# WRONG: Memory leak
def generate_3d_model(image):
    mesh = processor.generate_shape(image)
    return mesh  # GPU memory not released!

Monitor GPU before processing:

from gpu_manager import get_gpu_manager

gpu_mgr = get_gpu_manager()
if gpu_mgr.can_process_job(estimated_vram=6000):
    with gpu_mgr.allocate_job(job_id):
        result = generate()
else:
    raise ResourceError("Insufficient GPU memory")

[5.3] ASYNC JOB PATTERNS

Long-running operations MUST use AsyncJobQueue:

# CORRECT: Async processing for 3D generation
from batch_processor import get_async_queue

async_queue = get_async_queue()
job = async_queue.submit_job(
    job_type='3d_generation',
    image=image,
    quality=7
)

# Return job ID immediately
return jsonify({'job_id': job.id, 'status': 'queued'})

# WRONG: Blocking request (30-60s timeout)
@app.route('/generate-3d')
def generate_3d():
    mesh = processor.generate_shape(image)  # Blocks for 30-60s!
    return send_file(mesh)

[5.4] STL FILE HANDLING

ALWAYS validate STL files before sending:

from stl_processor import AdvancedSTLProcessor

processor = AdvancedSTLProcessor()

# Analyze mesh quality
stats = processor.analyze_stl(mesh_path)
if not stats['manifold']:
    # Repair non-manifold geometry
    mesh = processor.repair_stl(mesh)

# Optimize for 3D printing
mesh = processor.optimize_stl_for_printing(
    mesh,
    target_size_mm=100,
    wall_thickness_mm=2.0
)

# Export with validation
mesh.export('output.stl')

[5.5] MONITORING & METRICS

Track ALL API requests and generations:

from production_metrics import track_request, GenerationTracker

@app.route('/api/generate-3d')
@track_request('generate_3d')  # Automatic request tracking
def generate_3d():
    tracker = GenerationTracker('3d_generation')
    try:
        mesh = processor.generate(image)
        tracker.success(duration=elapsed, quality=8)
    except Exception as e:
        tracker.failure(reason=str(e), duration=elapsed)
        raise

Access metrics:

Prometheus: http://localhost:9090
Grafana: http://localhost:3000 (admin/orfeas_admin_2025)
Health check: http://localhost:5000/health

[5.6] AI AGENT PATTERNS

ENTERPRISE AGENT AUTHENTICATION & SECURITY:

from agent_auth import AgentAuthManager
from agent_security import AgentSecurityManager

auth_mgr = AgentAuthManager()
security_mgr = AgentSecurityManager()

@app.route('/api/agent/quality-check', methods=['POST'])
@security_mgr.agent_security_wrapper
def agent_quality_check():
    """Enterprise-grade agent endpoint with comprehensive security"""
    agent_id = request.headers.get('X-Agent-ID')
    api_key = request.headers.get('X-Agent-Key')
    request_signature = request.headers.get('X-Request-Signature')

    # Multi-layer authentication
    if not auth_mgr.authenticate_agent(agent_id, api_key):
        security_mgr.log_security_event('unauthorized_agent_access', agent_id)
        return jsonify({'error': 'Unauthorized agent'}), 401

    # Permission validation
    if not auth_mgr.check_permissions(agent_id, 'quality_assessment'):
        security_mgr.log_security_event('insufficient_permissions', agent_id)
        return jsonify({'error': 'Insufficient permissions'}), 403

    # Request signature validation for critical operations
    if not security_mgr.validate_request_signature(request, request_signature, agent_id):
        security_mgr.log_security_event('invalid_signature', agent_id)
        return jsonify({'error': 'Invalid request signature'}), 401

    # Rate limiting for agents
    if not auth_mgr.check_agent_rate_limits(agent_id, 'quality_assessment'):
        return jsonify({'error': 'Agent rate limit exceeded'}), 429

    return process_quality_check(request.get_json())

ADVANCED AGENT ORCHESTRATION WORKFLOW:

from agent_api import OrfeasAIAgent, AgentOrchestrator
from agent_communication import AgentMessageBus
from agent_monitoring import AgentPerformanceMonitor

# Initialize enterprise agent orchestration
orchestrator = AgentOrchestrator()
message_bus = AgentMessageBus()
performance_monitor = AgentPerformanceMonitor()

class EnterpriseAgentWorkflow:
    """
    Advanced agent workflow for complex multi-step processing
    """

    def __init__(self):
        self.quality_agent = OrfeasAIAgent('quality_agent', [
            'image_analysis', 'complexity_assessment', 'quality_prediction'
        ])
        self.workflow_agent = OrfeasAIAgent('workflow_agent', [
            'pipeline_selection', 'resource_optimization', 'error_recovery'
        ])
        self.optimization_agent = OrfeasAIAgent('optimization_agent', [
            'performance_tuning', 'model_selection', 'parameter_optimization'
        ])

    async def execute_intelligent_3d_generation(self, input_data: Dict) -> Dict:
        """Execute intelligent 3D generation with multi-agent coordination"""

        # Phase 1: Intelligent input analysis
        with performance_monitor.track_agent_performance('quality_agent'):
            quality_analysis = await self.quality_agent.analyze_input({
                'image': input_data['image'],
                'requested_quality': input_data.get('quality', 7),
                'user_preferences': input_data.get('user_preferences', {}),
                'deadline_constraints': input_data.get('deadline')
            })

        # Phase 2: Workflow optimization
        with performance_monitor.track_agent_performance('workflow_agent'):
            workflow_plan = await self.workflow_agent.create_optimal_workflow({
                'quality_analysis': quality_analysis,
                'system_resources': self.get_system_resources(),
                'current_load': self.get_current_system_load()
            })

        # Phase 3: Parameter optimization
        with performance_monitor.track_agent_performance('optimization_agent'):
            optimized_params = await self.optimization_agent.optimize_parameters({
                'workflow_plan': workflow_plan,
                'quality_requirements': quality_analysis['quality_requirements'],
                'performance_constraints': workflow_plan['performance_constraints']
            })

        # Phase 4: Coordinated execution with real-time monitoring
        execution_result = await self.execute_with_monitoring(
            workflow_plan, optimized_params, input_data
        )

        # Phase 5: Quality validation and feedback
        final_result = await self.validate_and_enhance_result(
            execution_result, quality_analysis, optimized_params
        )

        return final_result

    async def execute_with_monitoring(self, workflow_plan: Dict, params: Dict, input_data: Dict) -> Dict:
        """Execute generation with real-time agent monitoring and adaptation"""

        # Initialize execution context
        execution_context = {
            'start_time': time.time(),
            'workflow_plan': workflow_plan,
            'optimized_params': params,
            'agent_states': {}
        }

        try:
            # Select optimal model based on agent analysis
            if workflow_plan['recommended_model'] == 'hunyuan3d_ultra':
                processor = UltraQualityHunyuan3DProcessor(params)
            elif workflow_plan['recommended_model'] == 'hunyuan3d_fast':
                processor = FastHunyuan3DProcessor(params)
            elif workflow_plan['recommended_model'] == 'hunyuan3d_balanced':
                processor = BalancedHunyuan3DProcessor(params)
            else:
                # Agent-recommended fallback
                processor = AdaptiveHunyuan3DProcessor(params)

            # Execute with agent-driven resource management
            with self.agent_resource_manager(workflow_plan['resource_allocation']):
                result = await processor.generate_with_monitoring(
                    input_data,
                    monitoring_callback=self.agent_monitoring_callback
                )

            return result

        except Exception as e:
            # Agent-driven error recovery
            recovery_result = await self.workflow_agent.handle_execution_error(
                error=e,
                execution_context=execution_context,
                recovery_strategies=workflow_plan.get('recovery_strategies', [])
            )

            if recovery_result['recovery_successful']:
                return recovery_result['recovered_result']
            else:
                raise RuntimeError(f"Agent workflow execution failed: {e}")

# Initialize enterprise workflow
enterprise_workflow = EnterpriseAgentWorkflow()

# Usage in API endpoints
@app.route('/api/intelligent-generate-3d', methods=['POST'])
async def intelligent_3d_generation():
    """AI-driven intelligent 3D generation with agent orchestration"""
    try:
        input_data = {
            'image': request.files.get('image'),
            'quality': int(request.form.get('quality', 7)),
            'user_preferences': request.get_json().get('preferences', {}),
            'deadline': request.form.get('deadline'),
            'priority': request.form.get('priority', 'normal')
        }

        # Execute with enterprise agent workflow
        result = await enterprise_workflow.execute_intelligent_3d_generation(input_data)

        return jsonify({
            'generation_result': result['output'],
            'agent_analysis': result['agent_insights'],
            'performance_metrics': result['performance_data'],
            'quality_score': result['quality_assessment']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Intelligent generation failed: {e}")
        return jsonify({'error': str(e)}), 500

AGENT COMMUNICATION & MESSAGE PASSING:

from agent_communication import AgentMessageBus, AgentProtocol

class AgentCommunicationPatterns:
    """
    Advanced agent communication patterns for distributed processing
    """

    def __init__(self):
        self.message_bus = AgentMessageBus()
        self.protocol = AgentProtocol()

    async def setup_agent_communication_network(self):
        """Setup inter-agent communication network"""

        # Register message handlers for each agent type
        await self.message_bus.register_handler(
            'quality_assessment_request',
            self.handle_quality_assessment_message
        )

        await self.message_bus.register_handler(
            'workflow_optimization_request',
            self.handle_workflow_optimization_message
        )

        await self.message_bus.register_handler(
            'parameter_optimization_request',
            self.handle_parameter_optimization_message
        )

        # Setup agent discovery and heartbeat
        await self.setup_agent_discovery()

    async def coordinate_multi_agent_task(self, task: Dict) -> Dict:
        """Coordinate complex task across multiple agents"""

        # Create task coordination context
        coordination_context = {
            'task_id': self.generate_task_id(),
            'task_type': task['type'],
            'involved_agents': [],
            'coordination_strategy': self.select_coordination_strategy(task),
            'communication_graph': self.build_communication_graph(task)
        }

        # Phase 1: Task decomposition and agent assignment
        subtasks = await self.decompose_task_for_agents(task, coordination_context)

        # Phase 2: Parallel agent execution with coordination
        agent_results = {}
        async with self.coordination_context(coordination_context):
            for agent_id, subtask in subtasks.items():
                agent_results[agent_id] = await self.execute_agent_subtask(
                    agent_id, subtask, coordination_context
                )

        # Phase 3: Result synthesis and validation
        synthesized_result = await self.synthesize_agent_results(
            agent_results, coordination_context
        )

        return synthesized_result

# Agent message handling patterns
async def handle_quality_assessment_message(message: Dict) -> Dict:
    """Handle quality assessment requests from other agents"""
    quality_agent = await get_agent('quality_assessment')

    assessment_result = await quality_agent.assess_quality({
        'input_data': message['payload']['input_data'],
        'assessment_criteria': message['payload']['criteria'],
        'requesting_agent': message['sender_id']
    })

    return {
        'message_type': 'quality_assessment_response',
        'recipient_id': message['sender_id'],
        'payload': assessment_result,
        'correlation_id': message['correlation_id']
    }

AGENT LEARNING & ADAPTATION PATTERNS:

from agent_learning import AgentLearningSystem, ExperienceBuffer
from agent_adaptation import AdaptiveAgent, PerformanceFeedback

class IntelligentAdaptiveAgent(OrfeasAIAgent):
    """
    Self-improving agent with learning and adaptation capabilities
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        super().__init__(agent_type, capabilities)
        self.learning_system = AgentLearningSystem()
        self.experience_buffer = ExperienceBuffer()
        self.performance_history = {}

    async def execute_with_learning(self, task: Dict, context: Dict) -> Dict:
        """Execute task with learning and adaptation"""

        # Pre-execution: Retrieve relevant experience
        relevant_experience = self.experience_buffer.get_relevant_experience(
            task_signature=self.generate_task_signature(task),
            context_similarity_threshold=0.8
        )

        # Apply learned optimizations
        if relevant_experience:
            optimized_task = self.apply_learned_optimizations(task, relevant_experience)
        else:
            optimized_task = task

        # Execute with performance monitoring
        start_time = time.time()
        try:
            result = await self.execute_core_logic(optimized_task, context)

            # Calculate performance metrics
            execution_time = time.time() - start_time
            performance_score = self.calculate_performance_score(result, execution_time)

            # Store experience for future learning
            experience = {
                'task': task,
                'context': context,
                'optimizations_applied': optimized_task != task,
                'result': result,
                'performance_score': performance_score,
                'execution_time': execution_time,
                'success': True,
                'timestamp': time.time()
            }
            self.experience_buffer.store_experience(experience)

            # Trigger learning if enough new experiences accumulated
            if self.experience_buffer.should_trigger_learning():
                await self.learning_system.update_agent_knowledge(self, self.experience_buffer)

            return result

        except Exception as e:
            # Learn from failures
            failure_experience = {
                'task': task,
                'context': context,
                'error': str(e),
                'error_type': type(e).__name__,
                'success': False,
                'timestamp': time.time()
            }
            self.experience_buffer.store_experience(failure_experience)

            # Attempt intelligent error recovery
            recovery_result = await self.attempt_learned_recovery(e, task, context)
            if recovery_result['recovered']:
                return recovery_result['result']
            else:
                raise

    def apply_learned_optimizations(self, task: Dict, experiences: List[Dict]) -> Dict:
        """Apply optimizations learned from previous similar tasks"""

        optimized_task = task.copy()

        # Extract optimization patterns from successful experiences
        successful_experiences = [exp for exp in experiences if exp['success']]

        if successful_experiences:
            # Learn optimal parameters
            optimal_params = self.learning_system.extract_optimal_parameters(
                successful_experiences
            )
            optimized_task.update(optimal_params)

            # Learn optimal preprocessing steps
            preprocessing_optimizations = self.learning_system.extract_preprocessing_patterns(
                successful_experiences
            )
            optimized_task['preprocessing'] = preprocessing_optimizations

        return optimized_task

# Agent performance optimization patterns
class PerformanceOptimizedAgent(IntelligentAdaptiveAgent):
    """
    Agent optimized for maximum performance with intelligent caching
    """

    def __init__(self, agent_type: str, capabilities: List[str]):
        super().__init__(agent_type, capabilities)
        self.result_cache = self.initialize_intelligent_cache()
        self.performance_optimizer = AgentPerformanceOptimizer()

    async def execute_with_performance_optimization(self, task: Dict, context: Dict) -> Dict:
        """Execute with comprehensive performance optimization"""

        # Check intelligent cache
        cache_key = self.generate_cache_key(task, context)
        cached_result = await self.result_cache.get_cached_result(cache_key)

        if cached_result and self.is_cache_valid(cached_result, context):
            # Update cache hit metrics
            self.performance_optimizer.record_cache_hit(cache_key)
            return cached_result['result']

        # No cache hit - execute with optimization
        optimized_execution = await self.performance_optimizer.optimize_execution(
            agent=self,
            task=task,
            context=context
        )

        result = await self.execute_with_learning(task, context)

        # Cache result for future use
        await self.result_cache.cache_result(
            cache_key,
            result,
            ttl=self.calculate_cache_ttl(task, result)
        )

        return result

ENTERPRISE AGENT DEPLOYMENT PATTERNS:

from kubernetes import client, config
from agent_deployment import AgentDeploymentManager, AgentScaler

class EnterpriseAgentDeployment:
    """
    Enterprise-grade agent deployment and scaling patterns
    """

    def __init__(self):
        self.deployment_manager = AgentDeploymentManager()
        self.scaler = AgentScaler()
        self.k8s_client = self.initialize_kubernetes_client()

    def deploy_agent_cluster(self, agent_config: Dict) -> Dict:
        """Deploy agent cluster with auto-scaling and load balancing"""

        deployment_spec = {
            'agent_type': agent_config['type'],
            'replicas': agent_config.get('initial_replicas', 3),
            'resource_requirements': {
                'cpu': agent_config.get('cpu_request', '500m'),
                'memory': agent_config.get('memory_request', '1Gi'),
                'gpu': agent_config.get('gpu_request', 0)
            },
            'auto_scaling': {
                'min_replicas': agent_config.get('min_replicas', 2),
                'max_replicas': agent_config.get('max_replicas', 10),
                'target_cpu_utilization': agent_config.get('target_cpu', 70),
                'target_memory_utilization': agent_config.get('target_memory', 80)
            },
            'health_checks': {
                'readiness_probe': '/health/ready',
                'liveness_probe': '/health/live',
                'startup_probe': '/health/startup'
            }
        }

        # Deploy to Kubernetes
        deployment_result = self.deployment_manager.deploy_to_kubernetes(
            deployment_spec,
            namespace=agent_config.get('namespace', 'orfeas-agents')
        )

        # Setup service mesh integration
        service_mesh_config = self.setup_agent_service_mesh(deployment_result)

        # Configure monitoring and observability
        monitoring_config = self.setup_agent_monitoring(deployment_result)

        return {
            'deployment_id': deployment_result['deployment_id'],
            'service_endpoints': deployment_result['endpoints'],
            'service_mesh_config': service_mesh_config,
            'monitoring_config': monitoring_config,
            'scaling_config': deployment_spec['auto_scaling']
        }

    def setup_agent_circuit_breaker(self, agent_service: str) -> Dict:
        """Setup circuit breaker pattern for agent resilience"""

        circuit_breaker_config = {
            'failure_threshold': 5,     # Trip after 5 failures
            'recovery_timeout': 30,     # Try recovery after 30 seconds
            'success_threshold': 3,     # Close after 3 successes
            'request_timeout': 10,      # 10 second request timeout
            'monitoring_window': 60     # 60 second monitoring window
        }

        return self.deployment_manager.configure_circuit_breaker(
            agent_service, circuit_breaker_config
        )

# Agent load balancing patterns
@app.route('/api/agent-proxy/<agent_type>', methods=['POST'])
async def intelligent_agent_proxy(agent_type: str):
    """Intelligent agent proxy with load balancing and failover"""

    try:
        # Get available agent instances
        available_agents = await agent_discovery.get_healthy_agents(agent_type)

        if not available_agents:
            return jsonify({'error': f'No healthy {agent_type} agents available'}), 503

        # Select optimal agent based on current load
        selected_agent = agent_load_balancer.select_optimal_agent(
            available_agents,
            request_size=len(request.get_data()),
            request_complexity=analyze_request_complexity(request)
        )

        # Execute request with circuit breaker protection
        with agent_circuit_breaker.protected_execution(selected_agent['id']):
            result = await forward_to_agent(selected_agent, request.get_json())

        return jsonify(result)

    except CircuitBreakerOpenError:
        # Circuit breaker is open - try fallback
        fallback_result = await execute_fallback_agent_logic(agent_type, request.get_json())
        return jsonify(fallback_result)

    except Exception as e:
        logger.error(f"[ORFEAS] Agent proxy error: {e}")
        return jsonify({'error': 'Agent execution failed'}), 500

[5.6.1] TQM AUDIT TECHNIQUES PATTERNS

ENTERPRISE QUALITY AUDIT WORKFLOW:

Always integrate TQM (Total Quality Management) audit techniques into development and production workflows:

# CORRECT: Integrated quality audit patterns
from tqm_audit_system import TQMAuditSystem
from continuous_quality_monitor import ContinuousQualityMonitor

# Initialize TQM system at application startup
tqm_audit = TQMAuditSystem()
quality_monitor = ContinuousQualityMonitor()

# Start continuous quality monitoring
quality_monitor.start_real_time_monitoring()

@app.before_request
def quality_gate_check():
    """Quality gate before processing requests"""
    # Pre-request quality assessment
    quality_score = quality_monitor.assess_current_quality()

    if quality_score < 0.8:  # Below quality threshold
        # Trigger quality improvement actions
        improvement_actions = quality_monitor.automated_quality_improvement({
            'performance_score': quality_score,
            'timestamp': datetime.utcnow()
        })
        logger.warning(f"[TQM] Quality below threshold: {quality_score}, actions: {improvement_actions}")

@app.after_request
def quality_metrics_collection(response):
    """Collect quality metrics after each request"""
    # Post-request quality tracking
    quality_monitor.track_request_quality({
        'endpoint': request.endpoint,
        'response_time': g.get('response_time', 0),
        'status_code': response.status_code,
        'error_occurred': response.status_code >= 400
    })
    return response

# WRONG: No quality monitoring or audit integration
@app.route('/api/generate-3d')
def generate_without_quality_checks():
    return process_generation()  # No quality gates or monitoring!

AUTOMATED QUALITY AUDIT INTEGRATION:

# CORRECT: Comprehensive quality audit workflow
from automated_audit_scheduler import AutomatedAuditScheduler

audit_scheduler = AutomatedAuditScheduler()

def schedule_quality_audits():
    """Schedule automated quality audits based on TQM principles"""

    # Daily performance audits
    audit_scheduler.schedule_audit('performance', 'daily', {
        'focus_areas': ['response_times', 'throughput', 'error_rates'],
        'quality_targets': {
            'api_response_p95': '<500ms',
            'error_rate': '<0.1%',
            'uptime': '>99.9%'
        }
    })

    # Weekly process quality audits
    audit_scheduler.schedule_audit('process_quality', 'weekly', {
        'focus_areas': ['code_review', 'testing_coverage', 'deployment_quality'],
        'compliance_standards': ['iso_9001', 'cmmi_level5']
    })

    # Monthly comprehensive TQM audits
    audit_scheduler.schedule_audit('comprehensive_tqm', 'monthly', {
        'focus_areas': ['all_quality_dimensions'],
        'audit_standards': ['iso_9001_2015', 'six_sigma', 'lean_manufacturing'],
        'generate_report': True,
        'stakeholder_notification': True
    })

# Execute quality audit after major releases
def post_deployment_quality_audit(deployment_info: Dict):
    """Execute comprehensive quality audit after deployment"""

    audit_results = tqm_audit.conduct_comprehensive_quality_audit('post_deployment')

    # Generate executive report
    audit_report = tqm_audit.generate_quality_audit_report(audit_results)

    # Check for critical quality issues
    if audit_report['executive_summary']['overall_quality_score'] < 0.85:
        # Trigger immediate quality improvement actions
        logger.critical(f"[TQM] Post-deployment quality below threshold")
        trigger_quality_improvement_plan(audit_report)

    return audit_report

CONTINUOUS QUALITY MONITORING PATTERNS:

# CORRECT: Real-time quality monitoring with automated responses
class QualityGatewayMiddleware:
    """Middleware for continuous quality monitoring and control"""

    def __init__(self):
        self.quality_monitor = ContinuousQualityMonitor()
        self.quality_thresholds = {
            'performance': 0.8,
            'reliability': 0.95,
            'security': 0.9,
            'user_satisfaction': 0.85
        }

    def before_request(self):
        """Quality gate before request processing"""
        current_quality = self.quality_monitor.assess_real_time_quality()

        # Check quality dimensions
        for dimension, threshold in self.quality_thresholds.items():
            if current_quality.get(dimension, 0) < threshold:
                # Quality below threshold - take corrective action
                self.trigger_quality_correction(dimension, current_quality[dimension])

        # Store quality context for post-processing analysis
        g.quality_context = current_quality

    def after_request(self, response):
        """Quality measurement and learning after request"""
        processing_time = time.time() - g.get('start_time', time.time())

        # Update quality metrics
        quality_update = {
            'request_quality': g.get('quality_context', {}),
            'response_quality': {
                'processing_time': processing_time,
                'success': response.status_code < 400,
                'response_size': len(response.get_data())
            }
        }

        # Feed quality data to continuous improvement system
        self.quality_monitor.update_quality_metrics(quality_update)

        return response

    def trigger_quality_correction(self, dimension: str, current_score: float):
        """Automated quality correction based on dimension"""

        if dimension == 'performance':
            # Performance quality correction
            self.enable_performance_optimizations()

        elif dimension == 'reliability':
            # Reliability quality correction
            self.enhance_error_handling()

        elif dimension == 'security':
            # Security quality correction
            self.strengthen_security_controls()

        logger.info(f"[TQM] Quality correction applied for {dimension}: {current_score}")

QUALITY METRICS INTEGRATION:

# CORRECT: Comprehensive quality metrics collection
from prometheus_client import Counter, Histogram, Gauge

# TQM-specific metrics
quality_audit_counter = Counter(
    'tqm_audits_total',
    'Total number of TQM audits executed',
    ['audit_type', 'audit_scope']
)

quality_score_gauge = Gauge(
    'tqm_quality_score',
    'Current TQM quality score',
    ['quality_dimension']
)

quality_improvement_histogram = Histogram(
    'tqm_improvement_actions_duration',
    'Time taken for quality improvement actions',
    ['improvement_type']
)

def track_quality_metrics(func):
    """Decorator to track quality metrics for any function"""

    def wrapper(*args, **kwargs):
        start_time = time.time()

        try:
            result = func(*args, **kwargs)

            # Track successful quality operation
            quality_score = calculate_operation_quality_score(result)
            quality_score_gauge.labels(quality_dimension=func.__name__).set(quality_score)

            return result

        except Exception as e:
            # Track quality failure
            quality_score_gauge.labels(quality_dimension=func.__name__).set(0)
            raise

        finally:
            # Track operation duration
            duration = time.time() - start_time
            quality_improvement_histogram.labels(
                improvement_type=func.__name__
            ).observe(duration)

    return wrapper

# Usage in core functions
@track_quality_metrics
def generate_3d_with_quality_tracking(image):
    """3D generation with integrated quality tracking"""

    # Pre-generation quality assessment
    input_quality = assess_input_quality(image)

    if input_quality < 0.7:
        # Enhance input quality before processing
        image = enhance_image_quality(image)

    # Generation with quality monitoring
    result = processor.generate_shape(image)

    # Post-generation quality validation
    output_quality = assess_output_quality(result)

    if output_quality < 0.8:
        # Apply quality improvement techniques
        result = apply_quality_enhancements(result)

    return result

[5.6.2] LLM INTEGRATION & COPILOT PATTERNS

ENTERPRISE LLM INTEGRATION WORKFLOW:

# backend/llm_integration.py - Core LLM orchestration
from langchain_enterprise import LangChainManager
from llm_router import LLMRouter
from context_manager import IntelligentContextManager

class EnterpriseLLMManager:
    """
    Enterprise-grade Large Language Model integration and orchestration
    """

    def __init__(self):
        self.llm_router = LLMRouter()
        self.context_manager = IntelligentContextManager()
        self.active_models = {
            'gpt4_turbo': None,
            'claude_3_5_sonnet': None,
            'gemini_ultra': None,
            'llama_3_1_405b': None,
            'mistral_8x22b': None
        }
        self.model_capabilities = self.load_model_capabilities()

    def select_optimal_llm(self, task_context: Dict) -> str:
        """Select best LLM based on task requirements and context"""

        task_type = task_context.get('task_type', 'general')
        complexity = task_context.get('complexity_score', 0.5)
        latency_requirement = task_context.get('max_latency_ms', 5000)

        # Task-specific model selection
        if task_type == 'code_generation':
            if complexity > 0.8:
                return 'gpt4_turbo'  # Best for complex coding tasks
            else:
                return 'claude_3_5_sonnet'  # Fast and accurate for simple code

        elif task_type == 'reasoning_analysis':
            return 'gpt4_turbo'  # Superior reasoning capabilities

        elif task_type == 'content_creation':
            return 'claude_3_5_sonnet'  # Excellent creative writing

        elif task_type == 'multimodal_understanding':
            return 'gemini_ultra'  # Best multimodal capabilities

        elif task_type == 'real_time_chat':
            if latency_requirement < 1000:  # < 1 second
                return 'mistral_8x22b'  # Fastest response time
            else:
                return 'claude_3_5_sonnet'

        else:  # General purpose
            return 'gpt4_turbo'  # Default to most capable model

    async def process_with_llm(self, prompt: str, context: Dict, model_override: str = None) -> Dict:
        """Process request with optimal LLM selection and context awareness"""

        # Build comprehensive context
        processing_context = self.context_manager.build_llm_context(context)

        # Select optimal model
        selected_model = model_override or self.select_optimal_llm(processing_context)

        # Apply context-aware prompt enhancement
        enhanced_prompt = self.enhance_prompt_with_context(prompt, processing_context)

        # Execute with selected LLM
        try:
            result = await self.execute_llm_request(selected_model, enhanced_prompt, processing_context)

            # Apply post-processing and validation
            validated_result = self.validate_and_enhance_response(result, processing_context)

            # Update model performance metrics
            self.update_model_performance(selected_model, result, processing_context)

            return validated_result

        except Exception as e:
            # Intelligent fallback to alternative models
            return await self.fallback_llm_processing(prompt, processing_context, failed_model=selected_model)

    def enhance_prompt_with_context(self, prompt: str, context: Dict) -> str:
        """Enhance prompt with intelligent context injection"""

        context_template = """
        SYSTEM CONTEXT:
        - Platform: ORFEAS AI 2D→3D Studio Enterprise
        - User Expertise: {user_expertise}
        - Task Complexity: {complexity_score}
        - Previous Context: {previous_interactions}
        - Quality Requirements: {quality_requirements}

        USER REQUEST:
        {original_prompt}

        ENHANCED INSTRUCTIONS:
        - Provide enterprise-grade responses with technical accuracy
        - Include code examples when relevant (Python/JavaScript preferred)
        - Consider 3D modeling, AI, and multimedia context
        - Ensure responses align with ORFEAS platform capabilities
        """

        return context_template.format(
            user_expertise=context.get('user_expertise', 'intermediate'),
            complexity_score=context.get('complexity_score', 0.5),
            previous_interactions=context.get('previous_context', 'None'),
            quality_requirements=context.get('quality_requirements', 'high'),
            original_prompt=prompt
        )

GITHUB COPILOT ENTERPRISE INTEGRATION:

# backend/copilot_enterprise.py - GitHub Copilot Enterprise integration
class GitHubCopilotEnterprise:
    """
    GitHub Copilot Enterprise integration for advanced code generation
    """

    def __init__(self):
        self.copilot_api = self.initialize_copilot_api()
        self.code_context_manager = CodeContextManager()
        self.quality_validator = CodeQualityValidator()

    async def generate_code_with_copilot(self, requirements: str, context: Dict) -> Dict:
        """Generate code using GitHub Copilot Enterprise with context awareness"""

        # Build code generation context
        code_context = {
            'project_type': 'orfeas_ai_platform',
            'language': context.get('language', 'python'),
            'framework': 'flask_pytorch_enterprise',
            'coding_standards': 'orfeas_enterprise_standards',
            'security_level': 'enterprise_grade',
            'existing_codebase': self.code_context_manager.get_relevant_code(requirements)
        }

        # Generate code with Copilot
        copilot_response = await self.copilot_api.generate_code(
            prompt=self.build_copilot_prompt(requirements, code_context),
            max_tokens=2000,
            temperature=0.2,  # Low temperature for more deterministic code
            include_context=True
        )

        # Validate and enhance generated code
        validated_code = self.quality_validator.validate_and_enhance(
            code=copilot_response['generated_code'],
            requirements=requirements,
            context=code_context
        )

        return {
            'generated_code': validated_code['code'],
            'quality_score': validated_code['quality_score'],
            'suggestions': validated_code['improvement_suggestions'],
            'tests': await self.generate_tests_for_code(validated_code['code']),
            'documentation': self.generate_code_documentation(validated_code['code'])
        }

    def build_copilot_prompt(self, requirements: str, context: Dict) -> str:
        """Build optimized prompt for GitHub Copilot"""

        prompt_template = """
        # ORFEAS AI Enterprise Platform - Code Generation Request

        ## Project Context:
        - Platform: ORFEAS AI 2D→3D Studio Enterprise
        - Framework: Flask + PyTorch + Hunyuan3D-2.1
        - Language: {language}
        - Security: Enterprise-grade with input validation
        - Performance: GPU-optimized for RTX 3090
        - Quality: Production-ready with comprehensive error handling

        ## Requirements:
        {requirements}

        ## Technical Constraints:
        - Follow ORFEAS coding patterns and conventions
        - Include comprehensive error handling and logging
        - Implement proper input validation and security checks
        - Use type hints and docstrings
        - Optimize for GPU memory management
        - Include monitoring and metrics collection

        ## Expected Output:
        - Clean, production-ready code
        - Comprehensive error handling
        - Performance optimizations
        - Security best practices
        - Proper documentation
        """

        return prompt_template.format(
            language=context.get('language', 'python'),
            requirements=requirements
        )

MULTI-LLM ORCHESTRATION PATTERNS:

# Multi-LLM coordination for complex tasks
class MultiLLMOrchestrator:
    """
    Orchestrate multiple LLMs for complex multi-step tasks
    """

    def __init__(self):
        self.llm_manager = EnterpriseLLMManager()
        self.task_decomposer = TaskDecomposer()
        self.result_synthesizer = ResultSynthesizer()

    async def execute_complex_task(self, task_description: str, context: Dict) -> Dict:
        """Execute complex task using multiple specialized LLMs"""

        # 1. Decompose complex task into subtasks
        subtasks = self.task_decomposer.decompose_task(task_description, context)

        # 2. Assign optimal LLM for each subtask
        llm_assignments = {}
        for subtask in subtasks:
            optimal_llm = self.llm_manager.select_optimal_llm(subtask['context'])
            llm_assignments[subtask['id']] = optimal_llm

        # 3. Execute subtasks in parallel or sequence
        results = {}
        for subtask in subtasks:
            assigned_llm = llm_assignments[subtask['id']]

            # Execute with assigned LLM
            result = await self.llm_manager.process_with_llm(
                prompt=subtask['prompt'],
                context=subtask['context'],
                model_override=assigned_llm
            )

            results[subtask['id']] = {
                'result': result,
                'llm_used': assigned_llm,
                'execution_time': result.get('execution_time', 0)
            }

        # 4. Synthesize results from multiple LLMs
        final_result = self.result_synthesizer.synthesize_results(
            results, task_description, context
        )

        return {
            'final_result': final_result,
            'execution_breakdown': results,
            'llm_assignments': llm_assignments,
            'total_execution_time': sum(r['execution_time'] for r in results.values())
        }

RAG (RETRIEVAL-AUGMENTED GENERATION) PATTERNS:

# backend/rag_integration.py - Advanced RAG implementation
class EnterpriseRAGSystem:
    """
    Enterprise-grade Retrieval-Augmented Generation system
    """

    def __init__(self):
        self.vector_store = self.initialize_vector_store()
        self.knowledge_graph = self.initialize_knowledge_graph()
        self.retrieval_engine = AdvancedRetrievalEngine()
        self.generation_engine = LLMGenerationEngine()

    def initialize_vector_store(self):
        """Initialize enterprise vector database"""
        from pinecone_enterprise import PineconeEnterprise

        return PineconeEnterprise(
            index_name="orfeas_knowledge_base",
            dimension=1536,  # OpenAI embedding dimension
            metric="cosine",
            shards=4,  # Enterprise scaling
            replicas=2  # High availability
        )

    async def rag_enhanced_generation(self, query: str, context: Dict) -> Dict:
        """Generate response using RAG with multiple knowledge sources"""

        # 1. Multi-source knowledge retrieval
        retrieved_knowledge = await self.retrieve_relevant_knowledge(query, context)

        # 2. Knowledge synthesis and ranking
        synthesized_knowledge = self.synthesize_knowledge_sources(retrieved_knowledge)

        # 3. Context-aware prompt construction
        enhanced_prompt = self.build_rag_prompt(query, synthesized_knowledge, context)

        # 4. LLM generation with retrieved context
        generated_response = await self.generation_engine.generate_with_context(
            prompt=enhanced_prompt,
            context=context,
            knowledge_context=synthesized_knowledge
        )

        # 5. Response validation and citation
        validated_response = self.validate_and_cite_response(
            response=generated_response,
            sources=retrieved_knowledge,
            original_query=query
        )

        return {
            'response': validated_response['text'],
            'confidence_score': validated_response['confidence'],
            'citations': validated_response['citations'],
            'knowledge_sources': retrieved_knowledge,
            'retrieval_quality': self.assess_retrieval_quality(retrieved_knowledge, query)
        }

    async def retrieve_relevant_knowledge(self, query: str, context: Dict) -> List[Dict]:
        """Retrieve relevant knowledge from multiple sources"""

        retrieval_sources = [
            'orfeas_documentation',
            'technical_specifications',
            'code_examples',
            'best_practices',
            'troubleshooting_guides',
            'api_references'
        ]

        all_retrieved = []

        for source in retrieval_sources:
            # Vector similarity search
            vector_results = await self.vector_store.similarity_search(
                query=query,
                namespace=source,
                top_k=5,
                filter=self.build_retrieval_filter(context, source)
            )

            # Knowledge graph traversal
            graph_results = await self.knowledge_graph.find_related_concepts(
                query=query,
                source=source,
                max_depth=2,
                relationship_types=['related_to', 'implements', 'extends']
            )

            # Combine and score results
            combined_results = self.combine_retrieval_results(vector_results, graph_results)
            all_retrieved.extend(combined_results)

        # Rank and deduplicate
        ranked_results = self.rank_retrieval_results(all_retrieved, query, context)

        return ranked_results[:10]  # Return top 10 most relevant

LLM API ENDPOINTS:

# LLM-powered API endpoints for the ORFEAS platform
@app.route('/api/llm/generate-code', methods=['POST'])
@track_request('llm_code_generation')
def api_llm_generate_code():
    """Generate code using enterprise LLM capabilities"""
    try:
        data = request.get_json()
        requirements = data.get('requirements', '')
        language = data.get('language', 'python')
        context = data.get('context', {})

        if not requirements:
            return jsonify({'error': 'No requirements provided'}), 400

        # Generate code with GitHub Copilot Enterprise
        copilot = GitHubCopilotEnterprise()
        result = await copilot.generate_code_with_copilot(requirements, {
            'language': language,
            'user_context': context
        })

        return jsonify({
            'generated_code': result['generated_code'],
            'quality_score': result['quality_score'],
            'suggestions': result['suggestions'],
            'tests': result['tests'],
            'documentation': result['documentation']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM code generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/llm/intelligent-analysis', methods=['POST'])
@track_request('llm_intelligent_analysis')
def api_llm_intelligent_analysis():
    """Perform intelligent analysis using multiple LLMs"""
    try:
        data = request.get_json()
        analysis_request = data.get('analysis_request', '')
        analysis_type = data.get('analysis_type', 'general')
        context = data.get('context', {})

        # Use multi-LLM orchestration for complex analysis
        orchestrator = MultiLLMOrchestrator()
        result = await orchestrator.execute_complex_task(
            task_description=f"Perform {analysis_type} analysis: {analysis_request}",
            context=context
        )

        return jsonify({
            'analysis_result': result['final_result'],
            'llm_breakdown': result['execution_breakdown'],
            'execution_time': result['total_execution_time'],
            'confidence_score': result.get('confidence_score', 0.85)
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM intelligent analysis failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/llm/rag-query', methods=['POST'])
@track_request('llm_rag_query')
def api_llm_rag_query():
    """Answer questions using RAG (Retrieval-Augmented Generation)"""
    try:
        data = request.get_json()
        query = data.get('query', '')
        context = data.get('context', {})

        if not query:
            return jsonify({'error': 'No query provided'}), 400

        # Use RAG system for knowledge-enhanced responses
        rag_system = EnterpriseRAGSystem()
        result = await rag_system.rag_enhanced_generation(query, context)

        return jsonify({
            'response': result['response'],
            'confidence_score': result['confidence_score'],
            'citations': result['citations'],
            'knowledge_sources': result['knowledge_sources'],
            'retrieval_quality': result['retrieval_quality']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] LLM RAG query failed: {e}")
        return jsonify({'error': str(e)}), 500

[5.7] ALTERNATIVE AI ENGINE INTEGRATION

HuggingFace fallback patterns:

from huggingface_compat import HuggingFaceModelManager

hf_mgr = HuggingFaceModelManager()

try:
    # Primary: Hunyuan3D-2.1
    mesh = hunyuan_processor.generate_3d(image)
except Exception as e:
    logger.warning(f"[ORFEAS] Primary model failed: {e}")
    try:
        # Fallback: InstantMesh via HuggingFace
        mesh = hf_mgr.fallback_generation(prompt, 'instant_mesh')
    except Exception as e2:
        logger.error(f"[ORFEAS] All models failed: {e2}")
        return jsonify({'error': 'Generation failed'}), 500

ComfyUI workflow integration:

# backend/comfyui_integration.py
class ComfyUIWorkflowManager:
    def __init__(self):
        self.api_url = "http://localhost:8188"
        self.workflows = {
            'text_to_3d': 'workflows/text_to_3d.json',
            'image_to_3d': 'workflows/image_to_3d.json',
            'style_transfer_3d': 'workflows/style_transfer_3d.json'
        }

    async def execute_workflow(self, workflow_type: str, inputs: Dict):
        """Execute ComfyUI workflow as alternative generation method"""
        workflow_path = self.workflows.get(workflow_type)
        if not workflow_path:
            raise ValueError(f"Unknown workflow: {workflow_type}")

        # Load and customize workflow
        with open(workflow_path) as f:
            workflow = json.load(f)

        # Update inputs
        self.update_workflow_inputs(workflow, inputs)

        # Submit to ComfyUI API
        response = await self.submit_workflow(workflow)
        return response

[5.7.1] AI VIDEO COMPOSITION PATTERNS

VIDEO GENERATION WORKFLOW:

# backend/video_processor.py - Core video composition
from sora_video_integration import SoraVideoProcessor
from cinematic_composer import CinematicComposer

video_processor = SoraVideoProcessor()
cinematic_composer = CinematicComposer()

def generate_cinematic_video_from_3d(model_path: str, style: str = "cinematic") -> Dict:
    """Generate cinematic video from 3D model"""

    # 1. Analyze 3D model for optimal camera movements
    model_analysis = video_processor.analyze_3d_model(model_path)

    # 2. Generate camera motion sequence
    camera_sequence = cinematic_composer.generate_camera_movements(
        model_analysis, style=style
    )

    # 3. Apply lighting and environment
    scene_setup = cinematic_composer.setup_cinematic_scene(
        model_path, camera_sequence
    )

    # 4. Render video with Sora-inspired generation
    video_output = video_processor.render_cinematic_video(
        scene_setup, fps=24, resolution=(1920, 1080)
    )

    return video_output

# Text-to-Video generation workflow
def generate_video_from_text(prompt: str, duration: int = 10) -> Dict:
    """Generate cinematic video from text prompt"""

    # 1. Analyze prompt for visual elements
    prompt_analysis = video_processor.analyze_text_prompt(prompt)

    # 2. Generate initial frames
    key_frames = video_processor.generate_key_frames(
        prompt, frame_count=duration * 4  # 4 key frames per second
    )

    # 3. Apply temporal consistency
    consistent_frames = video_processor.apply_temporal_consistency(key_frames)

    # 4. Generate intermediate frames with motion
    full_video = video_processor.interpolate_motion(
        consistent_frames, target_fps=24
    )

    return full_video

VIDEO API ENDPOINTS:

# Video generation API endpoints
@app.route('/api/generate-video-from-3d', methods=['POST'])
def api_generate_video_from_3d():
    """Generate video from 3D model"""
    try:
        model_file = request.files.get('model')
        style = request.form.get('style', 'cinematic')
        duration = int(request.form.get('duration', 10))

        # Validate 3D model file
        if not model_file:
            return jsonify({'error': 'No 3D model provided'}), 400

        # Save uploaded model
        model_path = save_uploaded_file(model_file, 'models')

        # Generate video
        video_result = generate_cinematic_video_from_3d(model_path, style)

        return jsonify({
            'video_path': video_result['output_path'],
            'duration': video_result['duration'],
            'fps': video_result['fps'],
            'resolution': video_result['resolution']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Video generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/generate-video-from-text', methods=['POST'])
def api_generate_video_from_text():
    """Generate video from text prompt"""
    try:
        data = request.get_json()
        prompt = data.get('prompt', '')
        duration = data.get('duration', 10)
        style = data.get('style', 'cinematic')

        if not prompt:
            return jsonify({'error': 'No prompt provided'}), 400

        # Generate video
        video_result = generate_video_from_text(prompt, duration)

        return jsonify({
            'video_path': video_result['output_path'],
            'prompt': prompt,
            'duration': video_result['duration'],
            'style': style
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-video generation failed: {e}")
        return jsonify({'error': str(e)}), 500

CINEMATIC FEATURES IMPLEMENTATION:

# backend/cinematic_composer.py - Advanced cinematic features
class CinematicComposer:
    """Advanced cinematic video composition engine"""

    def __init__(self):
        self.camera_presets = {
            'turntable': self.generate_turntable_motion,
            'fly_around': self.generate_orbital_motion,
            'dolly_zoom': self.generate_dolly_zoom,
            'hero_shot': self.generate_hero_shot
        }

    def generate_turntable_motion(self, model_analysis: Dict) -> List[Dict]:
        """Generate smooth turntable camera motion"""
        frames = []
        center = model_analysis['bounding_box']['center']
        radius = model_analysis['bounding_box']['radius'] * 2.5

        for frame in range(240):  # 10 seconds at 24fps
            angle = (frame / 240) * 360  # Full rotation
            camera_pos = {
                'x': center['x'] + radius * math.cos(math.radians(angle)),
                'y': center['y'] + radius * 0.3,  # Slight elevation
                'z': center['z'] + radius * math.sin(math.radians(angle)),
                'target': center,
                'fov': 45
            }
            frames.append(camera_pos)

        return frames

    def apply_cinematic_lighting(self, scene: Dict, style: str) -> Dict:
        """Apply cinematic lighting setup"""
        # Define color values using RGB to avoid parser issues
        WHITE = 'rgb(255, 255, 255)'
        LIGHT_BLUE = 'rgb(240, 240, 255)'
        WARM_WHITE = 'rgb(255, 248, 225)'
        WARM_YELLOW = 'rgb(255, 238, 170)'
        DARK_GRAY = 'rgb(74, 85, 104)'
        WARM_ORANGE = 'rgb(255, 107, 53)'

        lighting_presets = {
            'studio': {
                'key_light': {'intensity': 1.0, 'color': WHITE, 'position': (2, 3, 2)},
                'fill_light': {'intensity': 0.4, 'color': LIGHT_BLUE, 'position': (-1, 2, 1)},
                'rim_light': {'intensity': 0.8, 'color': WARM_WHITE, 'position': (0, 1, -2)}
            },
            'dramatic': {
                'key_light': {'intensity': 1.2, 'color': WARM_YELLOW, 'position': (3, 4, 1)},
                'fill_light': {'intensity': 0.2, 'color': DARK_GRAY, 'position': (-2, 1, 0)},
                'rim_light': {'intensity': 1.0, 'color': WARM_ORANGE, 'position': (-1, 2, -3)}
            }
        }

        scene['lighting'] = lighting_presets.get(style, lighting_presets['studio'])
        return scene

[5.7.2] AI MULTIMEDIA GENERATION PATTERNS

TEXT-TO-IMAGE GENERATION WORKFLOW:

# backend/text_to_image_processor.py - Core text-to-image generation
from text_to_image_ai import TextToImageProcessor
from image_enhancement import ImageEnhancer

class TextToImageProcessor:
    """Advanced text-to-image generation with multiple AI engines"""

    def __init__(self):
        self.models = {
            'dalle3': self.setup_dalle3(),
            'stable_diffusion_xl': self.setup_sdxl(),
            'midjourney': self.setup_midjourney()
        }
        self.enhancer = ImageEnhancer()

    async def generate_image_from_text(self, prompt: str, style: str = "photorealistic") -> Dict:
        """Generate high-quality image from text prompt"""

        # 1. Analyze and enhance prompt
        enhanced_prompt = self.enhance_prompt(prompt, style)

        # 2. Select optimal model based on prompt analysis
        optimal_model = self.select_optimal_model(enhanced_prompt)

        # 3. Generate image with selected model
        generated_image = await self.models[optimal_model].generate(enhanced_prompt)

        # 4. Apply post-processing enhancements
        enhanced_image = self.enhancer.enhance_image(generated_image)

        return {
            'image': enhanced_image,
            'model_used': optimal_model,
            'prompt': enhanced_prompt,
            'style': style
        }

TEXT-TO-SPEECH GENERATION WORKFLOW:

# backend/text_to_speech_processor.py - Core TTS generation
from speech_synthesis import SpeechSynthesizer
from voice_cloning import VoiceCloningEngine

class TextToSpeechProcessor:
    """Enterprise-grade text-to-speech generation"""

    def __init__(self):
        self.engines = {
            'elevenlabs': self.setup_elevenlabs(),
            'azure_tts': self.setup_azure_tts(),
            'openai_tts': self.setup_openai_tts()
        }
        self.voice_cloner = VoiceCloningEngine()

    async def synthesize_speech(self, text: str, voice_id: str = "default") -> Dict:
        """Generate natural speech from text"""

        # 1. Preprocess text for optimal synthesis
        processed_text = self.preprocess_text(text)

        # 2. Select optimal TTS engine
        optimal_engine = self.select_tts_engine(processed_text, voice_id)

        # 3. Generate speech audio
        audio_data = await self.engines[optimal_engine].synthesize(
            text=processed_text,
            voice_id=voice_id,
            quality='high'
        )

        # 4. Apply audio enhancements
        enhanced_audio = self.enhance_audio_quality(audio_data)

        return {
            'audio_data': enhanced_audio,
            'duration': self.get_audio_duration(enhanced_audio),
            'engine_used': optimal_engine,
            'voice_id': voice_id
        }

SPEECH-TO-TEXT PROCESSING WORKFLOW:

# backend/speech_to_text_processor.py - Core STT processing
from speech_recognition import SpeechRecognizer
from audio_preprocessing import AudioPreprocessor

class SpeechToTextProcessor:
    """Advanced speech-to-text with real-time capabilities"""

    def __init__(self):
        self.recognizers = {
            'whisper_large': self.setup_whisper(),
            'azure_speech': self.setup_azure_stt(),
            'google_speech': self.setup_google_stt()
        }
        self.preprocessor = AudioPreprocessor()

    async def transcribe_audio(self, audio_data: bytes, language: str = "en-US") -> Dict:
        """Convert speech audio to text"""

        # 1. Preprocess audio for optimal recognition
        processed_audio = self.preprocessor.enhance_audio(audio_data)

        # 2. Select optimal recognition engine
        optimal_recognizer = self.select_stt_engine(processed_audio, language)

        # 3. Perform speech recognition
        transcription = await self.recognizers[optimal_recognizer].transcribe(
            audio=processed_audio,
            language=language,
            enable_punctuation=True,
            enable_timestamps=True
        )

        # 4. Post-process transcription
        enhanced_transcription = self.enhance_transcription(transcription)

        return {
            'text': enhanced_transcription['text'],
            'confidence': enhanced_transcription['confidence'],
            'timestamps': enhanced_transcription['timestamps'],
            'language': language,
            'engine_used': optimal_recognizer
        }

    async def real_time_transcription(self, audio_stream) -> AsyncGenerator[str, None]:
        """Real-time speech-to-text streaming"""
        async for audio_chunk in audio_stream:
            partial_result = await self.transcribe_audio_chunk(audio_chunk)
            yield partial_result['text']

MULTIMEDIA API ENDPOINTS:

# Multimedia AI API endpoints
@app.route('/api/generate-image-from-text', methods=['POST'])
def api_text_to_image():
    """Generate image from text prompt"""
    try:
        data = request.get_json()
        prompt = data.get('prompt', '')
        style = data.get('style', 'photorealistic')

        if not prompt:
            return jsonify({'error': 'No prompt provided'}), 400

        # Generate image
        result = text_to_image_processor.generate_image_from_text(prompt, style)

        return jsonify({
            'image_url': result['image_url'],
            'prompt': result['prompt'],
            'style': result['style'],
            'model_used': result['model_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-image generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/synthesize-speech', methods=['POST'])
def api_text_to_speech():
    """Convert text to speech"""
    try:
        data = request.get_json()
        text = data.get('text', '')
        voice_id = data.get('voice_id', 'default')

        if not text:
            return jsonify({'error': 'No text provided'}), 400

        # Generate speech
        result = tts_processor.synthesize_speech(text, voice_id)

        return jsonify({
            'audio_url': result['audio_url'],
            'duration': result['duration'],
            'voice_id': result['voice_id'],
            'engine_used': result['engine_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Text-to-speech generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/transcribe-audio', methods=['POST'])
def api_speech_to_text():
    """Convert speech to text"""
    try:
        audio_file = request.files.get('audio')
        language = request.form.get('language', 'en-US')

        if not audio_file:
            return jsonify({'error': 'No audio file provided'}), 400

        # Transcribe audio
        result = stt_processor.transcribe_audio(audio_file.read(), language)

        return jsonify({
            'text': result['text'],
            'confidence': result['confidence'],
            'timestamps': result['timestamps'],
            'language': result['language'],
            'engine_used': result['engine_used']
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Speech-to-text processing failed: {e}")
        return jsonify({'error': str(e)}), 500

[5.7.3] AI CODE DEVELOPMENT PATTERNS

INTELLIGENT CODE GENERATION WORKFLOW:

# backend/code_writer.py - Core code generation
from code_writer import IntelligentCodeWriter
from code_debugger import AutomatedCodeDebugger

code_writer = IntelligentCodeWriter()
code_debugger = AutomatedCodeDebugger()

def generate_code_from_requirements(requirements: str, language: str = "python") -> Dict:
    """Generate code from natural language requirements"""

    # 1. Analyze requirements and extract context
    analysis = code_writer.analyze_requirements(requirements, {
        'target_language': language,
        'code_style': 'clean',
        'include_tests': True,
        'include_docs': True
    })

    # 2. Generate code structure
    code_structure = code_writer.generate_code_structure(analysis, language)

    # 3. Generate implementation
    implementation = code_writer.generate_implementation(code_structure, analysis)

    # 4. Validate and optimize
    optimized_code = code_writer.optimize_code(implementation, language)

    return {
        'code': optimized_code,
        'structure': code_structure,
        'documentation': code_writer.generate_documentation(optimized_code),
        'tests': code_writer.generate_tests(optimized_code),
        'quality_score': code_writer.quality_analyzer.analyze(optimized_code)
    }

# Code debugging workflow
def debug_and_fix_code(code: str, error_message: str, language: str = "python") -> Dict:
    """Debug code and provide automated fixes"""

    # 1. Analyze error and code context
    debug_result = code_debugger.debug_code(code, error_message, language)

    # 2. Apply automated fixes if confidence is high
    if debug_result['confidence'] > 0.8:
        fixed_code = code_debugger.apply_fix(code, debug_result['fix_suggestion'])

        # 3. Validate the fix
        validation_result = code_debugger.validate_fix(fixed_code, code)

        return {
            'original_code': code,
            'fixed_code': fixed_code,
            'error_analysis': debug_result,
            'validation': validation_result,
            'auto_applied': True
        }
    else:
        return {
            'original_code': code,
            'error_analysis': debug_result,
            'suggested_fixes': debug_result['fix_suggestion'],
            'auto_applied': False
        }

CODE DEVELOPMENT API ENDPOINTS:

# Code generation and debugging API endpoints
@app.route('/api/generate-code', methods=['POST'])
def api_generate_code():
    """Generate code from natural language description"""
    try:
        data = request.get_json()
        requirements = data.get('requirements', '')
        language = data.get('language', 'python')
        context = data.get('context', {})

        if not requirements:
            return jsonify({'error': 'No requirements provided'}), 400

        # Generate code
        result = generate_code_from_requirements(requirements, language)

        return jsonify({
            'generated_code': result['code'],
            'documentation': result['documentation'],
            'tests': result['tests'],
            'quality_score': result['quality_score'],
            'language': language
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code generation failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/debug-code', methods=['POST'])
def api_debug_code():
    """Debug code and provide automated fixes"""
    try:
        data = request.get_json()
        code = data.get('code', '')
        error_message = data.get('error_message', '')
        language = data.get('language', 'python')

        if not code or not error_message:
            return jsonify({'error': 'Code and error message required'}), 400

        # Debug and fix code
        debug_result = debug_and_fix_code(code, error_message, language)

        return jsonify({
            'debug_analysis': debug_result['error_analysis'],
            'fixed_code': debug_result.get('fixed_code'),
            'auto_applied': debug_result['auto_applied'],
            'suggested_fixes': debug_result.get('suggested_fixes'),
            'validation': debug_result.get('validation')
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code debugging failed: {e}")
        return jsonify({'error': str(e)}), 500

@app.route('/api/analyze-code-quality', methods=['POST'])
def api_analyze_code_quality():
    """Analyze code quality and provide suggestions"""
    try:
        data = request.get_json()
        code = data.get('code', '')
        language = data.get('language', 'python')

        if not code:
            return jsonify({'error': 'No code provided'}), 400

        # Analyze code quality
        quality_analysis = code_writer.quality_analyzer.analyze_comprehensive(code, language)

        return jsonify({
            'quality_score': quality_analysis['overall_score'],
            'metrics': quality_analysis['metrics'],
            'suggestions': quality_analysis['suggestions'],
            'refactoring_opportunities': quality_analysis['refactoring'],
            'language': language
        })

    except Exception as e:
        logger.error(f"[ORFEAS] Code quality analysis failed: {e}")
        return jsonify({'error': str(e)}), 500

CODE DEVELOPMENT FEATURES IMPLEMENTATION:

# backend/code_analyzer.py - Advanced code analysis
class CodeQualityAnalyzer:
    """Advanced code quality analysis and suggestions"""

    def __init__(self):
        self.analysis_engines = {
            'syntax': self.analyze_syntax,
            'style': self.analyze_style,
            'complexity': self.analyze_complexity,
            'performance': self.analyze_performance,
            'security': self.analyze_security
        }

    def analyze_comprehensive(self, code: str, language: str) -> Dict:
        """Perform comprehensive code analysis"""
        results = {}

        for analysis_type, analyzer in self.analysis_engines.items():
            try:
                results[analysis_type] = analyzer(code, language)
            except Exception as e:
                logger.warning(f"[ORFEAS] {analysis_type} analysis failed: {e}")
                results[analysis_type] = {'score': 0, 'issues': [str(e)]}

        # Calculate overall score
        overall_score = self.calculate_overall_score(results)

        return {
            'overall_score': overall_score,
            'metrics': results,
            'suggestions': self.generate_suggestions(results),
            'refactoring': self.identify_refactoring_opportunities(code, results)
        }

    def generate_suggestions(self, analysis_results: Dict) -> List[str]:
        """Generate actionable suggestions for code improvement"""
        suggestions = []

        for analysis_type, result in analysis_results.items():
            if result['score'] < 0.7:  # Below acceptable threshold
                suggestions.extend(result.get('suggestions', []))

        return suggestions

    def identify_refactoring_opportunities(self, code: str, analysis: Dict) -> List[Dict]:
        """Identify specific refactoring opportunities"""
        opportunities = []

        # Complexity-based refactoring
        if analysis.get('complexity', {}).get('score', 1) < 0.6:
            opportunities.append({
                'type': 'extract_method',
                'description': 'Consider extracting complex logic into separate methods',
                'priority': 'high'
            })

        # Performance-based refactoring
        if analysis.get('performance', {}).get('score', 1) < 0.7:
            opportunities.append({
                'type': 'optimize_loops',
                'description': 'Optimize loops and data structures for better performance',
                'priority': 'medium'
            })

        return opportunities

[5.8] INTELLIGENT CONTEXT PATTERNS

CONTEXT-AWARE PROCESSING WORKFLOW:

# Always build context before processing
from context_manager import IntelligentContextManager

context_mgr = IntelligentContextManager()

@app.route('/api/generate-3d', methods=['POST'])
def generate_3d_with_context():
    """Context-aware 3D generation with intelligent decision making"""

    # 1. Build comprehensive processing context
    context = context_mgr.build_processing_context({
        'image': request.files.get('image'),
        'user_id': request.headers.get('X-User-ID'),
        'quality_preference': request.form.get('quality', 7),
        'deadline': request.form.get('deadline'),
        'priority': request.form.get('priority', 'normal')
    })

    # 2. Get contextual recommendations
    recommendations = context_mgr.get_contextual_recommendations(context)

    # 3. Select optimal model based on context
    from contextual_model_selector import ContextualModelSelector
    model_selector = ContextualModelSelector()
    model_choice = model_selector.select_optimal_model(context)

    # 4. Execute with context-aware error handling
    try:
        result = execute_generation_with_context(
            model_choice['selected_model'],
            context,
            recommendations
        )

        # 5. Learn from successful execution
        model_selector.learn_from_result(context, model_choice['selected_model'], result)

        return jsonify(result)

    except Exception as e:
        # 6. Context-aware error recovery
        from contextual_error_handler import ContextualErrorHandler
        error_handler = ContextualErrorHandler(context_mgr)
        recovery_result = await error_handler.handle_error_with_context(e, context)

        return jsonify(recovery_result)

AGENT COORDINATION WITH CONTEXT:

# Coordinate multiple agents with shared context
from contextual_agent_coordinator import ContextualAgentCoordinator

async def intelligent_3d_generation_workflow(image_data: Dict):
    """Multi-agent workflow with intelligent context sharing"""

    coordinator = ContextualAgentCoordinator(context_mgr)

    # Define agents for the workflow
    workflow_agents = [
        'quality_assessment_agent',
        'model_selection_agent',
        'resource_optimization_agent',
        'generation_execution_agent'
    ]

    # Execute coordinated workflow with context
    task = {
        'type': '3d_generation',
        'input_data': image_data,
        'quality_requirements': {'min_quality': 7, 'max_time': 60}
    }

    results = await coordinator.coordinate_with_context(task, workflow_agents)

    return results

CONTEXT-AWARE MODEL SELECTION:

# Intelligent model selection based on context analysis
def select_generation_model_with_context(request_data: Dict) -> str:
    """Select optimal generation model based on comprehensive context"""

    # Build context
    context = context_mgr.build_processing_context(request_data)

    # Analyze input characteristics
    input_analysis = context['input_analysis']

    # Context-based decision tree
    if input_analysis['complexity_score'] > 0.8:
        if context['resource_context']['available_vram'] > 16000:  # > 16GB
            return 'hunyuan3d_ultra_quality'
        else:
            return 'hunyuan3d_high_quality_optimized'

    elif input_analysis['input_type'] == 'simple_object':
        if context['user_context']['speed_preference'] == 'fast':
            return 'hunyuan3d_fast'
        else:
            return 'hunyuan3d_balanced'

    elif context['system_context']['current_load'] > 0.8:
        # High system load - use lighter model
        return 'instant_mesh_lightweight'

    else:
        # Default to balanced model with context optimization
        return 'hunyuan3d_context_optimized'

CONTEXT PERSISTENCE AND LEARNING:

# Persist context for continuous learning
from context_persistence import ContextPersistenceManager

persistence_mgr = ContextPersistenceManager()

def process_with_learning(session_id: str, request_data: Dict):
    """Process request with context learning and persistence"""

    # Build context
    context = context_mgr.build_processing_context(request_data)

    # Get historical insights
    insights = persistence_mgr.get_contextual_insights(context)

    # Apply learned optimizations
    if insights['success_probability'] > 0.9:
        # High confidence - use recommended parameters
        processing_params = insights['optimal_parameters']
    else:
        # Lower confidence - use conservative parameters
        processing_params = get_conservative_parameters(context)

    # Execute processing
    start_time = time.time()
    try:
        result = execute_generation(processing_params)

        # Record successful context
        success_context = {
            **context,
            'result_quality': result['quality_score'],
            'processing_time': time.time() - start_time,
            'success': True
        }
        persistence_mgr.persist_context(session_id, success_context)

        return result

    except Exception as e:
        # Record failure context for learning
        failure_context = {
            **context,
            'error_type': type(e).__name__,
            'error_message': str(e),
            'processing_time': time.time() - start_time,
            'success': False
        }
        persistence_mgr.persist_context(session_id, failure_context)
        raise

CONTEXT-AWARE RESOURCE ALLOCATION:

# Intelligent resource allocation based on context
def allocate_resources_with_context(context: Dict) -> Dict:
    """Allocate computational resources based on context analysis"""

    allocation = {
        'gpu_memory': 8000,  # Default 8GB
        'cpu_threads': 4,    # Default 4 threads
        'batch_size': 1,     # Default single processing
        'quality_level': 7   # Default quality
    }

    # Adjust based on input complexity
    if context['input_analysis']['complexity_score'] > 0.8:
        allocation['gpu_memory'] = 12000  # 12GB for complex inputs
        allocation['quality_level'] = 9

    # Adjust based on system load
    if context['system_context']['current_load'] > 0.7:
        allocation['cpu_threads'] = 2  # Reduce CPU usage
        allocation['batch_size'] = 1   # No batching under high load

    # Adjust based on user priority
    if context.get('user_context', {}).get('priority') == 'high':
        allocation['gpu_memory'] = min(allocation['gpu_memory'] * 1.5, 16000)
        allocation['cpu_threads'] = min(allocation['cpu_threads'] * 2, 8)

    # Adjust based on deadline constraints
    deadline = context.get('quality_context', {}).get('deadline')
    if deadline and deadline < 30:  # Less than 30 seconds
        allocation['quality_level'] = 5  # Reduce quality for speed
        allocation['gpu_memory'] = 6000  # Use less memory for faster processing

        return allocation

[5.9] PERFORMANCE OPTIMIZATION PATTERNS

SPEED OPTIMIZATION WORKFLOW:

# Apply comprehensive speed optimizations
from performance_optimizer import PerformanceOptimizer

optimizer = PerformanceOptimizer()

# Model-level optimizations
@app.before_first_request
def optimize_models():
    """Apply optimizations before serving requests"""

    global hunyuan_processor

    # Apply speed optimizations to all models
    hunyuan_processor.shapegen_pipeline = optimizer.apply_speed_optimizations(
        hunyuan_processor.shapegen_pipeline, 'shape_generation'
    )

    hunyuan_processor.texgen_pipeline = optimizer.apply_speed_optimizations(
        hunyuan_processor.texgen_pipeline, 'texture_generation'
    )

    # Pre-warm caches
    optimizer.warm_model_caches()

    logger.info("[ORFEAS] Performance optimizations applied successfully")

# Request-level optimizations
@app.before_request
def optimize_request():
    """Apply per-request optimizations"""

    # Cache request context for faster processing
    g.start_time = time.time()
    g.optimization_context = optimizer.build_optimization_context(request)

    # Apply request-specific optimizations
    if g.optimization_context.get('enable_fast_mode', False):
        g.processing_mode = 'fast'
    elif g.optimization_context.get('enable_quality_mode', False):
        g.processing_mode = 'quality'
    else:
        g.processing_mode = 'balanced'

@app.after_request
def track_performance(response):
    """Track and optimize based on performance metrics"""

    processing_time = time.time() - g.get('start_time', time.time())

    # Update performance metrics
    optimizer.update_performance_metrics(
        endpoint=request.endpoint,
        processing_time=processing_time,
        mode=g.get('processing_mode', 'unknown'),
        success=response.status_code < 400
    )

    # Trigger auto-optimization if performance degrades
    if optimizer.should_auto_optimize(processing_time):
        optimizer.trigger_auto_optimization()

    return response

ACCURACY ENHANCEMENT PATTERNS:

# Quality-first generation workflow
from accuracy_enhancer import AccuracyEnhancer

enhancer = AccuracyEnhancer()

def generate_high_accuracy_3d(image_data: bytes, quality_requirements: Dict) -> Dict:
    """Generate 3D model with maximum accuracy focus"""

    # Build accuracy-focused context
    context = {
        'accuracy_priority': True,
        'quality_requirements': quality_requirements,
        'enable_ensemble': quality_requirements.get('enable_ensemble', False),
        'enable_refinement': quality_requirements.get('enable_refinement', True)
    }

    # Multi-stage accuracy enhancement
    try:
        # Stage 1: Input analysis and preprocessing
        preprocessed_input = enhancer.preprocess_for_accuracy(
            {'image': image_data}, context
        )

        # Stage 2: Enhanced generation
        result = enhancer.enhance_generation_accuracy(preprocessed_input, context)

        # Stage 3: Quality validation
        if result['accuracy_metrics']['quality_score'] < 0.85:
            logger.warning("[ORFEAS] Quality below threshold, applying refinement")
            result = enhancer.apply_advanced_refinement(result, context)

        return result

    except Exception as e:
        logger.error(f"[ORFEAS] High-accuracy generation failed: {e}")
        # Fallback to standard generation with basic enhancement
        return enhancer.generate_with_fallback_enhancement(preprocessed_input)

# Quality monitoring and feedback loop
@app.route('/api/quality-feedback', methods=['POST'])
def submit_quality_feedback():
    """Receive user quality feedback for continuous improvement"""

    feedback = request.get_json()

    # Update accuracy models based on feedback
    enhancer.update_quality_models(
        generation_id=feedback['generation_id'],
        user_rating=feedback['rating'],
        specific_issues=feedback.get('issues', []),
        improvements_suggested=feedback.get('improvements', [])
    )

    # Trigger model retraining if needed
    if enhancer.should_retrain_quality_models():
        enhancer.schedule_quality_model_retraining()

    return jsonify({'status': 'feedback_received', 'quality_updated': True})

SECURITY OPTIMIZATION PATTERNS:

# Multi-layer security validation
from security_hardening import SecurityHardening

security = SecurityHardening()

def secure_3d_generation_workflow(image_file, user_context: Dict) -> Dict:
    """Secure 3D generation with comprehensive protection"""

    # Layer 1: Input validation and sanitization
    try:
        validated_file = security.comprehensive_file_validation(image_file)
    except SecurityError as e:
        logger.security(f"[ORFEAS-SECURITY] File validation failed: {e}")
        raise ValidationError("File failed security validation")

    # Layer 2: User authentication and authorization
    if not security.validate_user_permissions(user_context, 'generate_3d'):
        logger.security(f"[ORFEAS-SECURITY] Unauthorized access attempt from {user_context}")
        raise AuthorizationError("Insufficient permissions")

    # Layer 3: Rate limiting and abuse protection
    if not security.check_rate_limits(user_context['user_id'], 'generation'):
        logger.security(f"[ORFEAS-SECURITY] Rate limit exceeded for user {user_context['user_id']}")
        raise RateLimitError("Generation rate limit exceeded")

    # Layer 4: Secure processing environment
    with security.secure_processing_context() as secure_env:
        result = process_generation_securely(validated_file, secure_env)

    # Layer 5: Output sanitization
    sanitized_result = security.sanitize_generation_output(result)

    # Layer 6: Audit logging
    security.log_secure_operation(
        operation='3d_generation',
        user=user_context['user_id'],
        input_hash=security.hash_input(validated_file),
        output_hash=security.hash_output(sanitized_result),
        success=True
    )

    return sanitized_result

# Real-time threat detection
@app.before_request
def detect_threats():
    """Real-time threat detection and mitigation"""

    threat_level = security.assess_request_threat_level(request)

    if threat_level >= security.THREAT_HIGH:
        # Block immediately and alert
        security.block_request_and_alert(request, threat_level)
        abort(403, "Request blocked due to security threat")

    elif threat_level >= security.THREAT_MEDIUM:
        # Enhanced monitoring
        security.enable_enhanced_monitoring(request)
        g.security_enhanced = True

    # Log all security assessments
    security.log_threat_assessment(request, threat_level)

ML INTEGRATION OPTIMIZATION PATTERNS:

# Optimized ML pipeline execution
from ml_integration_optimizer import MLIntegrationOptimizer

ml_optimizer = MLIntegrationOptimizer()

def optimized_ai_generation_pipeline(input_data: Dict, performance_profile: str) -> Dict:
    """Execute AI generation with optimal ML integration"""

    # Select optimal pipeline configuration
    pipeline_config = ml_optimizer.get_optimal_pipeline_config(performance_profile)

    # Apply ML-specific optimizations
    optimized_pipeline = ml_optimizer.optimize_ml_pipeline(pipeline_config)

    # Execute with intelligent resource management
    with ml_optimizer.managed_ml_execution(optimized_pipeline) as executor:

        # Pre-execution optimizations
        executor.prepare_models(input_data)
        executor.allocate_optimal_resources()

        # Parallel execution stages
        if optimized_pipeline['parallel_stages']:
            results = executor.execute_parallel_stages(
                input_data, optimized_pipeline['parallel_stages']
            )
        else:
            results = executor.execute_sequential_pipeline(input_data)

        # Post-execution cleanup
        executor.cleanup_resources()

    return results

# Adaptive model selection based on real-time performance
class AdaptiveModelSelector:
    def __init__(self):
        self.performance_history = {}
        self.model_rankings = {}

    def select_optimal_model(self, task_context: Dict) -> str:
        """Select best model based on current conditions"""

        # Analyze current system state
        system_state = self.analyze_system_state()

        # Consider task requirements
        task_requirements = self.extract_task_requirements(task_context)

        # Historical performance analysis
        historical_performance = self.get_historical_performance(
            task_requirements, system_state
        )

        # Multi-objective model selection
        model_scores = {}
        for model_name in self.available_models:
            scores = {
                'speed_score': self.calculate_speed_score(model_name, system_state),
                'accuracy_score': self.calculate_accuracy_score(model_name, task_context),
                'resource_score': self.calculate_resource_score(model_name, system_state),
                'reliability_score': self.calculate_reliability_score(model_name)
            }

            # Weighted combination based on priorities
            final_score = self.combine_scores(scores, task_requirements)
            model_scores[model_name] = final_score

        # Select best model
        optimal_model = max(model_scores, key=model_scores.get)

        # Update selection history
        self.update_selection_history(optimal_model, task_context, system_state)

        return optimal_model

# Initialize adaptive components
adaptive_selector = AdaptiveModelSelector()

@app.route('/api/adaptive-generate', methods=['POST'])
def adaptive_3d_generation():
    """AI generation with adaptive optimization"""

    # Extract request context
    task_context = {
        'image_complexity': analyze_image_complexity(request.files['image']),
        'quality_preference': request.form.get('quality', 7),
        'speed_preference': request.form.get('speed_preference', 'balanced'),
        'user_priority': get_user_priority(request.headers.get('X-User-ID'))
    }

    # Select optimal model adaptively
    optimal_model = adaptive_selector.select_optimal_model(task_context)

    # Execute with selected optimization profile
    performance_profile = determine_performance_profile(task_context)
    result = optimized_ai_generation_pipeline(
        {'image': request.files['image']},
        performance_profile
    )

    # Update performance tracking
    adaptive_selector.update_performance_tracking(optimal_model, result)

    return jsonify(result)

[6] CRITICAL INTEGRATION POINTS### [6.1] HUNYUAN3D-2.1 MODELS

Model Location:

Hunyuan3D-2.1/Hunyuan3D-2/
├ hunyuan3d-dit-v2-1/        # Shape generation (3.3GB)
├ hunyuan3d-paintpbr-v2-1/   # Texture generation (2GB)
└ hy3dgen/                   # Core generation pipelines

VRAM Requirements:

Shape generation only: 10GB
Texture generation only: 21GB
Full pipeline (shape + texture): 29GB
Model cache overhead: ~8GB (persistent)

Environment Variables:

HUNYUAN3D_PATH=../Hunyuan3D-2.1
DEVICE=cuda                    # auto, cuda, cpu
GPU_MEMORY_LIMIT=0.8           # 80% safety margin
XFORMERS_DISABLED=1            # Prevent DLL crash

[6.2] DOCKER GPU ACCELERATION

CRITICAL: NVIDIA Container Runtime Required

# docker-compose.yml
services:
  backend:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Verify GPU access:

docker exec orfeas-backend-production nvidia-smi

[6.3] WEBSOCKET COMMUNICATION

Progress Updates Pattern:

# Backend: Emit progress during generation
from flask_socketio import emit

@socketio.on('start_generation')
def handle_generation(data):
    for progress in range(0, 100, 10):
        emit('generation_progress', {
            'job_id': job_id,
            'progress': progress,
            'stage': 'shape_generation',
            'eta_seconds': estimated_remaining
        })

// Frontend: Listen for updates
socket.on("generation_progress", (data) => {
  updateProgressBar(data.progress);
  updateETA(data.eta_seconds);
});

[6.4] FILE UPLOAD VALIDATION

Security-Critical Patterns:

from validation import FileUploadValidator

validator = FileUploadValidator()

# ALWAYS validate before processing
image = validator.validate_image(request.files['file'])
# Checks: file type, size, dimensions, malicious content

# NEVER trust user input
filename = secure_filename(request.files['file'].filename)

Allowed formats:

Images: PNG, JPG, JPEG, GIF, BMP, TIFF, WEBP
3D models: STL, OBJ, PLY, GLTF, FBX
Max size: 50MB (configurable via
```
MAX_FILE_SIZE
```
)

[6.5] AI AGENT ORCHESTRATION

Agent Service Discovery:

# backend/agent_discovery.py
class AgentServiceDiscovery:
    def __init__(self):
        self.registered_agents = {}
        self.agent_capabilities = {}

    def register_agent(self, agent_id: str, capabilities: List[str], endpoint: str):
        """Register AI agent with its capabilities"""
        self.registered_agents[agent_id] = endpoint
        self.agent_capabilities[agent_id] = capabilities

    def find_agents_for_task(self, required_capability: str) -> List[str]:
        """Find agents capable of handling specific task"""
        capable_agents = []
        for agent_id, capabilities in self.agent_capabilities.items():
            if required_capability in capabilities:
                capable_agents.append(agent_id)
        return capable_agents

Agent Communication Protocol:

# Inter-agent communication for complex workflows
async def coordinate_generation_workflow(image_data: bytes, quality_requirements: Dict):
    """Coordinate multiple AI agents for optimal 3D generation"""

    # 1. Quality assessment agent analyzes input
    quality_agent = await get_agent('quality_assessment')
    analysis = await quality_agent.analyze_input(image_data)

    # 2. Workflow agent selects optimal pipeline
    workflow_agent = await get_agent('workflow_orchestration')
    pipeline = await workflow_agent.select_pipeline(analysis)

    # 3. Optimization agent configures parameters
    optimization_agent = await get_agent('performance_optimization')
    optimized_params = await optimization_agent.optimize_parameters(pipeline, analysis)

    # 4. Execute generation with optimized settings
    result = await execute_generation_pipeline(pipeline, optimized_params)

    return result

[6.6] ALTERNATIVE AI MODEL INTEGRATION

Multi-Model Fallback Chain:

# backend/multi_model_manager.py
class MultiModelGenerationManager:
    def __init__(self):
        self.model_chain = [
            'hunyuan3d_2_1',      # Primary model
            'instant_mesh',        # HuggingFace fallback
            'stable_diffusion_3d', # SD-based 3D generation
            'comfyui_workflow'     # ComfyUI alternative
        ]

    async def generate_with_fallback(self, input_data: Dict) -> Dict:
        """Try models in order until one succeeds"""
        last_error = None

        for model_name in self.model_chain:
            try:
                logger.info(f"[ORFEAS] Attempting generation with {model_name}")
                result = await self.generate_with_model(model_name, input_data)

                # Validate result quality
                if self.validate_generation_quality(result):
                    logger.info(f"[ORFEAS] Successful generation with {model_name}")
                    return result
                else:
                    logger.warning(f"[ORFEAS] {model_name} produced low-quality result")
                    continue

            except Exception as e:
                logger.warning(f"[ORFEAS] {model_name} failed: {e}")
                last_error = e
                continue

        # All models failed
        logger.error(f"[ORFEAS] All generation models failed. Last error: {last_error}")
        raise RuntimeError("All generation methods exhausted")

Model Performance Monitoring:

# Track performance metrics for each AI model
from production_metrics import ModelPerformanceTracker

perf_tracker = ModelPerformanceTracker()

@perf_tracker.track_model_performance
async def generate_with_model(model_name: str, input_data: Dict):
    """Generate 3D model with performance tracking"""
    start_time = time.time()

    try:
        model = await load_model(model_name)
        result = await model.generate(input_data)

        # Track success metrics
        duration = time.time() - start_time
        perf_tracker.record_success(model_name, duration, result.quality_score)

        return result

    except Exception as e:
        # Track failure metrics
        duration = time.time() - start_time
        perf_tracker.record_failure(model_name, duration, str(e))
        raise

[6.7] INTELLIGENT CONTEXT INTEGRATION

CONTEXT DATABASE SETUP:

# backend/context_database.py
class ContextDatabase:
    """
    Context persistence and retrieval system
    """

    def __init__(self, db_path: str = "data/context.db"):
        self.db_path = db_path
        self.connection = self.initialize_database()

    def initialize_database(self):
        """Create context tables if they don't exist"""
        conn = sqlite3.connect(self.db_path)
        conn.execute("""
            CREATE TABLE IF NOT EXISTS context_history (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                session_id TEXT NOT NULL,
                timestamp REAL NOT NULL,
                context_data TEXT NOT NULL,
                context_hash TEXT NOT NULL,
                success BOOLEAN NOT NULL,
                processing_time REAL,
                quality_score REAL,
                model_used TEXT,
                resource_usage TEXT
            )
        """)
        conn.execute("""
            CREATE INDEX IF NOT EXISTS idx_context_hash ON context_history(context_hash);
        """)
        conn.execute("""
            CREATE INDEX IF NOT EXISTS idx_timestamp ON context_history(timestamp);
        """)
        return conn

CONTEXT-AWARE INITIALIZATION:

# Always initialize context manager at startup
from context_manager import IntelligentContextManager
from contextual_agent_coordinator import ContextualAgentCoordinator

def initialize_context_system():
    """Initialize intelligent context handling system"""

    # Initialize core context manager
    context_mgr = IntelligentContextManager()

    # Initialize agent coordinator with context
    agent_coordinator = ContextualAgentCoordinator(context_mgr)

    # Register available agents
    agent_coordinator.register_agent(
        'quality_assessment',
        ['image_analysis', 'complexity_scoring'],
        'http://localhost:5001/quality'
    )

    agent_coordinator.register_agent(
        'model_selection',
        ['model_recommendation', 'performance_prediction'],
        'http://localhost:5002/model-select'
    )

    # Start context learning background task
    if os.getenv('CONTEXT_LEARNING_ENABLED') == 'true':
        context_mgr.start_learning_daemon()

    return context_mgr, agent_coordinator

# Initialize at application startup
context_manager, agent_coordinator = initialize_context_system()

CONTEXT MIDDLEWARE INTEGRATION:

# Flask middleware for automatic context building
from flask import g

@app.before_request
def build_request_context():
    """Build context for every request automatically"""

    if not hasattr(g, 'context'):
        request_data = {
            'endpoint': request.endpoint,
            'method': request.method,
            'user_agent': request.headers.get('User-Agent'),
            'ip_address': request.remote_addr,
            'timestamp': time.time()
        }

        # Add file data if present
        if request.files:
            request_data['files'] = {
                name: {
                    'filename': file.filename,
                    'size': len(file.read()),
                    'content_type': file.content_type
                } for name, file in request.files.items()
            }
            # Reset file streams
            for file in request.files.values():
                file.seek(0)

        # Build comprehensive context
        g.context = context_manager.build_processing_context(request_data)
        g.recommendations = context_manager.get_contextual_recommendations(g.context)

@app.after_request
def update_context_learning(response):
    """Update context learning based on request outcome"""

    if hasattr(g, 'context') and hasattr(g, 'processing_result'):
        # Update learning based on request success/failure
        success = response.status_code < 400

        learning_data = {
            'context': g.context,
            'success': success,
            'status_code': response.status_code,
            'processing_time': getattr(g, 'processing_time', 0),
            'response_size': len(response.get_data())
        }

        context_manager.update_learning(learning_data)

    return response

CONTEXT-BASED MONITORING:

# Enhanced monitoring with context awareness
from prometheus_client import Counter, Histogram, Gauge

# Context-aware metrics
context_processing_time = Histogram(
    'context_processing_seconds',
    'Time spent on context processing',
    ['context_type', 'complexity_level']
)

context_accuracy = Gauge(
    'context_prediction_accuracy',
    'Accuracy of context-based predictions',
    ['prediction_type']
)

context_cache_hits = Counter(
    'context_cache_hits_total',
    'Number of context cache hits',
    ['cache_type']
)

def track_context_performance(func):
    """Decorator to track context-aware performance"""
    def wrapper(*args, **kwargs):
        context = g.get('context', {})
        complexity = context.get('input_analysis', {}).get('complexity_score', 0)

        complexity_level = 'simple' if complexity < 0.3 else 'medium' if complexity < 0.7 else 'complex'

        with context_processing_time.labels(
            context_type=context.get('input_analysis', {}).get('input_type', 'unknown'),
            complexity_level=complexity_level
        ).time():
            result = func(*args, **kwargs)

        return result
    return wrapper

[7] PRODUCTION DEPLOYMENT & ENTERPRISE MARKET POSITIONING

COMPREHENSIVE PRODUCTION DEPLOYMENT GUIDE:

For complete production deployment, enterprise sales enablement, industry partnerships, and competitive market positioning documentation, see:

📄

md/ORFEAS_PRODUCTION_DEPLOYMENT_OPTIMIZATION.md

This comprehensive document includes:

Enterprise Production Deployment Architecture with 99.99% SLA guarantees
Enterprise Sales & Customer Success Platform with ROI calculators
Strategic Technology Partnerships with Microsoft, Google, AWS, NVIDIA
Competitive Market Positioning targeting $1B revenue by 2027
Industry Leadership Strategy with thought leadership initiatives

Key Production Metrics:

Total Addressable Market: $5.5 Billion
Five-Year Revenue Target: $1 Billion (18% market share)
Enterprise SLA: 99.99% uptime with automatic credits
ROI Guarantee: 300-500% productivity improvements
Market Position: Industry-leading AI multimedia platform

Enterprise Deployment Capabilities:

Multi-region Kubernetes deployment with auto-scaling
Enterprise-grade security with SOC2, ISO27001, GDPR, HIPAA compliance
24/7 platinum support with dedicated success managers
Strategic partnerships for global market expansion
Competitive intelligence and market positioning strategies

══════════════════════════════════════════════════════════════════════════════╗

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