Nano Banana Pro
Agent skill for nano-banana-pro
`docker compose up` to run the docker container
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#commands
docker compose up to run the docker container
make build to build the solution
make run MLX_BACKEND=1 to run the solution with MLX_BACKEND instead of
make empty to run an empty container to install dependencies
#package installs
docker exec backend /bin/bash to enter the backend container
pip install PACKAGE to install dependencies
When installing packages, conda env export >> linux_environment.yml after installing to freeze installs.
#frontend package installs
cd client & npm i PACKAGE
#running tests
cd /opt/geist && PYTHONPATH=/opt/geist pytest in the backend container
#SQLAlchemy When adding classes to sql alchemy, take the following example EXAMPLE: import datetime from sqlalchemy import Column, Integer, String, ForeignKey, LargeBinary, DateTime, Boolean, ARRAY, DateTime from sqlalchemy.orm import relationship, Session from app.models.database.database import Base, Session from sqlalchemy.dialects.postgresql import insert
class AgentPreset(Base): """ Class used to represent a preset for an agent. """ tablename = 'agent_preset' agent_preset_id = Column(Integer, primary_key=True, autoincrement=True) #language model presets name = Column(String) version = Column(String) description = Column(String) max_tokens = Column(Integer) n = Column(Integer) temperature = Column(Integer) top_p = Column(Integer) frequency_penalty = Column(Integer) presence_penalty = Column(Integer) tags = Column(String) #memory presets working_context_length = Column(Integer) long_term_context_length = Column(Integer) agent_type = Column(String) #prompt presets prompt = Column(String) #interactive_only - is not an independent agent. interactive_only = Column(Boolean) # optional processing settings process_world = Column(Boolean) #restriction relationships restrictions = relationship("Restriction", back_populates="agent_preset") create_date = Column(DateTime) update_date = Column(DateTime)
classes are stored in app >> models >> database.
#Adding Models Models should be added to scripts/copy_weights.py as well as the associated agent implementation inheriting from agents/base_agent. (GPT4 Agent, llama_agent, etc.)
#preferences prefer minimal inline implementations over extra dependency imports. Core libraries are better than pypi packages.
#SDLC
docker compose up -d, then verifying no error logs in the docker container, then doing a curl command to localhost:3000