CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

FitSpice / Homeland Meals is a full-stack nutrition and recipe application focused on South Asian cuisine. It helps users track nutrition, convert traditional recipes to quick versions, and analyze food images using AI.

Architecture

Frontend (

/frontend

React 19 application with React Router for navigation
Tailwind CSS for styling with Craco build configuration
Context-based state management for meal tray functionality
Static JSON data files for recipes and ingredient pricing
Axios for API communication

Backend (

/backend

FastAPI server with MongoDB database integration
Custom LLM integration via Groq API using Llama models for food analysis and recipe conversion
Co-pilot chat functionality with professional ChatGPT-style interface
RESTful API with
```
/api
```
prefix for all endpoints
Pydantic models for data validation

Development Commands

Frontend Development:

cd frontend
yarn install
yarn start    # Development server
yarn build    # Production build
yarn test     # Run tests

Backend Development:

cd backend
pip install -r requirements.txt
uvicorn server:app --reload --host 0.0.0.0 --port 8000

Testing:

# Run comprehensive backend API tests
python backend_test.py

Code Quality:

# Backend linting and formatting
cd backend
black .
isort .
flake8 .
mypy .

# Frontend linting
cd frontend
npm run lint  # Uses ESLint

Key API Endpoints

Core Functionality:

```
POST /api/profile
```
- Create user profile with calculated BMR and calorie targets
```
POST /api/analyze-food
```
- Upload food image for AI nutritional analysis
```
POST /api/recipe
```
- Create recipe with AI-powered quick version conversion
```
GET /api/daily-stats/{user_id}/{date}
```
- Get daily nutrition and fitness statistics

GET /api/ingredient-substitutions/{ingredient}

- Get Western grocery substitutions

Co-pilot Features:

```
POST /api/copilot/chat
```
- Interactive chat assistant for cooking questions and guidance
```
POST /api/copilot/recipe-suggestions
```
- Get recipe suggestions based on available ingredients
```
POST /api/copilot/cooking-guidance
```
- Get step-by-step cooking guidance for recipes

Database Collections

```
user_profiles
```
- User BMR calculations and dietary goals
```
food_entries
```
- AI-analyzed food intake with nutritional data
```
workout_entries
```
- Exercise logging with calorie burn estimates
```
recipes
```
- Traditional and AI-converted quick recipes

Environment Configuration

The application requires

.env

files in both frontend and backend directories:

Backend: MongoDB connection, LLM API keys, CORS settings
Frontend: Backend API URL configuration

Important Implementation Notes

AI Integration:

Custom Groq API integration using Llama-3.1-8b-instant model
Co-pilot chat interface with natural language processing
Recipe suggestions and cooking guidance powered by AI
All AI responses are structured for consistent parsing
Fallback handling when AI parsing fails

Nutrition Calculations:

BMR calculated using Mifflin-St Jeor equation
Activity level multipliers for daily calorie targets
Goal-based calorie adjustments (±500 for weight goals)

Frontend State Management:

```
MealTrayContext
```
manages recipe selection and cost estimation
User cookbook data stored in localStorage
Recipe and ingredient data loaded from JSON files

File Structure Highlights

```
/frontend/src/data/
```
- Static JSON files for recipes and ingredient pricing
```
/frontend/src/App.js
```
- Main React application with co-pilot chat interface
```
/frontend/src/App.css
```
- Custom styling including professional chat UI
```
/backend/server.py
```
- Main FastAPI application with all API endpoints
```
/backend/emergentintegrations/
```
- Custom LLM integration module
```
/backend_test.py
```
- Comprehensive API testing suite
```
/tests/
```
- Additional test utilities

Co-pilot Features

Chat Interface:

Professional ChatGPT-style design with message bubbles
Real-time AI responses for cooking questions
Markdown rendering for structured responses
Two-mode interface: general chat and recipe suggestions

Recipe Intelligence:

Ingredient-based recipe suggestions
Step-by-step cooking guidance
Traditional to quick recipe conversion
Nutritional analysis and dietary adaptations

UI/UX Improvements:

Clean, modern interface with proper contrast
Responsive design for mobile and desktop
Accessible color schemes and typography
Branded as "Nutrichef AI"

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

Architecture

Frontend (

/frontend

React 19 application with React Router for navigation
Tailwind CSS for styling with Craco build configuration
Context-based state management for meal tray functionality
Static JSON data files for recipes and ingredient pricing
Axios for API communication

Backend (

/backend

FastAPI server with MongoDB database integration
Custom LLM integration via Groq API using Llama models for food analysis and recipe conversion
Co-pilot chat functionality with professional ChatGPT-style interface
RESTful API with
```
/api
```
prefix for all endpoints
Pydantic models for data validation

Development Commands

Frontend Development:

cd frontend
yarn install
yarn start    # Development server
yarn build    # Production build
yarn test     # Run tests

Backend Development:

cd backend
pip install -r requirements.txt
uvicorn server:app --reload --host 0.0.0.0 --port 8000

Testing:

# Run comprehensive backend API tests
python backend_test.py

Code Quality:

# Backend linting and formatting
cd backend
black .
isort .
flake8 .
mypy .

# Frontend linting
cd frontend
npm run lint  # Uses ESLint

Key API Endpoints

Core Functionality:

```
POST /api/profile
```
- Create user profile with calculated BMR and calorie targets
```
POST /api/analyze-food
```
- Upload food image for AI nutritional analysis
```
POST /api/recipe
```
- Create recipe with AI-powered quick version conversion
```
GET /api/daily-stats/{user_id}/{date}
```
- Get daily nutrition and fitness statistics

GET /api/ingredient-substitutions/{ingredient}

- Get Western grocery substitutions

Co-pilot Features:

```
POST /api/copilot/chat
```
- Interactive chat assistant for cooking questions and guidance
```
POST /api/copilot/recipe-suggestions
```
- Get recipe suggestions based on available ingredients
```
POST /api/copilot/cooking-guidance
```
- Get step-by-step cooking guidance for recipes

Database Collections

```
user_profiles
```
- User BMR calculations and dietary goals
```
food_entries
```
- AI-analyzed food intake with nutritional data
```
workout_entries
```
- Exercise logging with calorie burn estimates
```
recipes
```
- Traditional and AI-converted quick recipes

Environment Configuration

The application requires

.env

files in both frontend and backend directories:

Backend: MongoDB connection, LLM API keys, CORS settings
Frontend: Backend API URL configuration

Important Implementation Notes

AI Integration:

Custom Groq API integration using Llama-3.1-8b-instant model
Co-pilot chat interface with natural language processing
Recipe suggestions and cooking guidance powered by AI
All AI responses are structured for consistent parsing
Fallback handling when AI parsing fails

Nutrition Calculations:

BMR calculated using Mifflin-St Jeor equation
Activity level multipliers for daily calorie targets
Goal-based calorie adjustments (±500 for weight goals)

Frontend State Management:

```
MealTrayContext
```
manages recipe selection and cost estimation
User cookbook data stored in localStorage
Recipe and ingredient data loaded from JSON files

File Structure Highlights

```
/frontend/src/data/
```
- Static JSON files for recipes and ingredient pricing
```
/frontend/src/App.js
```
- Main React application with co-pilot chat interface
```
/frontend/src/App.css
```
- Custom styling including professional chat UI
```
/backend/server.py
```
- Main FastAPI application with all API endpoints
```
/backend/emergentintegrations/
```
- Custom LLM integration module
```
/backend_test.py
```
- Comprehensive API testing suite
```
/tests/
```
- Additional test utilities

Co-pilot Features

Chat Interface:

Professional ChatGPT-style design with message bubbles
Real-time AI responses for cooking questions
Markdown rendering for structured responses
Two-mode interface: general chat and recipe suggestions

Recipe Intelligence:

Ingredient-based recipe suggestions
Step-by-step cooking guidance
Traditional to quick recipe conversion
Nutritional analysis and dietary adaptations

UI/UX Improvements:

Clean, modern interface with proper contrast
Responsive design for mobile and desktop
Accessible color schemes and typography
Branded as "Nutrichef AI"

CLAUDE.md

CLAUDE.md

Project Overview

Architecture

Development Commands

Key API Endpoints

Database Collections

Environment Configuration

Important Implementation Notes

File Structure Highlights

Co-pilot Features

Related Skills

Nano Banana Pro

Markdown Converter

1password

CLAUDE.md

Project Overview

Architecture

Development Commands

Key API Endpoints

Database Collections

Environment Configuration

Important Implementation Notes

File Structure Highlights

Co-pilot Features