Phase 0 is complete

Dockerfile

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+# Multi-stage Docker build for FoodSnap AI
+FROM node:18-alpine AS frontend
+
+WORKDIR /app/frontend
+COPY frontend/package*.json ./
+RUN npm ci --only=production
+
+COPY frontend/ ./
+RUN npm run build
+
+# Backend with Python
+FROM python:3.9-slim AS backend
+
+WORKDIR /app
+COPY backend/requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY backend/ ./
+COPY --from=frontend /app/frontend/dist ./static
+
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

backend/.env.example

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+# Environment Configuration
+ENVIRONMENT=development
+
+# Server Configuration
+HOST=0.0.0.0
+PORT=8000
+
+# API Configuration
+API_V1_PREFIX=/api/v1
+
+# USDA FoodData Central API Key (REQUIRED for real nutrition data)
+# Get your free API key from: https://fdc.nal.usda.gov/api-key-signup.html
+USDA_API_KEY=your_usda_api_key_here
+
+# Logging Configuration
+LOG_LEVEL=info
+
+# CORS Configuration (additional origins can be added here)
+ALLOWED_ORIGINS=http://localhost:3000,http://127.0.0.1:3000,http://localhost:5173,http://127.0.0.1:5173
+
+# File Upload Configuration
+MAX_FILE_SIZE=10485760  # 10MB in bytes
+ALLOWED_IMAGE_TYPES=jpg,jpeg,png,gif,bmp,tiff

backend/README.md

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+# FoodSnap AI Backend
+
+FastAPI backend service for FoodSnap AI application.
+
+## Overview
+
+This backend provides REST API endpoints for food image analysis and nutrition information. Built with FastAPI for high performance and automatic API documentation.
+
+## Features
+
+- ✅ FastAPI with automatic OpenAPI documentation
+- ✅ CORS enabled for frontend integration
+- ✅ Environment variable configuration
+- ✅ Health check endpoints
+- ✅ Structured folder organization
+- 🚧 ML model integration (placeholder)
+- 🚧 Nutrition API integration (placeholder)
+
+## Project Structure
+
+```
+backend/
+├── main.py              # FastAPI application entry point
+├── requirements.txt     # Python dependencies
+├── .env.example        # Environment variables template
+├── README.md           # This file
+├── model/              # ML models and utilities
+│   └── __init__.py     # Package initialization
+├── services/           # Business logic services
+│   └── __init__.py     # Package initialization
+└── utils/              # Utility functions
+    └── __init__.py     # Package initialization
+```
+
+## Setup Instructions
+
+### 1. Install Dependencies
+
+```bash
+# Create virtual environment
+python -m venv venv
+
+# Activate virtual environment
+# On Windows:
+venv\Scripts\activate
+# On macOS/Linux:
+source venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 2. Environment Configuration
+
+```bash
+# Copy environment template
+cp .env.example .env
+
+# Edit .env file with your configuration
+# Add USDA API key when ready to implement nutrition features
+```
+
+### 3. Run the Server
+
+```bash
+# Development mode with auto-reload
+python main.py
+
+# Or using uvicorn directly
+uvicorn main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+## API Endpoints
+
+### Health Checks
+
+- `GET /` - Basic health check with API info
+- `GET /health` - Detailed health check with service status
+
+### Food Analysis
+
+#### `POST /analyze-food` - Standardized Food Analysis
+**Main production endpoint with clean response format**
+
+**Request:** `multipart/form-data` with `file` field
+
+**Response:**
+```json
+{
+  "food": "pizza",
+  "confidence": 0.87,
+  "calories": 266,
+  "protein": 11,
+  "carbs": 33,
+  "fat": 10,
+  "unit": "per 100g"
+}
+```
+
+#### `POST /analyze-food-debug` - Detailed Food Analysis
+**Debug endpoint with full metadata and alternatives**
+
+**Request:** `multipart/form-data` with `file` field
+
+**Response:**
+```json
+{
+  "food": "pizza",
+  "confidence": 0.87,
+  "calories": 266,
+  "protein": 11,
+  "carbs": 33,
+  "fat": 10,
+  "unit": "per 100g",
+  "debug": {
+    "alternatives": ["flatbread", "garlic bread"],
+    "threshold_met": true,
+    "inference_time_ms": 45.2,
+    "model_info": {...},
+    "nutrition_source": "usda_api",
+    "file_info": {...},
+    "service_status": {...}
+  }
+}
+```
+
+### Error Handling
+
+The API uses proper HTTP status codes:
+
+| Status Code | Description | Example |
+|------------|-------------|---------|
+| `200` | Success | Food analysis completed |
+| `400` | Bad Request | Invalid file type, empty file, invalid image |
+| `413` | Payload Too Large | File size exceeds 10MB limit |
+| `500` | Internal Server Error | Unexpected processing error |
+
+**Error Response Format:**
+```json
+{
+  "detail": "Invalid file type. Please upload an image file (JPG, PNG, etc.)"
+}
+```
+
+## Configuration
+
+The server configuration is controlled by environment variables:
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `ENVIRONMENT` | `development` | Environment mode |
+| `HOST` | `0.0.0.0` | Server host |
+| `PORT` | `8000` | Server port |
+| `USDA_API_KEY` | - | USDA API key for nutrition data |
+| `LOG_LEVEL` | `info` | Logging level |
+
+## Development Notes
+
+### Adding New Features
+
+1. **API Routes**: Create new route files in appropriate packages
+2. **Services**: Implement business logic in `services/` package
+3. **Models**: Add ML models in `model/` package
+4. **Utils**: Add helper functions in `utils/` package
+
+### Code Organization
+
+- **main.py**: Application setup, middleware, basic endpoints
+- **model/**: Machine learning models and inference logic
+- **services/**: Business logic and external API integrations
+- **utils/**: Helper functions, validators, utilities
+
+## Future Implementation
+
+The following features are planned for future iterations:
+
+1. **ML Model Integration**
+   - Food classification using ResNet50/MobileNetV2
+   - Image preprocessing pipeline
+   - Model inference endpoints
+
+2. **Nutrition API Integration**
+   - USDA FoodData Central API client
+   - Nutrition data caching
+   - Food database queries
+
+3. **File Upload Handling**
+   - Image validation and processing
+   - Temporary file management
+   - Error handling
+
+4. **Database Integration**
+   - User data storage
+   - Analysis history
+   - Caching layer
+
+## Testing
+
+```bash
+# Run health check
+curl http://localhost:8000/
+
+# Run detailed health check
+curl http://localhost:8000/health
+
+# View API documentation
+# Open http://localhost:8000/docs in browser
+```
+
+## Deployment
+
+For production deployment:
+
+1. Set `ENVIRONMENT=production`
+2. Configure proper CORS origins
+3. Add USDA API key
+4. Use proper WSGI server (Gunicorn/Uvicorn)
+5. Set up monitoring and logging
+
+## License
+
+This project is part of FoodSnap AI MVP.