AI Stylist App Backend

A robust and scalable FastAPI backend for the AI Stylist App that provides personalized fashion advice through an AI chat interface, wardrobe management, and user profile customization.

Front-End Of Lulu AI Fashion Stylist

Docs

Features

• 🤖 AI Chat Interface: Personalized outfit suggestions and fashion tips using OpenAI integration
• 👕 Wardrobe Management: Digital wardrobe organization with image upload and categorization
• 👤 User Profile Management: Detailed user preferences and measurements storage
• 🔐 JWT Authentication: Secure access with refresh token support
• 📦 Microservices Architecture: Scalable design with separate services
• ☁️ Cloud Storage: AWS S3 integration for image storage
• 📊 Dual Database: PostgreSQL for structured data, MongoDB for chat history

Technology Stack

• Backend Framework: FastAPI
• Databases:
  • PostgreSQL (User data, Wardrobe items)
  • MongoDB (Chat sessions, Messages)
• Cloud Services:
  • AWS S3 (Image storage)
  • OpenAI API (Chat intelligence)
• Development Tools:
  • Docker & Docker Compose
  • Alembic (Database migrations)
  • GitHub Actions (CI/CD)

Prerequisites

• Python 3.8+
• Docker and Docker Compose
• PostgreSQL
• MongoDB
• AWS Account (for S3)
• OpenAI API Key

Installation

1. Clone the Repository:

git clone https://github.com/yourusername/ai-stylist-backend.git
cd ai-stylist-backend

2. Environment Setup: Create a .env file in the root directory with the following variables:

# Database Configuration
DATABASE_URL=postgresql://<username>:<password>@localhost:5432/ai_fashion_app
POSTGRES_USER=<username>
POSTGRES_PASSWORD=<password>
POSTGRES_DB=ai_fashion_app

# MongoDB Configuration
MONGODB_URL=mongodb://localhost:27017
MONGODB_DB_NAME=ai_chat
MONGODB_HOST=mongodb

# JWT Configuration
SECRET_KEY=<your-secret-key>
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
REFRESH_TOKEN_EXPIRE_DAYS=7
REFRESH_SECRET_KEY=<your-refresh-secret-key>

# AWS Configuration
AWS_ACCESS_KEY_ID=<your-aws-access-key>
AWS_SECRET_ACCESS_KEY=<your-aws-secret-key>
AWS_REGION=us-east-1
AWS_S3_BUCKET=aifashion-images

# OpenAI Configuration
OPENAI_API_KEY=<your-openai-api-key>
OPENAI_MAX_TOKENS=8000

3. Docker Deployment:

_# Build and start services_
docker compose up -d

_# Check logs_
docker compose logs -f

4. Local Development Setup:

_# Create virtual environment_
python -m venv venv
source venv/bin/activate  _# On Windows: venv\Scripts\activate_

_# Install dependencies_
pip install -r requirements.txt

_# Run migrations_
alembic upgrade head

_# Start the application_
uvicorn app.main:app --reload

Project Structure

├── app/
│   ├── auth/ # Authentication & authorization
│   ├── database/ # Database connections & models
│   ├── models/ # SQLAlchemy models
│   ├── routers/ # API routes
│   ├── schemas/ # Pydantic models
│   ├── services/ # Business logic
│   └── main.py # Application entry point
├── migrations/         # Alembic migrations
├── tests/             # Test files
├── docs/              # Documentation
├── .github/           # GitHub Actions workflows
├── docker-compose.yml # Docker composition
├── requirements.txt   # Python dependencies
└── README.md

API Documentation

Access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Main Endpoints

Authentication

• POST /api/register - Register new user
• POST /api/login - User login
• POST /api/refresh - Refresh access token

User Management

• GET /api/users/me - Get current user profile
• PUT /api/users/me/profile - Update user profile
• POST /api/users/me/profile-picture - Upload profile picture

Wardrobe

• GET /api/wardrobe/items - List wardrobe items
• POST /api/wardrobe/items - Add new item
• GET /api/wardrobe/items/{item_id} - Get item details
• PUT /api/wardrobe/items/{item_id} - Update item
• DELETE /api/wardrobe/items/{item_id} - Delete item
• POST /api/wardrobe/items/{item_id}/image - Upload item image

Chat

• POST /api/chat/sessions - Create chat session
• GET /api/chat/sessions - List chat sessions
• POST /api/chat/{session_id}/message - Send message
• GET /api/chat/{session_id} - Get chat history

Development

Running Tests

pytest

Database Migrations

_# Create a new migration_
alembic revision --autogenerate -m "description"

_# Apply migrations_
alembic upgrade head

Deployment

The application uses GitHub Actions for CI/CD. On push to main:

1. Tests are run
2. Docker images are built
3. Application is deployed to production

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request