youtube-summarizer/README.md

# YouTube Summarizer Web Application

An AI-powered web application that automatically extracts, transcribes, and summarizes YouTube videos, providing intelligent insights and key takeaways.

## 🎯 Features

- **Video Transcript Extraction**: Automatically fetch transcripts from YouTube videos
- **AI-Powered Summarization**: Generate concise summaries using multiple AI models
- **Multi-Model Support**: Choose between OpenAI GPT, Anthropic Claude, or DeepSeek
- **Key Points Extraction**: Identify and highlight main topics and insights
- **Chapter Generation**: Automatically create timestamped chapters
- **Export Options**: Save summaries as Markdown, PDF, or plain text
- **Caching System**: Reduce API calls with intelligent caching
- **Rate Limiting**: Built-in protection against API overuse

## 🏗️ Architecture

```
[Web Interface] → [FastAPI Backend] → [YouTube API/Transcript API]
                         ↓
[AI Service] ← [Summary Generation] ← [Transcript Processing]
                         ↓
[Database Cache] → [Summary Storage] → [Export Service]
```

## 🚀 Quick Start

### Prerequisites

- Python 3.11+
- YouTube API Key (optional but recommended)
- At least one AI service API key (OpenAI, Anthropic, or DeepSeek)

### Installation

1. **Clone the repository**
```bash
git clone https://eniasgit.zeabur.app/demo/youtube-summarizer.git
cd youtube-summarizer
```

2. **Set up virtual environment**
```bash
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

3. **Install dependencies**
```bash
pip install -r requirements.txt
```

4. **Configure environment**
```bash
cp .env.example .env
# Edit .env with your API keys and configuration
```

5. **Initialize database**
```bash
alembic init alembic
alembic revision --autogenerate -m "Initial migration"
alembic upgrade head
```

6. **Run the application**
```bash
python src/main.py
```

The application will be available at `http://localhost:8082`

## 📁 Project Structure

```
youtube-summarizer/
├── src/
│   ├── api/              # API endpoints
│   │   ├── routes.py     # Main API routes
│   │   └── websocket.py  # Real-time updates
│   ├── services/         # Business logic
│   │   ├── youtube.py    # YouTube integration
│   │   ├── summarizer.py # AI summarization
│   │   └── cache.py      # Caching service
│   ├── utils/            # Utility functions
│   │   ├── validators.py # Input validation
│   │   └── formatters.py # Output formatting
│   └── main.py           # Application entry point
├── tests/                # Test suite
├── docs/                 # Documentation
├── alembic/             # Database migrations
├── static/              # Frontend assets
├── templates/           # HTML templates
├── requirements.txt     # Python dependencies
├── .env.example        # Environment template
└── README.md           # This file
```

## 🔧 Configuration

### Essential Environment Variables

| Variable | Description | Required |
|----------|-------------|----------|
| `YOUTUBE_API_KEY` | YouTube Data API v3 key | Optional* |
| `OPENAI_API_KEY` | OpenAI API key | One of these |
| `ANTHROPIC_API_KEY` | Anthropic Claude API key | is required |
| `DEEPSEEK_API_KEY` | DeepSeek API key | for AI |
| `DATABASE_URL` | Database connection string | Yes |
| `SECRET_KEY` | Session secret key | Yes |

*YouTube API key improves metadata fetching but transcript extraction works without it.

## 🧪 Testing

Run the test suite:
```bash
pytest tests/ -v
pytest tests/ --cov=src --cov-report=html  # With coverage
```

## 📝 API Documentation

Once running, visit:
- Interactive API docs: `http://localhost:8082/docs`
- Alternative docs: `http://localhost:8082/redoc`

### Key Endpoints

- `POST /api/summarize` - Submit a YouTube URL for summarization
- `GET /api/summary/{id}` - Retrieve a summary
- `GET /api/summaries` - List all summaries
- `POST /api/export/{id}` - Export summary in different formats

## 🚢 Deployment

### Docker

```bash
docker build -t youtube-summarizer .
docker run -p 8082:8082 --env-file .env youtube-summarizer
```

### Production Considerations

1. Use PostgreSQL instead of SQLite for production
2. Configure proper CORS settings
3. Set up SSL/TLS certificates
4. Implement user authentication
5. Configure rate limiting per user
6. Set up monitoring and logging

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

## 📄 License

This project is part of the Personal AI Assistant ecosystem.

## 🔗 Related Projects

- [Personal AI Assistant](https://eniasgit.zeabur.app/demo/my-ai-projects)
- [YouTube Automation Service](https://eniasgit.zeabur.app/demo/youtube-automation)
- [PDF Translator](https://eniasgit.zeabur.app/demo/pdf-translator)

## 📞 Support

For issues and questions, please create an issue in the repository.