🤖 MeetlyAI

AI-Powered Meeting Intelligence Platform

Transform your meeting transcripts into actionable insights with the power of Google's Gemini AI. MeetlyAI automatically extracts summaries, key decisions, and action items from your meeting notes.

✨ Features

• 📝 Smart Summarization: Get concise, accurate meeting summaries
• 🎯 Decision Tracking: Automatically extract key decisions made
• ✅ Action Items: Identify tasks with ownership and priority
• 🚀 Fast & Reliable: Built on FastAPI for high performance
• 🔌 RESTful API: Easy integration with any frontend or tool
• 🐳 Docker Ready: Deploy anywhere with containerization

🏗️ Architecture

MeetlyAI/
├── backend/              # FastAPI backend service
│   ├── app/
│   │   ├── api/         # HTTP endpoints
│   │   ├── core/        # Configuration & logging
│   │   ├── services/    # Business logic (Gemini AI)
│   │   ├── schemas/     # Pydantic models
│   │   └── utils/       # Helper functions
│   ├── main.py          # Application entry point
│   └── Dockerfile       # Backend container
├── frontend/            # React + Vite frontend
│   ├── src/
│   │   ├── App.jsx      # Main component
│   │   └── index.css    # Glass morphism design system
│   ├── Dockerfile       # Frontend container
│   └── nginx.conf       # Production server config
├── .github/
│   └── workflows/       # CI/CD pipelines
├── docker-compose.yml   # Multi-service orchestration
└── README.md           # This file

🚀 Quick Start

Prerequisites

• Docker & Docker Compose (recommended)
• OR Python 3.11+ and Node.js 18+ for local development
• Google Gemini API Key (Get one free)

Option 1: Docker Compose (Recommended)

1. Clone the repository

   git clone <your-repo-url>
   cd MeetlyAI

2. Configure environment

   cp .env.example .env
   # Edit .env and add your GEMINI_API_KEY

3. Start all services

   docker-compose up --build

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs
   • Health Check: http://localhost:8000/health

Option 2: Local Development

Backend:

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY
uvicorn main:app --reload

Frontend:

cd frontend
npm install
npm run dev

Access frontend at http://localhost:5173

📖 Usage

Example API Request

curl -X POST http://localhost:8000/api/analyze/transcript \
  -H "Content-Type: application/json" \
  -d '{
    "transcript": "Team sync on Jan 5th. Discussed Q1 goals. Alice will prepare the budget proposal by next week - high priority. Bob agreed to review the design mockups. Decision: We will use the new framework for the project."
  }'

Example Response

{
  "summary": "Team discussed Q1 goals and made key decisions about project framework and task assignments.",
  "decisions": [
    "Use the new framework for the project"
  ],
  "action_items": [
    {
      "task": "Prepare the budget proposal",
      "owner": "Alice",
      "priority": "High"
    },
    {
      "task": "Review the design mockups",
      "owner": "Bob",
      "priority": null
    }
  ]
}

🛠️ Technology Stack

Backend

• Framework: FastAPI 0.115+
• AI Engine: Google Gemini 2.0 Flash
• Validation: Pydantic v2
• Server: Uvicorn with async support

Frontend

• Framework: React 18
• Build Tool: Vite 5
• Styling: Custom CSS with Glass Morphism
• Fonts: Google Fonts (Inter)

DevOps

• Containerization: Docker
• Orchestration: Docker Compose
• Web Server: Nginx (production)
• CI/CD: GitHub Actions
• Linting: Flake8 (Python), ESLint (JavaScript)

📋 API Documentation

Once running, visit:

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

Available Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/api/analyze/transcript	Analyze meeting transcript

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

# Required
GEMINI_API_KEY=your_api_key_here

# Optional
APP_NAME=MeetlyAI
ENV=development
GEMINI_MODEL=gemini-2.0-flash-exp

See .env.example for all available options.

🧪 Testing & Quality

Continuous Integration

GitHub Actions automatically:

• ✅ Lints Python code with Flake8
• ✅ Validates code structure
• ✅ Runs on every push and PR

Run Linting Locally

cd backend
pip install flake8
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics

📦 Deployment

Docker Compose (Production)

# Build and start in detached mode
docker-compose up -d --build

# View logs
docker-compose logs -f backend

# Stop services
docker-compose down

Environment-Specific Configs

For production, update:

1. CORS origins in backend/main.py
2. Set ENV=production in .env
3. Use proper secrets management
4. Enable HTTPS/SSL

🗺️ Roadmap

• Frontend web application
• Real-time transcript processing
• Multi-language support
• Integration with calendar apps
• Export to PDF/Word
• Team collaboration features

🤝 Contributing

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

Development Guidelines

• Follow PEP 8 for Python code
• Add tests for new features
• Update documentation
• Ensure CI passes

🐛 Troubleshooting

Docker Issues

# Reset everything
docker-compose down -v
docker-compose up --build

API Key Issues

• Verify your Gemini API key is valid
• Check .env file exists and is loaded
• Ensure no extra spaces in environment variables

Port Conflicts

# Change port in docker-compose.yml
ports:
  - "8001:8000"  # Use 8001 instead

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Google Gemini AI for powerful language models
• FastAPI for the excellent web framework
• The open-source community

📞 Support

For issues and questions:

• Open an issue on GitHub
• Check existing documentation
• Review API docs at /docs

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Built with ❤️ using FastAPI and Gemini AI