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Amega AI is a cutting-edge artificial intelligence platform that empowers organizations to build, deploy, and manage AI solutions at scale

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AMEGA-AI

A powerful AI-driven platform for intelligent automation and decision making.

πŸš€ Features

  • πŸ€– Advanced AI Models Integration
  • πŸ”’ Robust Security & Authentication
  • πŸ“Š Real-time Monitoring & Analytics
  • πŸ§ͺ Comprehensive Testing Suite
  • πŸ” Ethical AI Compliance
  • πŸ“ˆ Performance Optimization

πŸ“‹ Prerequisites

  • Python 3.8 or higher
  • Git
  • Docker (optional)
  • PostgreSQL (optional, can use Docker)
  • Redis (optional, can use Docker)

πŸ› οΈ Setting up the Development Environment

  1. Clone the repository:

    git clone https://github.com/Cameroon-Developer-Network/amega-ai.git
    
    cd amega-ai
  2. Set up the environment:

    # Using the setup script (recommended)
    ./scripts/setup_venv.sh
    
    # Or manually:
    python3 -m venv venv
    source venv/bin/activate  # On Windows: .\venv\Scripts\activate
    pip install --upgrade pip
    pip install -r requirements.txt
  3. Configure environment variables:

    cp .env.example .env
    # Edit .env with your configuration
  4. Initialize the database:

    python scripts/init_db.py
  5. Run the development server:

    uvicorn src.amega_ai.main:app --reload

πŸ“ Project Structure

amega-ai/
β”œβ”€β”€ backend/          # Backend server implementation
β”‚   β”œβ”€β”€ api/         # API endpoints
β”‚   β”œβ”€β”€ core/        # Core business logic
β”‚   └── models/      # Database models
β”œβ”€β”€ frontend/        # Frontend application
β”œβ”€β”€ cli/            # Command-line interface tools
β”œβ”€β”€ docs/           # Documentation
β”‚   β”œβ”€β”€ api/        # API documentation
β”‚   └── guides/     # User and developer guides
β”œβ”€β”€ scripts/        # Utility scripts
β”œβ”€β”€ security/       # Security-related components
β”œβ”€β”€ src/           # Source code
β”‚   └── amega_ai/  # Main package
β”œβ”€β”€ tests/         # Test suites
β”‚   β”œβ”€β”€ unit/     # Unit tests
β”‚   └── e2e/      # End-to-end tests
β”œβ”€β”€ .env.example   # Environment variables template
β”œβ”€β”€ .gitignore    # Git ignore rules
β”œβ”€β”€ CHANGELOG.md   # Version history
β”œβ”€β”€ CONTRIBUTING.md # Contribution guidelines
β”œβ”€β”€ LICENSE       # License information
β”œβ”€β”€ README.md     # Project documentation
└── requirements.txt # Python dependencies

πŸ§ͺ Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src/

# Run specific test category
pytest tests/unit/
pytest tests/e2e/

πŸ“š Documentation

πŸ”„ Development Workflow

  1. Create a new branch from develop
  2. Make your changes
  3. Run tests and linting
  4. Submit a pull request
  5. Wait for review and CI checks

πŸš€ Deployment

# Production deployment
./scripts/deploy.sh production

# Staging deployment
./scripts/deploy.sh staging

πŸ” Monitoring

  • Access Grafana dashboard: http://localhost:3000
  • View API documentation: http://localhost:8000/docs
  • Check monitoring metrics: http://localhost:8000/metrics

🀝 Contributing

Please read our Contributing Guidelines before submitting a Pull Request.

πŸ“„ License

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

πŸ™ Acknowledgments

  • All contributors who have helped with code, documentation, and testing
  • The open-source community for the amazing tools and libraries

πŸ“ž Support

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