GitHub Analyzer

GitHub Analyzer - bu GitHub repositoriyalarini tahlil qilish va AI yordamida dasturchilar faoliyatini baholash uchun mo'ljallangan to'liq ishlaydigan loyiha.

🚀 Features

• AI-Powered Analysis: Leverage multiple AI providers (OpenAI, Anthropic, Google) for intelligent code analysis
• Repository Insights: Deep analysis of commit patterns, code quality, and development trends
• Multi-Format Reports: Generate reports in JSON, PDF, and Excel formats
• Real-time Processing: Asynchronous task processing with Celery
• Scalable Architecture: Microservices-based design with Docker containerization
• Comprehensive Monitoring: Built-in monitoring with Prometheus and Grafana
• Security First: Production-ready security configurations and best practices

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │   Backend       │    │   AI Services   │
│   (Next.js)     │◄──►│   (Django)      │◄──►│   (OpenAI, etc) │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│     Nginx       │    │   PostgreSQL    │    │     Redis       │
│  (Load Balancer)│    │   (Database)    │    │    (Cache)      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│    Traefik      │    │     Celery      │    │   Monitoring    │
│ (Reverse Proxy) │    │  (Task Queue)   │    │ (Prometheus)    │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📋 Prerequisites

• Docker and Docker Compose
• Node.js 18+ (for local development)
• Python 3.11+ (for local development)
• Git

🛠️ Quick Start

1. Clone the Repository

git clone https://github.com/your-username/github-analyzer.git
cd github-analyzer

2. Environment Setup

# Copy environment template
cp backend/.env.example backend/.env

# Edit the .env file with your configuration
nano backend/.env

3. Development Setup

# Make setup script executable
chmod +x scripts/setup.sh

# Run full setup (installs dependencies and sets up environment)
./scripts/setup.sh --full

4. Start Development Environment

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

5. Access the Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000/api/
• Admin Panel: http://localhost:8000/admin/ (admin/admin123)
• API Documentation: http://localhost:8000/api/docs/

🔧 Configuration

Environment Variables

Key environment variables to configure:

# Django Settings
SECRET_KEY=your-secret-key-here
DEBUG=True
ALLOWED_HOSTS=localhost,127.0.0.1

# Database
DATABASE_URL=postgresql://user:password@localhost:5432/github_analyzer

# Redis
REDIS_URL=redis://localhost:6379/0

# GitHub API
GITHUB_TOKEN=your-github-token
GITHUB_CLIENT_ID=your-client-id
GITHUB_CLIENT_SECRET=your-client-secret

# AI Providers
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key
GOOGLE_AI_API_KEY=your-google-key

# Email (for notifications)
EMAIL_HOST=smtp.gmail.com
EMAIL_PORT=587
EMAIL_HOST_USER=your-email@gmail.com
EMAIL_HOST_PASSWORD=your-app-password

AI Provider Configuration

The system supports multiple AI providers. Configure at least one:

1. OpenAI: Set OPENAI_API_KEY
2. Anthropic: Set ANTHROPIC_API_KEY
3. Google AI: Set GOOGLE_AI_API_KEY

📊 Usage

1. Repository Analysis

# Via API
POST /api/analysis/tasks/
{
    "repository_url": "https://github.com/owner/repo",
    "analysis_type": "comprehensive",
    "ai_provider": "openai"
}

2. Export Reports

# Generate Excel report
POST /api/analysis/exports/
{
    "analysis_id": "uuid-here",
    "format": "excel",
    "include_details": true
}

3. Monitor Progress

# Check task status
GET /api/analysis/tasks/{task_id}/

🚀 Production Deployment

1. Production Setup

# Copy production environment
cp backend/.env.example backend/.env.prod

# Edit production configuration
nano backend/.env.prod

# Set production environment
export ENVIRONMENT=production

2. SSL Configuration

# Generate SSL certificates (Let's Encrypt recommended)
certbot certonly --standalone -d yourdomain.com

3. Deploy

# Make deploy script executable
chmod +x scripts/deploy.sh

# Deploy to production
./scripts/deploy.sh deploy

4. Monitoring

Access monitoring dashboards:

• Grafana: http://yourdomain.com:3001 (admin/admin)
• Prometheus: http://yourdomain.com:9090

🔍 API Documentation

Authentication

The API uses JWT authentication:

# Login
POST /api/auth/login/
{
    "username": "your-username",
    "password": "your-password"
}

# Use token in subsequent requests
Authorization: Bearer <your-jwt-token>

Key Endpoints

• GET /api/repositories/ - List repositories
• POST /api/analysis/tasks/ - Create analysis task
• GET /api/analysis/results/ - List analysis results
• POST /api/analysis/exports/ - Export analysis
• GET /api/statistics/ - Get system statistics

🧪 Testing

Run Tests

# Backend tests
docker-compose exec backend python manage.py test

# Frontend tests
docker-compose exec frontend npm test

# Integration tests
docker-compose exec backend python manage.py test apps.analysis.tests.IntegrationTestCase

Load Testing

# Install locust
pip install locust

# Run load tests
locust -f tests/load_test.py --host=http://localhost:8000

📈 Monitoring and Logging

Logs

# View application logs
docker-compose logs -f backend

# View all logs
docker-compose logs -f

# View specific service logs
docker-compose logs -f postgres

Metrics

Key metrics monitored:

• Request rate and response time
• Error rates (4xx, 5xx)
• Database performance
• Celery task queue status
• GitHub API rate limits
• System resources (CPU, memory, disk)

Alerts

Configured alerts for:

• Service downtime
• High error rates
• Resource exhaustion
• Database issues
• Task queue backlog

🔒 Security

Security Features

• JWT authentication with refresh tokens
• Rate limiting on API endpoints
• CORS configuration
• Security headers (CSP, HSTS, etc.)
• Input validation and sanitization
• SQL injection protection
• XSS protection

Security Best Practices

1. Environment Variables: Never commit secrets to version control
2. HTTPS: Always use HTTPS in production
3. Database: Use strong passwords and limit access
4. API Keys: Rotate API keys regularly
5. Updates: Keep dependencies updated

🤝 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
• Use ESLint for JavaScript/TypeScript
• Write tests for new features
• Update documentation
• Follow conventional commit messages

📝 License

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

🆘 Support

Common Issues

1. Port conflicts: Change ports in docker-compose.yml
2. Permission errors: Check file permissions and Docker group membership
3. Memory issues: Increase Docker memory allocation
4. API rate limits: Configure GitHub token with higher limits

Getting Help

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

🙏 Acknowledgments

• Django and Django REST Framework
• Next.js and React
• OpenAI, Anthropic, and Google AI
• Docker and Docker Compose
• Prometheus and Grafana
• All open-source contributors