ai-codereview

An AI-powered code review platform that automatically analyzes pull requests, detects security vulnerabilities, suggests improvements, and generates comprehensive documentation. It combines multiple AI tasks to provide a complete developer productivity solution.

NB. This code is not yet fully functional.

What ai-codereview does

ai-codereview acts like a tireless code reviewer that:

• Analyzes every pull request automatically when you create or update it
• Finds security vulnerabilities before they reach production
• Suggests improvements to make your code better
• Generates documentation to help others understand your code
• Provides quality scores to track code health over time

Key Features

🔍 Multi-AI Analysis

• Uses 5+ specialized AI models for comprehensive code review
• Text classification for issue categorization
• Security vulnerability detection
• Automatic documentation generation
• Code quality assessment

🔒 Security First

• Detects SQL injection vulnerabilities
• Identifies XSS risks
• Finds authentication bypasses
• Spots data exposure risks

📊 Quality Metrics

• Overall code quality scores (0-100)
• Complexity analysis
• Improvement suggestions
• Performance recommendations

🚀 GitHub Integration

• Automatic pull request analysis
• Real-time webhook processing
• Inline code comments
• Summary reports

Quick Start

1. Clone the Repository

git clone https://github.com/crissyg/ai-codereview.git
cd ai-codereview

2. Set Up Environment

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

# Install dependencies
pip install -r requirements.txt

For GPU Support (if you have NVIDIA GPU):

Replace torch line in the requirements.txt with:

torch==2.1.0+cu118
torchvision==0.16.0+cu118
torchaudio==2.1.0+cu118

3. Configure GitHub Token

# Create .env file
echo "GITHUB_TOKEN=ghp_your_github_token_here" > .env

4. Run the Application

# Start the API server
uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

5. Test the System

# Test with sample code
curl -X POST "http://localhost:8000/api/v1/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "code_content": "def hello_world():\n    print(\"Hello, World!\")",
    "file_path": "example.py"
  }'

Architecture Overview

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   GitHub API    │────│   ai-codereview  │────│   AI Models     │
│   (Webhooks)    │    │ (Python/FastAPI) |    │   (HuggingFace) │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                       ┌──────────────────┐
                       │    Database      │
                       │   (PostgreSQL)   │
                       └──────────────────┘

Project Structure

ai-codereview/
├── backend/
│   ├── app/
│   │   ├── main.py              # Application entry point
│   │   ├── services/
│   │   │   ├── code_analyzer.py # AI analysis engine
│   │   │   └── github_integration.py # GitHub API client
│   │   ├── api/
│   │   │   └── routes.py        # API endpoints
│   │   └── utils/
│   │       └── config.py        # Configuration management
├── frontend/                    # Web dashboard (future)
├── docker-compose.yml          # Container orchestration
├── requirements.txt            # Python dependencies
└── README.md                   # This file

API Endpoints

Analyze Code

POST /api/v1/analyze
Content-Type: application/json

{
  "code_content": "your code here",
  "file_path": "path/to/file.py",
  "language": "python"
}

GitHub Webhook

POST /api/v1/webhook/github
Content-Type: application/json

{
  "action": "opened",
  "pull_request": { ... },
  "repository": { ... }
}

Health Check

GET /api/v1/health

Configuration

Create a .env file with the following variables:

# Required
GITHUB_TOKEN=ghp_your_github_personal_access_token

# Optional
DATABASE_URL=postgresql://user:pass@localhost/ai_codereview
MODEL_CACHE_DIR=./cache
MAX_CONCURRENT_ANALYSES=5
DEBUG=false

Deployment

Using Docker Compose

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f ai-codereview-api

# Stop services
docker-compose down

Manual Deployment

# Install dependencies
pip install -r requirements.txt

# Start the application
uvicorn backend.app.main:app --host 0.0.0.0 --port 8000

Performance Metrics Example

• Analysis Speed: ~2-5 seconds per file
• Throughput: 10,000+ daily analyses
• Accuracy: 85% security vulnerability detection
• Uptime: 99.9% availability target

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