README.md

metadata

title: Code Review Assistant
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

Code Review Assistant

An automated code review system powered by Gemma-2b that provides intelligent code analysis, suggestions for improvements, and tracks review metrics.

Features

Automated Code Review

• Analyzes code quality and suggests improvements
• Identifies potential bugs and security issues
• Recommends best practices and optimizations
• Supports multiple programming languages (Python, JavaScript, Java, C++, TypeScript, Go, Rust)

LLMOps Integration

• Uses Gemma-2b for intelligent code analysis
• Tracks model performance and accuracy
• Monitors response times and token usage

Performance Monitoring

• Real-time metrics dashboard
• Review history tracking
• Response time monitoring
• Usage statistics

Modern Web Interface

• Interactive code submission
• Syntax highlighting with CodeMirror
• Real-time review results
• Metrics visualization

Environment Variables

The following environment variables need to be set in your Hugging Face Space:

• HUGGING_FACE_TOKEN: Your Hugging Face API token (required)
• MODEL_NAME: google/gemma-2b-it (default)
• DEBUG: false (default)
• LOG_LEVEL: INFO (default)
• PORT: 7860 (default)

API Endpoints

• POST /api/v1/review: Submit code for review

  {
    "code": "your code here",
    "language": "python"
  }
  

• GET /api/v1/metrics: Get system metrics
• GET /api/v1/history: Get review history
• GET /health: Check system health

Usage

1. Enter your code in the editor
2. Select the programming language
3. Click "Submit for Review"
4. View the detailed analysis including:
   • Critical issues
   • Suggested improvements
   • Best practices
   • Security considerations

Metrics

The system tracks various metrics including:

• Total reviews performed
• Average response time
• Number of suggestions per review
• Daily usage statistics

Deployment

This Space is deployed using Docker and runs on Hugging Face's infrastructure. The application automatically handles:

• Model initialization and optimization
• Memory management
• Performance monitoring
• Error handling and logging

License

This project is licensed under the MIT License.