README.md

metadata

title: CommitLens
emoji: 🔍
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.18.0
python_version: '3.12'
app_file: app.py
pinned: true
license: mit
short_description: urn any Git commit into a human-readable engineering report.
tags:
  - track:backyard
  - sponsor:nvidia
  - achievement:offgrid
  - achievement:offbrand
  - achievement:sharing
  - achievement:fieldnotes

CommitLens — AI-Powered Code Review Pipeline

https://huggingface.co/pkheria

CommitLens is a high-performance information extraction and analysis pipeline that transforms raw GitHub diffs into structured, human-readable engineering reports. It uses a hybrid LLM approach: JetBrains Mellum 2 for deep per-file analysis and Groq-hosted Llama 3.3 for lightning-fast synthesis.

🔗 Resources & Links

• Demo Video: Watch the Product Demo
• Social Post : Linkedin Post

🚀 Key Features

• Automated Diff Extraction: Fetches the two latest commits from any GitHub repository and generates semantic diffs.
• Top-Impact Filtering: Automatically identifies and prioritizes the most significant changes (top 2 files by lines changed) to ensure high-signal reviews.
• Hybrid LLM Pipeline:
  • Mellum 2 (12B): Performs surgical, per-file code analysis. Optimized with 6-bit NF4 quantization for efficient GPU utilization.
  • Groq (Llama 3.3 70B): Generates a high-level executive summary and key takeaways in milliseconds.
• Cinematic UI: A bespoke, low-latency frontend featuring a custom particle engine, real-time status tracking, and a "git-graph" hero visualization.

🛠 Tech Stack

• Core: Python 3.12, FastAPI, Gradio (Server Mode).
• ML/Inference: transformers, bitsandbytes (4-bit/6-bit quantization), torch, spaces (ZeroGPU).
• APIs: GitHub REST API, Groq Cloud API.
• Frontend: Vanilla JavaScript (ES6+), HTML5 Canvas, CSS3 Grid/Flexbox.

📂 Project Structure

File	Purpose
app.py	Main application server; manages model lifecycle and GPU/API orchestration.
commitlens.py	Data pipeline; handles GitHub API interaction, file filtering, and prompt engineering.
index.html	Custom-built, high-fidelity frontend with interactive Git visualizations.
requirements.txt	Dependency manifest (requests, gradio, torch, transformers, etc.).

⚙️ How It Works

1. Extraction: The GitHubClient fetches commit metadata and raw patches.
2. Filtering: Files are filtered by extension (keeping source code, ignoring binaries/locks) and sorted by impact.
3. Mellum Analysis: The pipeline builds structured prompts containing "Before", "After", and "Diff" blocks. Mellum 2 generates concise summaries for each file.
4. Groq Synthesis: Per-file summaries are batched and sent to Groq for a final structured Markdown report including a "Commit Overview" and "Key Takeaways".

🛠 Setup & Usage

Local Development

1. Install dependencies:

   pip install -r requirements.txt
   

2. Set Environment Variables:

   export GROQ_API_KEY="your_groq_api_key"
   

3. Run the application:

   python app.py
   

CLI Mode

You can also run the extraction pipeline directly:

python commitlens.py <github_repo_url> --token <optional_pat> --print-prompts

📄 License

MIT