GitInsight: Intelligent Developer Hiring & Analytics System

GitInsight is a modern, full-stack analytics platform designed to help recruiters, engineering managers, and developers gain deep insights into GitHub activity and developer potential. It combines FastAPI, the GitHub API, advanced machine learning, and a beautiful React dashboard to deliver actionable analytics for hiring and team building.

Project Overview

GitInsight analyzes GitHub profiles, repositories, and commit histories to:

• Predict developer hire scores using a custom ML regression model
• Detect developer trajectory and activity patterns with Hidden Markov Models (HMM)
• Classify commits (feature, bugfix, refactor, low-value) using NLP
• Visualize languages, repositories, and activity heatmaps in an interactive dashboard

The platform is built for extensibility and can be adapted for both individual and organizational analytics.

Features

• Deep GitHub profile, repository, language, and commit analysis
• Hire score prediction with a learned regression model
• HMM-based state detection for developer trajectory
• NLP commit classification for feature, bugfix, refactor, and low-value commits
• Interactive, dark-themed analytics dashboard with charts and visualizations
• Search and analyze any public GitHub user
• Modern UI/UX with React, Tailwind CSS, and Vite

Tech Stack

• Backend: FastAPI (Python), Uvicorn, Pydantic
• Frontend: React, TypeScript, Vite, Tailwind CSS
• ML/NLP: Custom regression models, HMM, commit classification
• APIs: GitHub REST API

Architecture

┌────────────┐      REST API     ┌──────────────┐      ┌──────────────┐
│  Frontend  │ <───────────────> │   FastAPI    │ <───>│  GitHub API  │
│  (React)   │                   │   Backend    │      │  + ML/NLP    │
└────────────┘                   └──────────────┘      └──────────────┘

Outputs

Getting Started (Local Development)

1. Clone the repository
2. Create and activate a Python virtual environment
3. Install backend dependencies:

   pip install -r requirements.txt

4. Copy .env.example to .env and add your GITHUB_TOKEN (optional, for higher API rate limits)
5. Build the frontend:

   npm install
   npm run build

6. Start the backend server (ensure the port matches Vite proxy, default is 8010):

   uvicorn app.main:app --reload --port 8010

7. Start the frontend (for development):

   npm run dev

8. Open http://localhost:5173 in your browser

API Endpoints

• POST /analyze-user with { "username": "octocat" } — Analyze a GitHub user
• GET /search-users?q=... — Search for GitHub users
• GET /health — Health check

Deployment

This project is currently in local development and not yet deployed. If you’d like a demo or want to contribute, please connect or check out the repository!

License

MIT License

API

• POST /analyze-user with { "username": "octocat" }