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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

Screenshot 2026-04-22 180141

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" }