LEETST★R

The AI-powered companion that decodes LeetCode complexity in real-time.

Install Extension • Report Issue • Suggest Feature

• One-Click Analysis: Click the ★ star button on any LeetCode problem page for an instant AI-powered breakdown
• Plain-English Summaries: Understand what the problem is actually asking without decoding the description
• Data Structure Recommendations: Get the best algorithm/DS to use with a clear reason why
• Practice First: Suggests an easier foundational problem to solve before tackling the current one
• Summary History: View your past 20 analyzed problems, stored in MongoDB and synced via unique browser ID
• Smart Caching: Client-side (7-day) and server-side caching to minimize API calls and speed up responses
• Dark/Light Mode: Toggle between themes to match your LeetCode setup
• Customizable View: Hide or show the Data Structure and Practice First sections
• Copy to Clipboard: One-click copy on any summary

1. Download the extension from the Chrome Web Store (coming soon)
2. Navigate to any leetcode.com/problems/... page
3. Click the ★ star button in the top-right corner
4. Get your summary instantly

git clone https://github.com/zarifislam10/leetcode-ai-summarizer.git
cd leetcode-ai-summarizer

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Create a .env file in backend/:

OPENAI_API_KEY=sk-your-key-here
MONGODB_URI=mongodb+srv://<USERNAME>:<PASSWORD>@cluster.mongodb.net/leetstar

Run the server:

python app.py

The server runs on http://localhost:5000.

1. Open Chrome and go to chrome://extensions
2. Enable Developer Mode (top right toggle)
3. Click Load unpacked and select the root project folder
4. Navigate to any LeetCode problem page
5. Click the ★ star button

Note: For local development, update BACKEND_URL in content.js to http://localhost:5000/summarize.

leetcode-ai-summarizer/
├── .github/
│   └── workflows/
│       └── main_leetstar.yml    # CI/CD pipeline
├── backend/
│   ├── app.py                   # Flask API (summarize, history, health)
│   ├── requirements.txt         # Python dependencies
│   └── startup.txt              # Azure startup command
├── content.js                   # Chrome extension logic
├── styles.css                   # UI styles (dark/light mode)
├── manifest.json                # Chrome extension manifest (MV3)
└── README.md

The backend is deployed on Azure App Service with CI/CD via GitHub Actions.

• Every push to main triggers an automatic build and deploy
• Azure's Oryx build engine installs Python dependencies on deployment
• Environment variables (OPENAI_API_KEY, MONGODB_URI) are set in Azure App Settings

• API keys live only in environment variables, never in client code
• OpenAI API key is server-side only; the extension never sees it
• Problem text is capped at 3,000 characters before sending to OpenAI
• Each browser install gets a unique anonymous ID (no personal data collected)

• GPT-powered problem analysis
• Azure deployment with CI/CD
• MongoDB history with cross-device support
• Client-side caching (7-day expiry)
• Server-side caching (shared across users)
• Dark/Light mode
• Collapsible sections (Data Structure, Practice First)
• Chrome Web Store publication
• Rate limiting with Flask-Limiter
• Redis caching layer
• Datadog monitoring
• Docker containerization
• System design diagram

Contributions are welcome!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m "add your feature")
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

• Built with OpenAI GPT-4o-mini for problem analysis
• LeetCode API for practice problem slug verification
• Deployed on Microsoft Azure with MongoDB Atlas

v1.0 (March 2025): Client-side proof-of-concept focused on real-time extraction.

v2.0 (Current): Re-architected with a Flask backend, MongoDB persistence, Azure CI/CD deployment, and a polished dark/light UI.

Made with ★ for LeetCoders everywhere