# Cross-Platform Coding Problem Search Engine

A search engine built with Flask and FastAPI that helps developers find coding problems across multiple platforms (LeetCode, CodeForces, and CodeChef) using TF-IDF based search.

## Features

- Search across multiple competitive programming platforms
- TF-IDF based relevance ranking
- Challenge name and direct link in results
- Clean and simple REST API endpoints
- Cross-platform compatibility
- Score-based result ranking

## Technology Stack

- Backend: Flask/FastAPI
- Frontend: React + TypeScript + Vite
- Search Algorithm: TF-IDF (Term Frequency-Inverse Document Frequency)

## Project Structure

project/ ├── server/ │ ├── app.py # Main server file │ └── data/ # Data files │ ├── leetcode/ │ ├── codeforces/ │ └── codechef/ ├── client/ │ ├── src/ │ ├── package.json │ └── vite.config.ts └── requirements.txt

## Installation

1. Clone the repository:
```bash
git clone [repository-url]

2. Install Python dependencies:

pip install -r requirements.txt

3. Install frontend dependencies:

cd client
npm install

Data Files Required

Each platform (leetcode, codeforces, codechef) needs the following files in its respective directory under data/:

• vocab.txt: Vocabulary terms
• documents.txt: Problem documents
• inverted-index.txt: Search index
• question-links.txt: Problem URLs
• idf-values.txt: IDF values for terms

API Endpoints

• GET /leetcode?query=<search_term>: Search LeetCode problems
• GET /codeforces?query=<search_term>: Search CodeForces problems
• GET /codechef?query=<search_term>: Search CodeChef problems

Running the Application

1. Start the backend server:

# For Flask
python app.py

# For FastAPI
uvicorn app:app --reload

2. Start the frontend development server:

cd client
npm run dev

Search Algorithm

The search engine uses TF-IDF (Term Frequency-Inverse Document Frequency) to rank results:

1. Documents are preprocessed and indexed
2. Query terms are processed similarly
3. Results are ranked based on TF-IDF scores
4. Results include challenge names and direct links

Development

• The backend is built with Flask/FastAPI
• Frontend uses React with TypeScript
• Vite is used for frontend tooling
• CORS is enabled for development

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.