Hackmentees

Welcome to Hackmentees! 🎉

This is a beginner-to-intermediate friendly repository for Hacktoberfest 2025, where contributors can add ML models and frontend enhancements.

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🚀 Project Goal

The goal of Hackmentees is to create a central hub of lightweight ML models displayed via a React + Vite frontend, where contributors can:

• Add ML models (lightweight or hosted externally if large)
• Improve the frontend UI (cards, sidebar, dark/light mode, animations)
• Integrate model APIs dynamically with the frontend

This project is ideal for beginners who want to contribute to real-world ML + frontend integration in a structured and guided way.

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📂 Repository Structure

Hackmentees/
│
├── frontend/                       # React + Vite frontend
│   ├── src/
│   │   ├── components/             # UI components (Sidebar, Card, ThemeToggle)
│   │   ├── pages/                  # Pages (Home, etc.)
│   │   └── data/
│   │       └── models.js           # Frontend metadata for models
│   ├── App.jsx
│   └── main.jsx
│
├── ml_models/                      # ML contributions
│   ├── TEMPLATE_MODEL/             # Template for new models
│   │   ├── app.py                  # API stub
│   │   ├── requirements.txt
│   │   ├── metadata.json           # Frontend integration info
│   │   └── README.md               # Explain dataset/usage
│
├── .github/ISSUE_TEMPLATE/         # Issue templates
├── README.md
└── CONTRIBUTING.md

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💻 How to Run Frontend

1. Navigate to the frontend folder:

cd frontend

2. Install dependencies:

npm install

3. Run dev server:

npm run dev

4. Open browser at http://localhost:5173

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Example Workflow for Adding a Model

1. Copy ml_models/TEMPLATE_MODEL → rename folder to house_price_prediction
2. Replace app.py with your prediction logic (small dataset / lightweight model)
3. Update metadata.json:

{
  "name": "House Price Prediction",
  "version": "v1.0",
  "description": "Predict house prices from square footage and location",
  "endpoint": "/ml_models/house_price_prediction/predict",
  "features": ["square_feet", "location"]
}

4. Test locally:

cd ml_models/house_price_prediction
pip install -r requirements.txt
uvicorn app:app --reload

5. Add a new card to frontend/src/data/models.js:

models.push({
  id: 1,
  name: "House Price Prediction",
  description: "Predict house prices from square footage and location",
  tag: "ML",
  endpoint: "/ml_models/house_price_prediction/predict"
})

6. Test frontend and Submit a Pull Request (PR)

Warning

⚠️ Keep models lightweight; for large models, host externally (Google Drive / Hugging Face) and download in app.py.

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🎨 Frontend Contribution

• Add new cards or pages in frontend/src/pages or components
• Update models.js to include your model metadata
• UI improvements: dark/light theme toggle, sidebar animations, responsive design

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📝 Contribution Flow

1. Pick an issue (frontend / ML / integration)
2. Fork repo → create branch → implement feature
3. Testlocally → submit PR
4. Maintainers review → merge

Note This ensures contributions are structured, beginner-friendly, and review-ready.

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🙌 Hacktoberfest Tips

• Look for issues labeled beginner or enhancement
• Follow folder structure strictly
• Keep PRs focused on one feature at a time
• Always test your ML models or frontend changes locally before submitting a PR

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📬 Need Help?

• Open an issue and tag question
• Discuss in PR comments for guidance

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📚 Resources

• Contribution Guide
• Issue Template