Gem Classifier App

Gem Classifier App is a full-stack web application that empowers users to identify and classify gemstones using artificial intelligence. Built with the MERN stack (MongoDB, Express, React, Node.js) and a Python-based machine learning service, GEMX combines modern web technologies with a robust Random Forest model for accurate gemstone predictions.

---

🚀 Features

• Interactive Web Interface: Intuitive React + Tailwind CSS frontend for uploading gem images, viewing predictions, and managing your profile.
• AI-Powered Classification: Python ML service leverages a trained Random Forest model to classify gems from images.
• RESTful API: Node.js/Express backend handles authentication, user management, image uploads, and communication with the ML service.
• User Accounts: Register, login, edit profile, reset password, and manage favorite gem predictions.
• Image Uploads: Secure image upload and storage via Cloudinary integration.
• Favorites & History: Save favorite predictions and view your upload history.
• Responsive Design: Optimized for desktop and mobile devices.
• Developer & Mentor Credits: Meet the team and mentor behind GEMX.

---

🗂️ Project Structure

gem_classifier_app/
├── README.md
├── client/           # React frontend
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── store/
│   │   ├── App.jsx
│   │   ├── main.jsx
│   │   └── ...
│   ├── public/
│   ├── package.json
│   └── ...
├── server/           # Node.js/Express backend
│   ├── src/
│   │   ├── controllers/
│   │   ├── models/
│   │   ├── routes/
│   │   ├── utils/
│   │   ├── app.js
│   │   └── ...
│   ├── package.json
│   └── ...
├── ml_service/       # Python ML microservice
│   ├── app.py
│   ├── requirements.txt
│   └── ml_model/
│       ├── label_encoder.pkl
│       └── random_forest_model.pkl
└── ...

---

🛠️ Getting Started

Prerequisites

• Node.js & npm
• Python 3.13+
• MongoDB (local or cloud)
• Cloudinary account (for image uploads)
• Docker (optional, for containerized development)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/gem_classifier_app.git
   cd gem_classifier_app

2. Setup the ML Service:

   cd ml_service
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   python app.py

   The ML service runs on a separate port (default: 5000).

3. Setup the Backend Server:

   cd server
   npm install
   npm start

   Configure environment variables in .env (MongoDB URI, Cloudinary keys, etc.).

4. Setup the Frontend Client:

   cd client
   npm install
   npm run dev

   The React app runs on port 5173 by default.

---

⚡ Usage

• Visit http://localhost:5173 in your browser.
• Register or login to your account.
• Upload a gemstone image to get AI-powered predictions.
• Save your favorite results and view your prediction history.
• Explore the About page to learn more about the project and team.

---

📦 Tech Stack

• Frontend: React, Tailwind CSS, Framer Motion, React Router
• Backend: Node.js, Express, MongoDB, Mongoose, Cloudinary, Multer, JWT, Nodemailer
• ML Service: Python, scikit-learn, Flask, Pickle
• Dev Tools: Docker, ESLint, Vite

---

🔗 Backend API Routes

The backend exposes several RESTful endpoints for client and ML service interaction. Here are the main routes:

Auth & User Management

• POST /api/auth/register — Register a new user
• POST /api/auth/login — Login and receive JWT token
• POST /api/auth/reset-password — Request password reset
• PUT /api/user/profile — Update user profile
• GET /api/user/profile — Get current user profile

Gem Prediction & Image Upload

• POST /api/gems/predict — Upload a gemstone image and get AI prediction
• GET /api/gems/history — Get user's prediction history
• POST /api/gems/favorite — Save a prediction to favorites
• GET /api/gems/favorites — Get user's favorite predictions

Miscellaneous

• GET /api/about — Project and team info

All protected routes require a valid JWT token in the Authorization header.

---

👨‍💻 Developers

• Himanshu Parihar
  GitHub | LinkedIn

• Agrim Sharma
  GitHub | LinkedIn

🎓 Mentor

• Prof. Vinod Sharma
  Jammu University
  Google Scholar Profile

---

📄 License

This project is licensed under the MIT License.

---

🤝 Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements.

---

📬 Contact

For support or inquiries, email: [email protected]

---

🌐 Links

• Live Demo (if deployed)
• Documentation

---

Made with ❤️ ☮︎