SmartShea Skin Analyzer

A full-stack application that uses AI to analyze skin type and recommend personalized shea butter products.

Features

• AI-powered skin type analysis
• Personalized product recommendations
• Interactive skin analysis interface
• Real-time camera integration
• Responsive design

Tech Stack

Frontend

• React.js
• React Router
• Axios
• CSS3

Backend

• Flask
• PyTorch
• OpenCV
• NumPy
• Pillow

Installation

Quick Install (Unix-based systems)

chmod +x install.sh
./install.sh

Manual Installation

1. Frontend Setup

cd frontend
npm install

2. Backend Setup

cd skin-analyzer/backend
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Unix or MacOS:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Running the Application

1. Start the Backend

cd skin-analyzer/backend
source venv/bin/activate  # or venv\Scripts\activate on Windows
python app.py

2. Start the Frontend (in a new terminal)

cd frontend
npm start

The application will be available at:

• Frontend: http://localhost:8080
• Backend API: http://localhost:8000

Deployment

Frontend (GitHub Pages)

The frontend is configured for GitHub Pages deployment:

1. Update the homepage in frontend/package.json if needed
2. Deploy to GitHub Pages:

cd frontend
npm run deploy

The application will be available at: https://murayeeto.github.io/SmartShea

Backend (Render.com)

The backend is configured for Render.com deployment:

1. Connect your GitHub repository to Render.com
2. Create a new Web Service
3. Configure the service:
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
   • Root Directory: skin-analyzer/backend

Environment Variables

Frontend

Create a .env file in the frontend directory:

REACT_APP_API_URL=your_backend_url

Backend

Configure in Render.com dashboard:

• GITHUB_PAGES_URL: https://murayeeto.github.io
• UPLOAD_FOLDER: /tmp/uploads
• PYTHON_VERSION: 3.8.0

Project Structure

/
├── frontend/               # React frontend
│   ├── public/            # Static files
│   └── src/               # Source code
├── skin-analyzer/         # Python backend
│   └── backend/          # Flask application
└── install.sh            # Installation script

Contributing

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

License

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