A professional web application for AI-powered skin disease classification using deep learning. The system provides real-time analysis of skin conditions with treatment recommendations and severity assessments.

DermoAI uses a pre-trained deep learning model to classify 24 different skin conditions from uploaded images. The application features a modern React frontend, Express.js backend, and a Python-based ML service using PyTorch.

• AI-Powered Analysis: Classifies 24 different skin conditions using deep learning
• Real-time Predictions: Get instant analysis with confidence scores
• Treatment Recommendations: Provides condition-specific treatment suggestions
• Severity Assessment: Automatic severity classification (Low/Medium/High)
• Dermatologist Finder: Integrated Google Maps to find nearby specialists
• AI Chatbot: LLM-powered assistant for dermatology questions
• Professional UI: Clean, modern interface with responsive design

• React 19 with TypeScript
• Modern CSS with responsive design
• Google Maps API integration
• Real-time image preview and analysis

• Node.js with Express.js
• Multi-LLM support (OpenAI, Google Gemini, Anthropic Claude)
• RESTful API architecture
• CORS-enabled with security middleware

• PyTorch (ResNet152/DenseNet121)
• Flask API for model serving
• 24-class skin disease classification
• Confidence scoring system

skindiseasemlapp/
├── frontend/               # React frontend application
│   ├── public/            # Static assets
│   ├── src/               # Source code
│   │   ├── App.tsx        # Main application component
│   │   ├── Chatbot.tsx    # AI chatbot component
│   │   └── GoogleAPIDermatologistFinder.tsx
│   ├── package.json       # Frontend dependencies
│   └── tsconfig.json      # TypeScript configuration
│
├── backend/               # Node.js backend server
│   ├── model_service.py   # Python ML service
│   ├── server.js          # Express server
│   ├── routes/            # API routes
│   ├── classes.txt        # Disease classifications
│   ├── skin_disease_model.pth  # Model weights
│   ├── requirements.txt   # Python dependencies
│   └── package.json       # Node dependencies
│
└── README.md             # This file

• Node.js 16+ and npm
• Python 3.8+
• Git

git clone https://github.com/YisakTolla/dermoai.git
cd dermoai

cd backend

# Install Python dependencies
pip install -r requirements.txt

# Install Node dependencies
npm install

# Configure environment variables
# Copy .env.example to .env and add your API keys
cp .env.example .env

cd ../frontend

# Install dependencies
npm install

# Configure environment variables
# Update .env with your configuration

# API Keys
ANTHROPIC_API_KEY=your-key-here
GOOGLE_MAPS_API_KEY=your-key-here

# Server
PORT=3001
FRONTEND_URL=http://localhost:3000
DEFAULT_LLM_PROVIDER=anthropic

REACT_APP_API_URL=http://localhost:3001/api
REACT_APP_GOOGLE_MAPS_API_KEY=your-key-here

1. ML Model Service (Terminal 1):

cd backend
python model_service.py

2. Backend Server (Terminal 2):

cd backend
npm run dev

3. Frontend (Terminal 3):

cd frontend
npm start

The application will be available at http://localhost:3000

Analyzes uploaded skin images

Request:

{
  "image": "base64_encoded_image"
}

Response:

{
  "success": true,
  "analysis": {
    "condition": "Detected Condition",
    "confidence": 85.5,
    "severity": "medium",
    "recommendations": ["..."],
    "disclaimer": "..."
  }
}

AI-powered dermatology assistant

Health check endpoint

The model can classify 24 different skin conditions including:

• Acne and Rosacea
• Melanoma and Skin Cancer
• Basal Cell Carcinoma
• Eczema
• Psoriasis
• Fungal Infections
• And 18 more conditions

• Frontend: TypeScript with React best practices
• Backend: ES6+ JavaScript with Express patterns
• Python: PEP 8 compliant

# Frontend tests
cd frontend && npm test

# Backend tests
cd backend && npm test

The application is designed for easy deployment on free platforms:

1. Frontend: Deploy to Vercel

   cd frontend && vercel --prod

2. Backend: Deploy to Render

   • Connect GitHub repo
   • Set root directory: backend
   • Add environment variables

3. ML Model: Already deployed at Hugging Face Spaces

See DEPLOYMENT_SIMPLE.md for detailed instructions.

• All API keys are stored in environment variables
• CORS configured for production domains
• Rate limiting implemented on API endpoints
• Input validation and sanitization
• HTTPS required for production

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

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

This application is for educational and demonstration purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical concerns.

• Model architecture based on ResNet152/DenseNet121
• Dataset: DermNet skin disease dataset
• UI components: Lucide React icons
• LLM providers: OpenAI, Google, Anthropic