SkinSight is a cutting-edge healthcare management platform that combines traditional hospital management with advanced AI capabilities. The platform serves patients, doctors, and hospital staff with features ranging from AI-powered skin condition analysis to intelligent medical report interpretation.

• 🤖 AI-Powered Skin Analysis: Upload skin images for instant condition detection using deep learning
• 💬 Intelligent Medical Assistant: AI chat bot for medical report analysis and health insights
• 🏥 Comprehensive Hospital Management: Complete appointment and queue management system
• 📱 Real-time Communication: Live video calls and instant messaging between patients and doctors
• 🔒 Secure & Scalable: JWT authentication with role-based access control

• Smart Hospital Discovery: Find nearby hospitals with advanced filtering
• AI Skin Analysis: Upload photos for instant skin condition detection
• Intelligent Medical Assistant: Get insights on medical reports and test results
• Appointment Booking: Easy appointment scheduling with real-time availability
• Queue Management: Join virtual queues and track your position
• Video Consultations: Secure video calls with healthcare providers
• Medical History: Complete appointment and consultation history

• Patient Management: View and manage patient appointments
• Queue Control: Real-time queue management and patient flow
• Video Consultations: Conduct secure video calls with patients
• Profile Management: Comprehensive doctor profile setup
• Hospital Association: Connect with multiple healthcare facilities

• Hospital Dashboard: Complete hospital management interface
• Doctor Management: Manage doctor profiles and schedules
• Queue Monitoring: Real-time queue status and management
• Appointment Oversight: Monitor and manage all appointments

• Skin Condition Detection: Deep learning model for skin disease classification
• Medical Report Analysis: AI-powered interpretation of medical documents
• Intelligent Chat: Context-aware medical assistance
• OCR Integration: Extract text from medical images and documents

• React 19 - Modern UI framework with hooks
• Vite - Fast build tool and development server
• Tailwind CSS 4 - Utility-first CSS framework
• React Router DOM - Client-side routing
• Socket.IO Client - Real-time communication
• Axios - HTTP client for API calls
• React Hot Toast - User notifications

• Node.js - JavaScript runtime
• Express.js - Web application framework
• MongoDB - NoSQL database
• Mongoose - MongoDB object modeling
• Socket.IO - Real-time bidirectional communication
• JWT - JSON Web Token authentication
• bcryptjs - Password hashing
• OpenAI API - AI-powered chat and analysis

• Python Flask - ML service framework
• PyTorch - Deep learning framework
• ResNet-50 - Pre-trained CNN for image classification
• OpenAI GPT-4 - Advanced language model for medical assistance

• Netlify - Frontend hosting
• Render - Backend and ML service hosting
• MongoDB Atlas - Cloud database
• Environment Variables - Secure configuration management

• Node.js (v18.0.0 or higher)
• npm or yarn package manager
• MongoDB instance (local or MongoDB Atlas)
• Python 3.8+ (for ML service)
• OpenAI API Key (for AI features)

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

cd server

# Install dependencies
npm install

# Create environment file
cp .env.example .env

Configure your .env file:

PORT=5000
MONGO_URL=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret_key
OPENAI_API_KEY=your_openai_api_key
NODE_ENV=development

# Start development server
npm run dev

cd ../client

# Install dependencies
npm install

# Create environment file
cp .env.example .env

Configure your .env file:

VITE_API_URL=http://localhost:5000

# Start development server
npm run dev

cd ../server/ml

# Install Python dependencies
pip install -r requirements.txt

# Start ML service
python app.py

• Frontend: http://localhost:5173
• Backend API: http://localhost:5000
• ML Service: http://localhost:5001

POST /api/auth/register
POST /api/auth/login
POST /api/auth/logout
GET  /api/auth/profile

GET    /api/hospitals
POST   /api/hospitals
GET    /api/hospitals/:id
PUT    /api/hospitals/:id
DELETE /api/hospitals/:id

GET    /api/appointments
POST   /api/appointments
PUT    /api/appointments/:id
DELETE /api/appointments/:id

GET    /api/queues
POST   /api/queues/join
PUT    /api/queues/:id
DELETE /api/queues/:id

POST /api/chat
POST /api/analyze-image
POST /api/ocr

GET  /ml/health
POST /ml/predict

SkinSight/
├── client/                 # React frontend
│   ├── public/            # Static assets
│   │   ├── components/    # React components
│   │   ├── context/       # React context providers
│   │   ├── hooks/         # Custom React hooks
│   │   ├── constants/     # Application constants
│   │   └── assets/        # Images and icons
│   ├── package.json
│   └── vite.config.js
├── server/                # Node.js backend
│   ├── controllers/       # Route controllers
│   ├── models/           # MongoDB models
│   ├── routes/           # API routes
│   ├── middleware/       # Custom middleware
│   ├── ml/              # Python ML service
│   │   ├── app.py       # Flask ML API
│   │   ├── best_resnet50_model.pth
│   │   └── requirements.txt
│   ├── socket.js        # Socket.IO configuration
│   └── index.js         # Main server file
└── README.md

PORT=5000
MONGO_URL=mongodb://localhost:27017/skinsight
JWT_SECRET=your_super_secret_jwt_key
OPENAI_API_KEY=sk-your_openai_api_key
NODE_ENV=development
CORS_ORIGIN=http://localhost:5173

VITE_API_URL=http://localhost:5000
VITE_ML_SERVICE_URL=http://localhost:5001

1. Local MongoDB:

   mongod --dbpath /path/to/your/data/directory

2. MongoDB Atlas:

   • Create a cluster at MongoDB Atlas
   • Get your connection string
   • Update MONGO_URL in your .env file

1. Build the project:

   cd client
   npm run build

2. Deploy to Netlify:

   • Connect your GitHub repository
   • Set build command: npm run build
   • Set publish directory: dist

1. Render:

   • Connect your GitHub repository
   • Set build command: npm install
   • Set start command: npm start
   • Add environment variables

2. Heroku:

   heroku create your-app-name
   heroku config:set MONGO_URL=your_mongodb_url
   heroku config:set JWT_SECRET=your_jwt_secret
   git push heroku main

1. Create a new Web Service on Render
2. Connect your GitHub repository
3. Set build command: pip install -r requirements.txt
4. Set start command: python app.py

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a 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

• Follow the existing code style
• Add tests for new features
• Update documentation as needed
• Ensure all tests pass before submitting

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

• OpenAI for providing the GPT-4 API
• MongoDB for the database solution
• Netlify and Render for hosting services
• Tailwind CSS for the beautiful UI framework
• React team for the amazing frontend framework

• Live Demo: https://skinsight.netlify.app
• ML Service: https://skinsight-ml.onrender.com
• Issues: GitHub Issues
• Email: support@skinsight.com