Mental-Health-Chatbot-Solutions

MediRAG is a comprehensive healthcare platform that leverages artificial intelligence to provide accessible, personalized healthcare solutions.

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MediRAG - Intelligent Healthcare Solutions 🏥✨

MediRAG Banner

📋 Overview

MediRAG is a comprehensive healthcare platform that leverages artificial intelligence to provide accessible, personalized healthcare solutions. Built with modern web technologies including JavaScript, TypeScript, and Node.js, it aims to revolutionize patient care, streamline medical processes, and provide easy access to health-related information and services.

💡 Vision: Making quality healthcare accessible through intelligent technology integration

🏗️ Healthcare Application Architecture

The MediRAG platform follows a client-server architecture with AI integration for advanced healthcare features.

MediRAG Architecture

System Architecture Diagram

Healthcare Application Architecture

graph TD
    A[Frontend - React.js] -->|API Requests| B[Backend - Node.js]
    B -->|Responses| A
    B -->|AI Processing| C[OpenAI API]
    C -->|Analysis Results| B
    B -->|Data Storage| D[(Database)]
    E[Users] -->|Interacts with| A
    B -->|Image Processing| F[PDF to Image Conversion]

✨ Key Features

🔍 X-ray and Document Diagnosis

  • AI-powered analysis of medical images and documents
  • Quick and accurate diagnoses with confidence levels
  • Support for various file formats including images and PDFs
  • Detailed analysis reports with recommendations

X-ray Analysis

🥗 Personalized Health Plans

  • Tailored nutrition recommendations based on individual profiles
  • Custom sleep routines addressing specific sleep issues
  • Personalized caloric intake and macronutrient distribution
  • Daily meal plans with timing suggestions

Health Plans

📅 Appointment Scheduling

  • Intuitive interface for booking medical appointments
  • Selection from various healthcare professionals
  • Customizable appointment types and reasons
  • Automatic email confirmations and reminders

Appointment Scheduling Email Confirmation

🧠 Mental Health Support

  • 24/7 access to AI-assisted mental health resources
  • Interactive chatbot with empathetic responses
  • Integration with professional support services
  • Relaxation exercises and resources

Mental Health Chatbot

🛠️ Technology Stack

Layer Technologies
Frontend React.js, TypeScript, Tailwind CSS
Backend Node.js, Express
AI Integration OpenAI API
File Processing Multer, pdf-img-convert
Styling Tailwind CSS, Lucide React (icons)
Routing React Router
HTTP Requests Axios

🚀 Setup Instructions

Prerequisites

  • Node.js (v14 or higher)
  • npm or yarn
  • OpenAI API key

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/healthcare-website.git
cd healthcare-website

  2. Setup the backend:

    cd backend
npm install

  3. Setup the frontend:

    cd ../frontend
npm install

  4. Configure environment variables: Create a .env file in the backend directory with:

    PORT=3001
OPENAI_API_KEY=your_openai_api_key
DATABASE_URL=your_database_connection_string

  5. Start the development servers:

For backend:

   cd backend
   npm run dev

For frontend:

   cd frontend
   npm start
  1. Access the application: Open your browser and navigate to http://localhost:3000

📡 API Endpoints

Endpoint Method Description Request Body Response
/api/analyze-image POST Analyze medical images File upload (image/PDF) Diagnosis results with confidence level
/api/HealthPlans POST Generate health plans Age, weight, height, activity level, dietary restrictions, sleep issues Personalized diet and sleep routine
/api/mental-health-chat POST Mental health chat User message AI assistant response
/api/test GET Test backend connectivity - Connection status
/api/appointments Various Manage appointments Appointment details Confirmation/details

📦 Project Structure

healthcare-website/
├── backend/               # Node.js backend
│   ├── index.js           # Main server file
│   ├── uploads/           # Storage for uploaded files
│   └── package.json       # Backend dependencies
├── frontend/              # React frontend
│   ├── public/            # Static files
│   ├── src/               # Source code
│   │   ├── components/    # React components
│   │   ├── styles/        # CSS styles
│   │   ├── api/           # API service files
│   │   └── routes/        # Frontend routes
│   └── package.json       # Frontend dependencies
└── README.md              # Project documentation

🔄 Component Flow

flowchart TB
    A[HomePage] --> B[XrayDiagnosis]
    A --> C[HealthPlans]
    A --> D[AppointmentScheduling]
    A --> E[MentalHealthSupport]

    B -- "Upload Image" --> B1[Backend API]
    B1 -- "AI Analysis" --> B2[Display Results]

    C -- "Submit Health Info" --> C1[Backend API]
    C1 -- "Generate Plan" --> C2[Display Health Plan]

    D -- "Book Appointment" --> D1[Save Appointment]
    D1 --> D2[Send Confirmation]

    E -- "User Message" --> E1[Backend API]
    E1 -- "AI Response" --> E2[Display Response]

🧪 Features in Detail

X-ray Diagnosis Process

  1. Upload: User uploads X-ray image or PDF document
  2. Processing: System converts PDFs to images if needed
  3. AI Analysis: OpenAI API analyzes the image with expert radiologist prompting
  4. Results: System returns diagnosis, confidence level, and recommendations

Health Plan Generation

  1. Input Collection: User provides health information and preferences
  2. AI Processing: System generates personalized diet and sleep plans
  3. Presentation: Interactive display of health recommendations
  4. Follow-up: Optional monitoring and adjustment features

Mental Health Support System

  1. User Interface: Chatbot with friendly, empathetic design
  2. Context Management: Conversation history tracking for coherent responses
  3. AI Responses: Empathetic and supportive message generation
  4. Resources: Integration with relaxation videos and exercises

📝 Contributing

We welcome contributions to improve MediRAG. Please follow these steps:

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

📄 License

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

👏 Acknowledgments

  • OpenAI for providing the AI models
  • React and Node.js communities for excellent documentation
  • All contributors who have helped improve this project

📞 Contact

Your Name - arkavaiya@gmail.com

Project Link: https://github.com/Yash-Kavaiya/MediRAG

**MediRAG** - Your Partner in Modern Healthcare

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