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MedBuddy MyCut

MedBuddy MyCut is a cutting-edge healthcare assistant application powered by generative AI. This project integrates advanced AI capabilities to enhance the process of connecting patients with healthcare professionals, making it easier to find doctors, schedule appointments, and manage healthcare-related queries efficiently.

Project Overview

MedBuddy MyCut leverages the Gemini AI model to offer a comprehensive solution for healthcare management. Key features include:

• Intelligent Doctor Matching: Find doctors based on location and specialization with AI-driven search capabilities.
• Appointment Booking: Seamlessly book appointments with doctors through an interactive chatbot interface.
• Real-Time Communication: Receive automated email confirmations and reminders for both patients and doctors.
• User-Friendly Interface: A modern frontend built with React and Vite provides a smooth user experience.
• Reliable Backend: FastAPI-based backend ensures efficient data handling and integration with a MongoDB database.

Features

• AI-Powered Doctor Discovery: Search for doctors by location and specialization, with detailed profiles including ratings, experience, and fees.
• Appointment Management: Book, reschedule, or cancel appointments with ease.
• Email Notifications: Automated confirmation and reminder emails for appointment scheduling.
• Interactive Chatbot: Engaging chatbot interface for natural interaction and query resolution.

Technology Stack

• Frontend: React, Vite
• Backend: FastAPI, Python
• AI Integration: Gemini AI Model
• Database: MongoDB
• Email Service: Brevo (formerly Sendinblue)
• Environment Management: Virtualenv

Project Structure

MedBuddy_mycut/
├── backend/
│   ├── api/
│   │   └── gemini_data.py
│   ├── database/
│   │   └── connect.py
│   ├── Routes/
│   │   └── doctor.py
│   └── Schema/
│       └── schema.py
├── frontend/
│   ├── node_modules/
│   ├── public/
│   │   └── vite.svg
│   ├── src/
│   │   ├── assets/
│   │   │   ├── chatbot.avif
│   │   │   └── react.svg
│   │   ├── components/
│   │   │   ├── Chat.jsx
│   │   │   ├── Footer.jsx
│   │   │   ├── Homepage.jsx
│   │   │   └── Navbar.jsx
│   │   ├── App.css
│   │   ├── App.jsx
│   │   ├── index.css
│   │   ├── main.jsx
│   │   └── output.css
│   ├── .gitignore
│   ├── eslint.config.js
│   ├── index.html
│   ├── package-lock.json
│   ├── package.json
│   ├── postcss.config.js
│   ├── README.md
│   ├── tailwind.config.js
│   └── vite.config.js
├── venv/
├── .env
├── .gitignore
├── main.py
├── medbuddy.iml
├── requirements.txt
└── test_main.http

Setup and Installation

Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

Backend

1. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

2. Install dependencies:

   pip install -r requirements.txt

3. Run the FastAPI server:

   uvicorn main:app --reload

Environment Variables

Create a .env file in the root directory and add the following variables:

GOOGLE_API_KEY=<Your Google API Key>
DB_URI=<Your MongoDB URI>
RESEND_API_KEY=<Your Resend API Key>
BREVO_API_KEY=<Your Brevo API Key>

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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