Medical Chatbot with Appointment Booking 🏥

Overview 🎯

The Medical Chatbot is a Streamlit-powered virtual medical assistant that combines advanced NLP with appointment scheduling capabilities. Using retrieval-augmented generation (RAG) techniques and trusted medical references, it provides accurate medical information while facilitating seamless appointment bookings.

Features ✨

• 🤖 Intelligent Medical Q&A

  • RAG-based information retrieval
  • Trusted medical reference integration
  • Context-aware responses

• 💬 Interactive Interface

  • User-friendly Streamlit interface
  • Empathetic conversation design
  • Real-time response generation

• 📅 Appointment Management

  • Keyword-triggered booking flow
  • Automated scheduling system
  • Confirmation notifications

• 🔒 Safety & Compliance

  • Medical disclaimers
  • Educational purpose clarity
  • Professional guidance emphasis

Technical Architecture 🏗️

medical-chatbot/
├── app.py                # Main Streamlit application
├── requirements.txt      # Project dependencies
├── code.py               # Code without GUI

Installation 🚀

Prerequisites

• Python 3.7+
• OpenAI API key
• Modern web browser

Setup Steps

1. Clone the repository:

git clone https://github.com/Siddhant231xyz/Medical-ChatBot.git
cd Medical-ChatBot

2. Create virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Configure OpenAI API:

export OPENAI_API_KEY='your-api-key-here'

Usage Guide 📚

1. Start the application:

streamlit run app.py

2. Interact with the chatbot:
   • Ask medical questions
   • Type "appointment" for booking
   • Review responses with disclaimers

How It Works 🔄

1. Document Processing

   • PDF text chunking
   • Vector store indexing
   • Semantic search capability

2. Conversation Flow

   • Query understanding
   • Context retrieval
   • Response generation
   • Appointment integration

Deployment Options 🌐

Streamlit Cloud

1. Connect to GitHub repository
2. Configure environment variables
3. Deploy application

Alternative Platforms

• Heroku
• AWS
• Google Cloud

Contributing 🤝

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

Dependencies 📦

• streamlit
• langchain-openai
• langchain-core
• langchain-community
• langchain-text-splitters
• langgraph

License 📝

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

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Created with ❤️ by Medical ChatBot Team