README.md

metadata

title: Medical Chatbot
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - medical
  - chatbot
  - rag
  - gemini
  - streamlit

Medical Chatbot 🏥

An intelligent medical question-answering chatbot that uses retrieval-augmented generation (RAG) with Gemini 1.5 Flash, Sentence Transformers, and Pinecone DB.

Features

• 🤖 Powered by Gemini 1.5 Flash for natural language understanding
• 📊 Uses Sentence Transformers for semantic search
• 🔍 Retrieves relevant medical information from vector database
• 📚 Provides citations with source attribution
• 🎯 Confidence scoring for each response
• 🌐 Beautiful Streamlit interface
• ⚠️ Important disclaimers for medical advice

Prerequisites

1. Python 3.8 or higher
2. Pinecone account (https://www.pinecone.io/)
3. Google AI Studio API key (https://makersuite.google.com/app/apikey)
4. Hugging Face account (optional, for accessing datasets)

Installation

For detailed step-by-step instructions, see QUICK_START.md

1. Clone or download this repository

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file in the root directory:

PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_ENVIRONMENT=us-east1
GOOGLE_API_KEY=your_google_api_key_here

4. Set up the database:

python setup_database.py

This will download medical data from Hugging Face and upload it to Pinecone.

Usage

Run the Streamlit application:

streamlit run app.py

Open your browser to the URL shown (typically http://localhost:8501)

Quick Start Guide: QUICK_START.md

How It Works

1. Data Loading: Medical questions and answers are loaded from Hugging Face datasets
2. Embedding: Texts are converted to embeddings using Sentence Transformers
3. Vector Storage: Embeddings are stored in Pinecone for fast similarity search
4. Query Processing: User queries are embedded and searched against the database
5. Response Generation: Gemini 1.5 Flash generates responses based on retrieved context
6. Citation: Sources are tracked and displayed with confidence scores

Important Disclaimers

• ⚠️ This is not medical advice
• ⚠️ Not a substitute for professional healthcare
• ⚠️ Always consult healthcare professionals for medical decisions
• ⚠️ Confidence scores indicate data quality, not medical accuracy

Configuration

Edit config.py to customize:

• Embedding model
• Number of retrieved documents (TOP_K)
• Similarity threshold
• Dataset selection

Troubleshooting

"API Key not found"

• Ensure your .env file exists and contains valid API keys

"Index not found"

• Run python setup_database.py to create the Pinecone index

"No results found"

• The similarity threshold might be too high
• Adjust SIMILARITY_THRESHOLD in config.py

License

This project is for educational purposes only. Medical information should be verified with healthcare professionals.