RAG-Powered Healthcare Assistant

An AI-driven healthcare assistant that utilizes Retrieval-Augmented Generation (RAG) with multilingual support in Darija, Arabic, and English to offer personalized health management. The app helps users with prescription scanning, health queries, multilingual support, and pharmacy locator.

• Watch the video demo of our app video-demo.

Background and Problem Statement

Managing healthcare information effectively remains a challenge in today's world. Common issues include:

• Fragmented medical records
• Difficulty in obtaining personalized medical advice
• Language barriers in healthcare communication
• Difficulty locating nearby healthcare providers

Impact and Proposed Solution

The RAG-Powered Healthcare Assistant aims to solve these challenges by:

• Centralizing personal health records for easy access
• Leveraging RAG to provide intelligent, context-aware responses to health queries
• Enabling Prescription OCR for easy scanning and processing of prescriptions
• Offering multilingual support (Darija, Arabic, English) for diverse users
• Incorporating a Pharmacy Locator with geolocation to help users find nearby pharmacies and healthcare providers

Project Outcomes and Deliverables

This project delivers:

• A fully functional AI-powered healthcare assistant
• A multilingual chatbot for health queries
• An OCR system for prescription scanning and processing
• An integrated Pharmacy Locator with real-time geolocation
• Secure storage and retrieval of personalized medical records

Technology Stack

• Frontend:
  • Streamlit - Main web interface
  • Chainlit - Chat interface
• Backend:
  • Python 3.9+
  • LangChain - For AI/LLM orchestration
  • ChromaDB - Vector database for document storage
  • Ollama - Local LLM integration
  • OpenCV - Image processing
  • PyPDF & PDFPlumber - PDF processing
  • Unstructured - Document parsing
  • ElevenLabs - Text-to-speech capabilities

Prerequisites

Before running the application, ensure you have:

1. Python 3.9 or higher installed
2. Git installed
3. Ollama installed (for local LLM support)
4. Sufficient disk space for dependencies and document storage
5. A modern web browser

Installation and Setup

1. Clone the repository:

   git clone https://github.com/abelmou/RAG-Healthcare-Assistant.git
   cd RAG-Healthcare-Assistant
   

2. Create and activate a virtual environment (recommended):

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

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Install and start Ollama (if not already done):

   # Follow Ollama installation instructions at: https://ollama.ai/
   # Pull the required model:
   ollama pull llama3.2
   

Installation and Setup

1. OCR Interface (for prescription scanning):

   streamlit run ocr.py
   

2. Pharmacy Locator Interface:

   streamlit run pharmascysol.py
   

3. Chainlit Chat Interface (for health queries):

   chainlit run app_chainlit.py
   

4. Streamlit Documents Assitant :

   streamlit run app_streamlit.py
   

5. Main Interface:

   streamlit run main.py
   

The application will be accessible through your web browser at the provided local URL.

Future Enhancements

• Expanding language support to more regions and dialects.
• Improving OCR accuracy and expanding prescription types supported.
• Adding integration with healthcare provider APIs for real-time health data.