https://github.com/abelmou/rag-healthcare-assistant
RAG-Powered Healthcare Assistant is an AI-driven tool using Retrieval-Augmented Generation (RAG) with multilingual support (Darija, Arabic, English) to provide personalized health info. It features Prescription OCR, multilingual queries, and a Pharmacy Locator to offer tailored, accurate healthcare responses and services.
https://github.com/abelmou/rag-healthcare-assistant
ai-healthcare-assistant chainlit chromadb darija deep-learning health-chatbot langchain llama llama3 machine-learning medical-records multilingual-support ollama pharmacy-locator prescription-ocr rag rag-healthcare-assistant streamlit
Last synced: 7 months ago
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RAG-Powered Healthcare Assistant is an AI-driven tool using Retrieval-Augmented Generation (RAG) with multilingual support (Darija, Arabic, English) to provide personalized health info. It features Prescription OCR, multilingual queries, and a Pharmacy Locator to offer tailored, accurate healthcare responses and services.
- Host: GitHub
- URL: https://github.com/abelmou/rag-healthcare-assistant
- Owner: abelmou
- Created: 2024-12-16T07:34:47.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T07:42:25.000Z (10 months ago)
- Last Synced: 2025-04-04T05:09:38.006Z (7 months ago)
- Topics: ai-healthcare-assistant, chainlit, chromadb, darija, deep-learning, health-chatbot, langchain, llama, llama3, machine-learning, medical-records, multilingual-support, ollama, pharmacy-locator, prescription-ocr, rag, rag-healthcare-assistant, streamlit
- Language: Python
- Homepage:
- Size: 1.71 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://shorturl.at/HHoY6).
## 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:
```bash
git clone https://github.com/abelmou/RAG-Healthcare-Assistant.git
cd RAG-Healthcare-Assistant
```
2. Create and activate a virtual environment (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Install and start Ollama (if not already done):
```bash
# 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):
```bash
streamlit run ocr.py
```
2. Pharmacy Locator Interface:
```bash
streamlit run pharmascysol.py
```
3. Chainlit Chat Interface (for health queries):
```bash
chainlit run app_chainlit.py
```
4. Streamlit Documents Assitant :
```bash
streamlit run app_streamlit.py
```
5. Main Interface:
```bash
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.