MediMinds is an intelligent medical chatbot that transforms your medical documents into an interactive knowledge base. Built with state-of-the-art AI technology, it helps medical professionals, students, and researchers quickly find and understand medical information from their documents.

Here's how MediMinds looks in action:

The interface shows:

1. Clean, intuitive chat interface
2. Direct question-answer format
3. Medical-specific responses
4. Easy-to-read formatting

• 🤖 Intelligent Responses: Get concise, accurate answers based on your documents
• 💻 User-Friendly Interface: Clean, intuitive chat interface for easy interaction
• ⚡ Fast Processing: Quick response times with efficient document processing
• 🔒 Local Processing: All documents are processed locally for privacy

• LLM: Mistral-7B-Instruct-v0.3
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• Vector Store: FAISS
• Framework: Langchain
• Frontend: Streamlit
• Document Processing: LangChain Document Loaders

mediminds/
├── data/                           # Store your medical PDFs here
│   └── documents/
├── vector_db                       # FAISS index storage
│   ├── index.faiss
│   └── index.pkl
├── src/                            # Source code directory
|   ├── __init__.py
│   ├── llm/
│   │   ├── model_config.py
│   │   └── vectorstore_init.py
│   │   └── __init__.py
│   ├── app/
│   │   └── chatbot.py
│   │   └── __init__.py
│   └── utils/
│       └── config.py
│       └── __init__.py
├── .env               # HuggingFace API Token
├── setup.py           # Package setup
├── requirements.txt   # Dependencies
└── README.md         # Documentation

• Python 3.8+
• Hugging Face API Token
• Medical PDFs for the knowledge base

1. Clone the repository:

git clone https://github.com/Manish-Kumar24/MediMinds.git
cd MediMinds

2. Install dependencies:

pip install -e .

3. Set up your environment:

   • Create a .env file in the root directory
   • Add your Hugging Face API token:

   HF_TOKEN=your_huggingface_token_here
   

4. Add your medical PDFs:

   • Place your PDF files in the data/documents/ directory

5. Initialize the vector store:

python src/llm/vectorstore_init.py

6. Run the application:

streamlit run src/app/chatbot.py

1. Starting a conversation:

   User: What are the common symptoms of Type 2 Diabetes?
   
   MediMinds: Common symptoms of Type 2 Diabetes include:
   - Increased thirst and frequent urination
   - Fatigue and increased hunger
   - Slow-healing sores and frequent infections
   

2. Follow-up questions:

   User: How is Type 2 Diabetes diagnosed?
   
   MediMinds: Type 2 Diabetes is diagnosed through blood tests including:
   - Fasting Blood Sugar Test (FBS)
   - Glycated Hemoglobin (A1C) Test
   

• This chatbot's knowledge is limited to the documents you provide
• Always verify medical information with certified healthcare professionals
• Keep your documents up-to-date for the most accurate responses

We welcome contributions! Please feel free to submit pull requests.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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

• Hugging Face for the LLM infrastructure
• Langchain community for the framework
• Streamlit team for the UI framework

For questions and support, please open an issue in the GitHub repository.

Made with ❤️ by Manish Kumar