MedDio

An Intelligent Medical Diagnosis System that seamlessly integrates a chatbot interface with medical image analysis capabilities for streamlined and interactive diagnostics.

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Table of Contents

• Overview
• Features
• Installation
• Usage
• Project Structure
• Contributing
• License

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Overview

MedDio combines natural language-based interactions with medical image processing to assist in diagnostic tasks. Users can converse with a chatbot, upload medical images (e.g., X-rays, scans), and receive insights based on both text understanding and image analysis.

The system is implemented using Python, likely involving components that handle interactive chat sessions and image-based inference models.

The application is deployable via a Streamlit interface, as evident from the repository's URL :contentReference[oaicite:1]{index=1}.

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Features

• Chatbot-Based Diagnostics: Users can describe symptoms or ask health-related questions through a conversational interface.
• Medical Image Analysis: Integrates image processing and deep learning to interpret uploaded medical images.
• Interactive UI: Launched as a web app (via Streamlit), offering an intuitive and interactive experience.
• Modular & Extensible: Structured codebase, suitable for model updates or expanding diagnostic capabilities.

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Installation

1. Clone the Repository

   git clone https://github.com/subhash-kr0/MedDio.git
   cd MedDio

2. Set Up the Environment It's recommended to use a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Prepare the Models and Data

   • Train or acquire any necessary models in the models/ directory.
   • Place sample images in the data/ directory if required.
   • Use notebook files for exploration or experimentation in the notebooks/ directory.

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Usage

To launch the app:

Project Structure

streamlit run app.py

MedDio/
├── .devcontainer/           # Dev environment config
├── data/                    # Data samples
├── models/                  # Trained models
├── notebooks/               # Training / exploration notebooks
├── reports/metrics/         # Evaluation reports
├── src/                     # Core modules
├── static/                  # Static assets
├── vectorstore/db_faiss/    # Vector database (if used)
├── app.py                   # Main Streamlit app
├── utils.py                 # Utility functions
├── connect_memory_with_ll.py
├── create_memory_for_llm.py
├── chat_history.json        # Example chat logs
├── setup.py                 # Package setup
├── requirements.txt         # Dependencies
├── README.md                # Documentation
└── LICENSE                  # License

Contributing

Contributions are welcome!

1. Fork the repo 🍴
2. Create your feature branch (git checkout -b feature-name)
3. Commit changes (git commit -m "Add feature")
4. Push to branch (git push origin feature-name)
5. Open a Pull Request 🔥

License

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