Medi-Care Project

Introduction

Medi-Care utilizes LLaVA-Med base weights for precise visual question answering in medical images, alongside the LLaMA model for accurate answer generation. This combination enables efficient and accurate diagnostic support, enhancing healthcare outcomes.

Installation

Steps

1. Clone the repository:

   git clone https://github.com/rahulsharmavishwakarma/medi-care.git
   cd medi-care

2. Set up a virtual environment (optional but recommended):

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

3. Upgrade pip:

   pip install --upgrade pip

4. Install the required libraries: Ensure you have a requirements.txt file and then run the following command to install all dependencies:

   pip install -r requirements.txt

5. Install Git Large File Storage (LFS) if required:

   git lfs install

Starting the Inference

To start the inference, run the following commands in the terminal:

1. Start the controller:

   python -m llava.serve.controller --host 0.0.0.0 --port 10008

2. Start the model worker:

   python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10008 --port 40000 --worker http://localhost:40000 --model-path /teamspace/studios/this_studio/Medi-Care/LLaVA-Med-weights --multi-modal --num-gpus 1

3. Run the Gradio web server:

   python -m llava.serve.gradio_web_server --controller http://localhost:10008 --share

Contributing

We welcome contributions from the community. To contribute:

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

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