Skip to content

GDSC-IIIT-Kalyani/MediCare-Prime

 
 

Repository files navigation

MediCare-Prime

Prediction or detection of various medical ailments. Deployed locally using Flask 2.0.

Setup

If you do not have Anaconda or Miniconda3 installed install it using the following link

Anaconda : https://www.anaconda.com/products/individual

MiniConda3 : https://docs.conda.io/en/latest/miniconda.html

If you have Anaconda installed create a conda environment :

  • Install the current release of CPU-only TensorFlow, recommended for beginners:
conda create -n tf 
conda activate tf
conda install -c conda-forge tensorflow
  • Or, to install the current release of GPU TensorFlow on Linux or Windows:
conda create -n tf-gpu
conda activate tf-gpu
conda install tensorflow-gpu
  • Cd into your newly created environment (from command line or terminal)
cd C:\...\path-to-your-conda-environment\
  • Installing modules we will need Though your virtual env will have all required modules, here are some extra ones required to setup this project locally
pip install flask
pip install pillow

Running the code

  • Fork and clone the project.
git clone https://github.com/IIITKalyaniFOSC/MediCare-Prime
  • Cd into your cloned repo (folder with the same name as the repo on your system)
cd C:\...\path-to-your-cloned-repo\
  • After making sure your tf conda environment we just created above, is activated, run the app.py file
python app.py

Succesfull installation and running will give you a link you can visit locally. For any exceptions, kindly recheck the entire process and try again, or feel free to create an issue.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

✨ Contributors

License

GPL

About

Prediction or detection of various medical ailments

Topics

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Jupyter Notebook 70.5%
  • CSS 24.3%
  • HTML 4.6%
  • Other 0.6%