WomenWhoCode Hackathon Covid Detection and Antibiotic Resistance Prevention application.

The application can be found here.

To run the application clone the repository and follow these steps :

1. Clone the repository to your local machine.
2. Create a new python virtual environment.
   
3. Got to the Deployment directory and open terminal.
   
4. Run pip install -r requiremnts.txt to install the required dependencies.
   
5. Run streamlit run app.py to run the application.
   

[IEEE8023 GitHub Account] [Mendeley]

• DenseNet121. The pre-trained files were obtained from the course 'AI for Medical Diagnosis', in Coursera

• AWS
• AWS Sagemaker
• AWS S3

• Streamlit
• Heroku

The project demonstrates the Covid Detection and Antibiotic Resistance Prevention application built into an interactive Streamlit app.

• Random Selection from ZhangLabData.ipynb
  Function: From thousands of ‘Normal’ and ‘Bacterial Pneumonia’ images in Mendeley, this code randomly selects 490 for each class for our project.
  
• Dataset_Creation_Final.ipynb
  Function: It splits all images into training, test and validation sets using stratified random sampling in order to ensure that all sets come from a similar distribution.

• SageMaker_Model.ipynb
  Function: It generates our model in AWS SageMaker.
  
• densenet.hdf5
  Function: These are the pre-trained weights, obtained from the course 'AI for Medical Diagnosis', in Coursera.
  

• model_architecture.json and model_weights.h5
  Function: These are our trained model files.
  
• model_prediction.py
  Function: It generates predictions using our model for the X-rays uploaded in the streamlit application.
  
• app.py
  Function: This is the main streamlit application that generates prediction for an uploaded X-ray image and also provide antibiotic resistance guidelines.
  
• requirements.txt
  Function: It contains all the package versions used for deployment.