Based on Transfer Learning of VGG16 architecture. The work uses Kaggle Pneumonia dataset https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/home for training and validation. It uses Flask webapp by mtobeiyf to deploy the Keras Model https://github.com/mtobeiyf/keras-flask-deploy-webapp.

• Clone this repo
• Install requirements
• Run the script
• Check http://localhost:5000
• Done! 🎉

👇Screenshot:

$ git clone https://github.com/palanithanarajk/pneumonia-diagnosis.git

$ pip install -r requirements.txt

Make sure you have the following installed:

• tensorflow
• keras
• flask
• pillow
• h5py
• gevent

Python 2.7 or 3.5+ are supported and tested.

$ python app.py

Open http://localhost:5000 and have fun. 😃

Analyze

Diagnosis

Modify files in templates and static directory.

index.html for the UI and main.js for all the behaviors

To deploy it for public use, you need to have a public linux server.

Run the script and hide it in background with tmux or screen.

$ python app.py

You can also use gunicorn instead of gevent

$ gunicorn -b 127.0.0.1:5000 app:app

More deployment options, check here

To redirect the traffic to your local app. Configure your Nginx .conf file.

server {
    listen  80;

    client_max_body_size 20M;

    location / {
        proxy_pass http://127.0.0.1:5000;
    }
}

Try this live AI Demo https://dl-cad-pneumonia.herokuapp.com/

Check Siraj's "How to Deploy a Keras Model to Production" video. The corresponding repo.

Building a simple Keras + deep learning REST API