On the verge of defeating the tyranny of biology
This is an example of the tandem of PyTorch, Django and Heroku.
This application was written as a practice.All you have to do is upload a chest x-ray, and then you will get a Grad-cam and prediction with probabilities.
If you are interested in creating any interface for other people to interact with your ML-models, then this repository can be an example and starting point for this.
- This is Resnet18, trained on a dataset on NIH Chest X-ray Datasetfrom
The predictive model of the service has a minimal configuration due to the limitations of the Heroku free server.
1.Your apps configuration:
- host: smtp.gmail.com
- port: 587 or 465 (587 for tls, 465 for ssl)
- protocol: tls or ssl
- user: YOUR_USERNAME @ gmail.com
- password: YOUR_PASSWORD
- The given Gmail account settings:
- If you've turned on 2-Step Verification for your account, you might need to enter an App password.
- Without 2-Step Verification:
- Allow less secure apps access to your account.
- Visit http://www.google.com/accounts/DisplayUnlockCaptcha and sign in with your Gmail username and password.