Deploying your ML models on the web, sharing them, and making the awesome web interface part 2

In the previous part, we have designed the app.py our main interface and in this part, we are gonna focus on the functionality of our application. Before creating the functionality of the application we will make sure that our model is ready for this I found drive as the best platform for storing our model since on GitHub we can store models just up to 20 MB. We load the model using Keras load_model function and return it for making predictions on that model. Finally, make sure you commit and push to your GitHub repository. If you want to learn how to push files on GitHub refer to Jayesh Jain's blog