Hands-On Python Deep Learning for the Web

Implementing the backend

In this section, we will work on creating the required APIs along with the server script for the demonstration. Edit the app.py file in the root folder of the project:

1. First, we will make some necessary imports to the script:

from flask import Flask, request, jsonify, render_templateimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.neural_network import MLPClassifiernp.random.seed(5)

Notice that the imports here are very similar to the imports we made during model creation in the Jupyter notebook. This is explained due to the fact that we're only converting the Jupyter notebook code into a server script for the backend ...