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Sebastian Raschka, 2015

Python Machine Learning - Code Examples

Chapter 9 - Embedding a Machine Learning Model into a Web Application

  • Serializing fitted scikit-learn estimators
  • Setting up a SQLite database for data storage
  • Developing a web application with Flask
  • Our first Flask web application
    • Form validation and rendering
    • Turning the movie classifier into a web application
  • Deploying the web application to a public server
    • Updating the movie review classifier
  • Summary

The code for the Flask web applications can be found in the following directories:

  • 1st_flask_app_1/: A simple Flask web app
  • 1st_flask_app_2/: 1st_flask_app_1 extended with flexible form validation and rendering
  • movieclassifier/: The movie classifier embedded in a web application
  • movieclassifier_with_update/: same as movieclassifier but with update from sqlite database upon start

To run the web applications locally, cd into the respective directory (as listed above) and execute the main-application script, for example,

cd ./1st_flask_app_1
python3 app.py

Now, you should see something like

 * Running on http://127.0.0.1:5000/
 * Restarting with reloader

in your terminal. Next, open a web browser and enter the address displayed in your terminal (typically http://127.0.0.1:5000/) to view the web application.

Link to a live example application built with this tutorial: http://raschkas.pythonanywhere.com/.