ch09

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

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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/.