Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

Latest commit

 

History

History
History
 
 

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/.

Morty Proxy This is a proxified and sanitized view of the page, visit original site.