wehub-resource-sync
42 lines
1.4 KiB
Markdown
42 lines
1.4 KiB
Markdown
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/**.
|