Embedding a Machine Learning Model into a Web Application

In the previous chapters, you learned about the many different machine learning concepts and algorithms that can help us with better and more efficient decision-making. However, machine learning techniques are not limited to offline applications and analyses, and they have become the predictive engine of various web services. For example, popular and useful applications of machine learning models in web applications include spam detection in submission forms, search engines, recommendation systems for media or shopping portals, and many more.

In this chapter, you will learn how to embed a machine learning model into a web application that can not only classify, but also learn from data in real time. The topics that we will cover are as follows:

• Saving the current state of a trained machine learning model
• Using SQLite databases for data storage
• Developing a web application using the popular Flask web framework...

Serializing fitted scikit-learn estimators

Training a machine learning model can be computationally expensive, as you saw in Chapter 8, Applying Machine Learning to Sentiment Analysis. Surely, we don't want to retrain our model every time we close our Python interpreter and want to make a new prediction or reload our web application?

One option for model persistence is Python's in-built pickle module (https://docs.python.org/3.7/library/pickle.html), which allows us to serialize and deserialize Python object structures to compact bytecode so that we can save our classifier in its current state and reload it if we want to classify new, unlabeled examples, without needing the model to learn from the training data all over again. Before you execute the following code, please make sure that you have trained the out-of-core logistic regression model from the last section of Chapter 8 and have it ready in your current Python session:

>>> import pickle
>>>...

Setting up an SQLite database for data storage

In this section, we will set up a simple SQLite database to collect optional feedback about the predictions from users of the web application. We can use this feedback to update our classification model. SQLite is an open source SQL database engine that doesn't require a separate server to operate, which makes it ideal for smaller projects and simple web applications. Essentially, an SQLite database can be understood as a single, self-contained database file that allows us to directly access storage files.

Furthermore, SQLite doesn't require any system-specific configuration and is supported by all common operating systems. It has gained a reputation for being very reliable and is used by popular companies such as Google, Mozilla, Adobe, Apple, Microsoft, and many more. If you want to learn more about SQLite, visit the official website at http://www.sqlite.org.

Fortunately, following Python's batteries included philosophy...

Developing a web application with Flask

Having prepared the code for classifying movie reviews in the previous subsection, let's discuss the basics of the Flask web framework to develop our web application. Since Armin Ronacher's initial release of Flask in 2010, the framework has gained huge popularity, and examples of popular applications that use Flask include LinkedIn and Pinterest. Since Flask is written in Python, it provides us Python programmers with a convenient interface for embedding existing Python code, such as our movie classifier.

The Flask microframework

Flask is also known as a microframework, which means that its core is kept lean and simple but it can be easily extended with other libraries. Although the learning curve of the lightweight Flask API is not nearly as steep as those of other popular Python web frameworks, such as Django, you are encouraged to take a look at the official Flask documentation at https://flask.palletsprojects...

Turning the movie review classifier into a web application

Now that we are somewhat familiar with the basics of Flask web development, let's advance to the next step and implement our movie classifier into a web application. In this section, we will develop a web application that will first prompt a user to enter a movie review, as shown in the following screenshot:

After the review has been submitted, the user will see a new page that shows the predicted class label and the probability of the prediction. Furthermore, the user will be able to provide feedback about this prediction by clicking on the Correct or Incorrect button, as shown in the following screenshot:

If a user clicked on either the Correct or Incorrect button, our classification model will be updated with respect to the user's feedback. Furthermore, we will also store the movie review text provided by the user, as well as the suggested class label, which can be inferred from the button click,...

Deploying the web application to a public server

After we have tested the web application locally, we are now ready to deploy our web application onto a public web server. For this tutorial, we will be using the PythonAnywhere web hosting service, which specializes in the hosting of Python web applications and makes it extremely simple and hassle-free. Furthermore, PythonAnywhere offers a beginner account option that lets us run a single web application free of charge.

Creating a PythonAnywhere account

To create a new PythonAnywhere account, we visit the website at https://www.pythonanywhere.com/ and click on the Pricing & signup link that is located in the top-right corner. Next, we click on the Create a Beginner account button where we need to provide a username, password, and valid email address. After we have read and agreed to the terms and conditions, we should have a new account.

Unfortunately, the free beginner account doesn't allow us to access the remote...

Summary

In this chapter, you learned about many useful and practical topics that will extend your knowledge of machine learning theory. You learned how to serialize a model after training and how to load it for later use cases. Furthermore, we created an SQLite database for efficient data storage and created a web application that lets us make our movie classifier available to the outside world.

So far, in this book, we have covered many machine learning concepts, best practices, and supervised models for classification. In the next chapter, we will take a look at another subcategory of supervised learning, regression analysis, which lets us predict outcome variables on a continuous scale, in contrast to the categorical class labels of the classification models that we have been working with so far.