Book Image

Web App Development Made Simple with Streamlit

By : Rosario Moscato
Book Image

Web App Development Made Simple with Streamlit

By: Rosario Moscato

Overview of this book

This book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.
Table of Contents (23 chapters)
Free Chapter
1
Part 1: Getting Started with Streamlit
5
Part 2: Building a Basic Web App for Essential Streamlit Skills
10
Part 3: Developing Advanced Skills with a Covid-19 Detection Tool
15
Part 4: Advanced Techniques for Secure and Customizable Web Applications

Diving deep into sentiment analysis

The sentiment analysis task is quite easy because we can leverage TextBlob, which has already been imported. So, let’s start with the very poor code we have, which, at the moment, just prints a subheading on the screen:

Figure 6.11: Sentiment Analysis section

Figure 6.11: Sentiment Analysis section

Currently, when we select Sentiment Analysis from our web application menu, we just get a subheading and some white space below it.

Figure 6.12: Sentiment Analysis starting point

Figure 6.12: Sentiment Analysis starting point

Let us start by creating a text area, since we need somewhere to add the text we want to analyze in order to extract its sentiment. Adding a text_area now is really quite simple for us:

Figure 6.13: A text_area for Sentiment Analysis

Figure 6.13: A text_area for Sentiment Analysis

This is the result of the preceding change on the browser side:

Figure 6.14: The text area in the browser

Figure 6.14: The text area in the browser

Now, we can type something in the text area and store...