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Web App Development Made Simple with Streamlit

Web App Development Made Simple with Streamlit

By : Moscato
4.3 (10)
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Web App Development Made Simple with Streamlit

Web App Development Made Simple with Streamlit

4.3 (10)
By: 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)
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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

Summary

This chapter was very important since we learned how to integrate AI into our web applications.

AI is a highly disruptive technology that can completely change the game when managed and leveraged properly in our applications. In this chapter, we learned that it is possible to include AI models developed and trained separately in Streamlit. Several packages and tools can create AI models, and almost all these models can be included in Streamlit; it’s only a matter of checking the proper documentation and following the rules.

Loading AI models into our web applications is only one part of the process since each model needs to be fed information in a specific format. For this reason, all the data we provide as input to the model that we decide to use must be pre-processed so that it’s compliant with that specific format. This is the reason why we spent a good amount of our time manipulating and transforming the image of the X-ray we uploaded in a format that...

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