Beginner's Guide to Streamlit with Python: Build Web-Based Data and Machine Learning Applications

ISBN-13: 9781484289822 / Angielski / Miękka / 2022 / 203 str.

Sujay Raghavendra

Opis

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models.Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You’ll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you’ll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit.After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own.What You Will Learn

• How to start developing web applications using Streamlit
• What are Streamlit's components 
• Media elements in Streamlit
• How to visualize data using various interactive and dynamic Python libraries
• How to implement models in Streamlit web applications

Who This Book Is For

Professionals working in data science and machine learning domains who want to showcase and deploy their work in a web application with no prior knowledge of web development.

Spis treści

​Chapter 1. Introduction to Steamlit

Chapter Goal: Introduce the reader to the Streamlit library

No of pages - 10

Sub -Topics

1. A brief introduction to Streamlit

2. Pre-requisites and installation guide for Streamlit

3. Creating our first Streamlit application

 

Chapter 2. Texts & Table Elements

Chapter Goal: The text is one of the important features that will be discussed in this chapter.

No of pages - 10

Sub -Topics

1. Write title, header, sub-header, markdown and a caption.

2. Code text, latex and default text in an application.

3. json, table, metric and dataframe in the application.

Chapter 3. Charts / Visualization 

Chapter Goal: Visualization is one of the important aspects in data science and machine learning. The visualizing techniques helps to understand the data more appropriately. In this chapter, we will implement different visualizing techniques that are available in python for data science and machine learning developers. 

No of pages - 20

Sub -Topics

1. Implementing simple charts 

2. Visualizing data using interactive charts in the application.

3. Implementing data into the maps. 

Chapter 4. Data and Media Elements

Chapter Goal: In this chapter, we will learn how media elements can be used in the streamlit application. 

No of pages - 20

Sub -Topics

1. We will first try to implement simple charts to start with and display them on the application.

2. Next, we will visualize data using interactive charts in the application.

3. At last, we will see how we can use data into the maps. 

Chapter 5. Buttons

Chapter Goal: One more important feature from Streamlit are buttons. These buttons are used to select the required data to process or visualize in the application developed. 

No of pages - 20

Sub -Topics

1. Introduction to buttons

2. Discuss various buttons like download button, checkbox, radio buttons and multiselect.

3. Sliders to select specific range of data.

Chapter 6. Forms

Chapter Goal: This chapter mainly focusses on data that will be provided by the user to process data in the application. We will discuss user data in terms forms.   

No of pages - 20

Sub -Topics

1. Discuss various types input data like numbers and text.

2. Discuss advanced input data like date, time, file uploads and color picker.  

3. Getting live image data from webcam

Chapter 7. Navigations

Chapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation.   

No of pages - 20

Sub -Topics

1. Discuss on navigation. 

2. Discuss the complex layouts associated with it.

3. Discuss on containers that can be used to hold multiple elements in it.

Chapter 8. Control Flow and Status

Chapter Goal: We will discuss on custom handling of application using control flow in this chapter. We will also learn on status elements provided by streamlit.   

No of pages - 20

Sub -Topics

1. Handling functionality of the application using control flow. 

2. Flow control of application can be changed from its default flow.

3. We will also check on the what are status elements? and their types available in Streamlit.

Chapter 9. Advanced Features

Chapter Goal: In this chapter, we will discuss on huge data handling, mutating data and optimizing performance of the Streamlit application.   

No of pages - 20

Sub -Topics

1. Handling huge data in the Streamlit Application developed for data science and machine learning.

2. Implementing various optimizing techniques to improve performance of the application. 

3. How to mutate data in live application.

Chapter 10. Project Build

Chapter Goal: Finally, we will discuss to build and run complete application on various platforms.   

No of pages - 10

Sub -Topics

1. Discuss various application platforms available.

2. Pre-requisites to implement developed application on these platforms.

3. Implement and run the project.

4. Test application on deployment and create requirement files for it.

Chapter 11. Use case: NLP Project Prototype

Chapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation.   

No of pages - 10

Sub -Topics

1. Pre-requisites.

2. NLP module that will be implemented in our application.

3. Test application after deployment.

Chapter 12. Use case: Computer Vision Project Prototype

Chapter Goal: We will develop a complete streamlit application on Computer Vision from scratch. We will see how all the features we have seen in the above chapters will be implemented in this application 

No of pages - 10

Sub -Topics

1. Pre-requisite.

2. Computer Vision techniques that needs to implemented.

3. Test all functions implemented on our deployed application.

O autorze

Sujay Raghavendra is an IT professional with a Master’s Degree in Information Technology. His research interests include machine learning, computer vision, NLP, and deep learning. He has been a consultant for multiple research centers in various universities. He has published many research articles in international journals and is the author of the book “Python Testing with Selenium” published by Apress. 

Blurb

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you’ll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models.

Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You’ll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you’ll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit.

After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own.

You will:

• Start developing web applications using Streamlit
• Understand Streamlit's components 
• Utilize media elements in Streamlit
• Visualize data using various interactive and dynamic Python libraries
• Implement models in Streamlit web applications