Beginner's guide to Streamlit with Python : build web-based data and machine learning applications / Sujay Raghavendra.
Raghavendra, Sujay, author.
2023
TK5105.875.I6
Available Online

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Title Beginner's guide to Streamlit with Python : build web-based data and machine learning applications / Sujay Raghavendra.
Author Raghavendra, Sujay, author.
Edition [First edition].
ISBN 9781484289839 (electronic bk.)
1484289838 (electronic bk.)
9781484289822
148428982X
DOI https://doi.org/10.1007/978-1-4842-8983-9
Published New York, NY : Apress, [2023]
Language English
Description 1 online resource (215 pages) : illustrations
Item Number 10.1007/978-1-4842-8983-9 doi
Call Number TK5105.875.I6
Dewey Decimal Classification 006.7/8
Summary 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.
Note Includes index.
Access Note Access limited to authorized users.
Available in Other Form Print version: 9781484289822
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Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction to Streamlit
What Is Streamlit?
Why Streamlit?
Why Streamlit for Data Science and ML Engineers?
Features of Streamlit
Open Source
Platforms
Ease of Development
Interactive Applications
Reduced Time of Development
No Core Web Development Knowledge
Easy to Learn
Model Implementation
Compatibility
Literate Programming Document
Streamlit Cloud
Optimize Change
Error Notifications

Comparing Streamlit to Alternative Frameworks
Installing Python
Installing Streamlit on Windows
Installing Streamlit on Linux
Installing Streamlit on macOS
Testing the Streamlit Installation
Creating Our First App
Summary
Chapter 2: Text and Table Elements
Text Elements
Titles
Headers
Subheaders
Captions
Plain Text
Markdown
LaTeX
Code
Data Elements
Dataframes
Tables
Metrics
JSON
The write() Function as a Superfunction
Magic
Summary
Chapter 3: Visualization
The Importance of Visualization

Visualization in Streamlit
Purpose of Visualization
Streamlit Functions
Bar
Line
Area
Map
Graphviz
Seaborn
Count
Violin
Strip
Altair
Boxplot
Area
Heatmap
Plotly
Pie
Donut
Scatter
Line
Bar
Bar Horizontal
Subplots
Summary
Chapter 4: Data and Media Elements
Images
Multiple Images
Background Image
Resizing an Image
Audio
Video
Balloon
Snowflake
Emojis
Summary
Chapter 5: Buttons and Sliders
Buttons
Radio Buttons
Check Boxes
Drop-Downs
Multiselects
Download Buttons

Progress Bars
Spinners
Summary
Chapter 6: Forms
Text Box
Text Area
Number Input
Time
Date
Color
File Upload
Text/Docx Document
PDF Upload
Dataset Upload
Image Upload
Uploading Multiple Images
Saving Uploaded Documents
Submit Button
Summary
Chapter 7: Columns and Navigation
Columns
Spaced-Out Columns
Columns with Padding
Grids
Expanders/Accordions
Containers
Empty Containers
Sidebars
Multipage Navigation
Main Page
Pages
Summary
Chapter 8: Control Flow and Advanced Features
Alert Box
st.info()

St.warning()
st.success()
st.error()
st.exception()
Control Flow
Stop Execution
Rerun the Script
st.form_submit_button
Advanced Features
Configuring the Page
st.echo
st.experimental_show
Session State
Performance
Caching
st.experimental_memo
st.experimental_memo.clear()
st.experimental_singleton
st.experimental_singleton.clear
Summary
Chapter 9: Natural Language Processing
NLP App Creation
User Input
Cleaning the Text
Predictions
Setting Up Files
Requirement Text
setup.sh
Procfile
GitHub Repository Creation

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