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Build Python Web Apps with Streamlit: AI and data applications in minutes
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You just built something amazing in Python and you’re ready to share it with the world! But does shipping it to the web mean…learning JavaScript? With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript.
Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. You’ll also learn from intentional mistakes and real-world debugging challenges that teach you how Streamlit actually works. As you go, each project helps you master software engineering skills you might not have learned as a Python programmer—gathering requirements, persistence and database integration, user authentication, deployment, and troubleshooting.
• Build interactive web apps without HTML/CSS/JavaScript
• Understand Streamlit’s execution model
• Work with databases and persistent data
• Create and deploy production-grade architectures
• Implement user authentication and authorization
• Build AI-powered applications with LLMs
• Develop dashboards, and visualizations
• Implement security best practices
About the technology
With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript.
About the book
Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. As you go, you’ll practice debugging, deployment, database integration and other software engineering skills.
What's inside
• Understand Streamlit’s execution model
• Work with databases and persistent data
• Implement user authentication and authorization
• Build AI-powered applications
About the reader
For Python programmers. No web app or AI skills required.
About the author
Aneev Kochakadan is a software engineer at OpenAI, with prior experience at Stripe and Google. He has designed and built systems ranging from online transactional services to data pipelines and business intelligence tools.
Table of Contents
Part 1
1 Introduction to Streamlit
2 Getting started with Streamlit
3 Taking an app from concept to code
4 Streamlit’s execution model
5 Sharing your apps with the world
Part 2
6 A dashboard fit for a CEO
7 The CEO strikes Supercharging the dashboard
8 Building a CRUD app with Streamlit
Part 3
9 Creating an AI-powered application
10 RAG and agentic apps with LangGraph and Streamlit
Part 4
11 Testing Streamlit apps
12 Packaging and deploying Streamlit apps
Appendixes
A Installing Python and Streamlit
You just built something amazing in Python and you’re ready to share it with the world! But does shipping it to the web mean…learning JavaScript? With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript.
Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. You’ll also learn from intentional mistakes and real-world debugging challenges that teach you how Streamlit actually works. As you go, each project helps you master software engineering skills you might not have learned as a Python programmer—gathering requirements, persistence and database integration, user authentication, deployment, and troubleshooting.
• Build interactive web apps without HTML/CSS/JavaScript
• Understand Streamlit’s execution model
• Work with databases and persistent data
• Create and deploy production-grade architectures
• Implement user authentication and authorization
• Build AI-powered applications with LLMs
• Develop dashboards, and visualizations
• Implement security best practices
About the technology
With the Streamlit framework, you can build interactive web apps entirely in Python incredibly fast. By providing a collection of pre-built UI components and streamlined configurations, Streamlit turns your ideas for data tools and AI workflows into usable applications without any tedious HTML, CSS, and JavaScript.
About the book
Build Python Web Apps with Streamlit follows a proven learn-by-building approach. Each chapter introduces a new hands-on project. You’ll create data dashboards, interactive checklists, and even an AI chatbot optimized with RAG and agentic patterns. As you go, you’ll practice debugging, deployment, database integration and other software engineering skills.
What's inside
• Understand Streamlit’s execution model
• Work with databases and persistent data
• Implement user authentication and authorization
• Build AI-powered applications
About the reader
For Python programmers. No web app or AI skills required.
About the author
Aneev Kochakadan is a software engineer at OpenAI, with prior experience at Stripe and Google. He has designed and built systems ranging from online transactional services to data pipelines and business intelligence tools.
Table of Contents
Part 1
1 Introduction to Streamlit
2 Getting started with Streamlit
3 Taking an app from concept to code
4 Streamlit’s execution model
5 Sharing your apps with the world
Part 2
6 A dashboard fit for a CEO
7 The CEO strikes Supercharging the dashboard
8 Building a CRUD app with Streamlit
Part 3
9 Creating an AI-powered application
10 RAG and agentic apps with LangGraph and Streamlit
Part 4
11 Testing Streamlit apps
12 Packaging and deploying Streamlit apps
Appendixes
A Installing Python and Streamlit
480 pages, Paperback
Published June 9, 2026
Ratings & Reviews
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Community Reviews
Displaying 1 - 4 of 4 reviews
October 1, 2025
Streamlit is an excellent open-source Python library that enables developers and data scientists to build and share beautiful, custom web apps for machine learning and data science in a matter of hours, not weeks. Its key strength is its simplicity; you can turn data scripts into shareable web applications with just a few lines of code.
The book to read to get clear, detailed access to this tech stack is 'Build Python Web Apps with Streamlit: AI and Data Applications in Minutes, by Aneev Kochakadan, published by Manning.
This comprehensive guide teaches one everything you need to know about StreamLit from its background through to writing a wide variety of StreamLit apps. The range of use cases is impressive, with some examples including interactive data dashboards, machine learning model demos, data exploration and profiling tools, educational explainers and simulators, and simple data collection front-ends.
A particular strength of this fine, friendly book is its chapters on RAG and agentic apps, utilising StreamLit with LangGraph, as well as its focus on testing and deployment.
If you are in any way in the business of creating front ends for Python apps, this is the book you need. (Note the name change from the cover here)
The book to read to get clear, detailed access to this tech stack is 'Build Python Web Apps with Streamlit: AI and Data Applications in Minutes, by Aneev Kochakadan, published by Manning.
This comprehensive guide teaches one everything you need to know about StreamLit from its background through to writing a wide variety of StreamLit apps. The range of use cases is impressive, with some examples including interactive data dashboards, machine learning model demos, data exploration and profiling tools, educational explainers and simulators, and simple data collection front-ends.
A particular strength of this fine, friendly book is its chapters on RAG and agentic apps, utilising StreamLit with LangGraph, as well as its focus on testing and deployment.
If you are in any way in the business of creating front ends for Python apps, this is the book you need. (Note the name change from the cover here)
June 22, 2026
I have been working with Streamlit for a couple years now, having started on it when I was looiking for an easier way to write GenAI applications. I wish I had read this book sooner as one of the main pitfalls I had was how to handle session state, and after asking about it I learned, but there are areas that can cause.a person to trip that Aneev Kochakadan managed to help people to avoid.
Streamlit is a great framework to use if you want to write web applications for a smallish group of people and the author explains when you may want to go beyond what it can do. I use it to demo to co-workers some application.
I loved the fact that we learn to go beyond what I did and to create dashboard/visualizations using it, and how to pull data from other places and at the end to add GenAI capabilities to the application.
One of the things I love about streamlit is I can just share it with people, they can run it and have the same web app without having to stand up much.
I didn't realize about the visualizations so it is nice when I learn how to use a tool better.
If you are new to programming you may find the approach useful, starting with how to define the problem, and work out requirements before you write anything.
Overall this book is great for using this great tool.
Streamlit is a great framework to use if you want to write web applications for a smallish group of people and the author explains when you may want to go beyond what it can do. I use it to demo to co-workers some application.
I loved the fact that we learn to go beyond what I did and to create dashboard/visualizations using it, and how to pull data from other places and at the end to add GenAI capabilities to the application.
One of the things I love about streamlit is I can just share it with people, they can run it and have the same web app without having to stand up much.
I didn't realize about the visualizations so it is nice when I learn how to use a tool better.
If you are new to programming you may find the approach useful, starting with how to define the problem, and work out requirements before you write anything.
Overall this book is great for using this great tool.
Review of advance copy received from Publisher
Top Highlights
- Real world use. The book gives you clear code to get apps running fast.
- Chatbot guides. The walkthroughs for building AI chat interfaces are a win.
- Broad coverage. It handles the basics of the Streamlit framework well.
The Reality Check
- Needs more honesty. I wanted a better look at what Streamlit can and can't do compared to other tools.
- The customization wall. You can build a basic app in minutes, but it still feels like a struggle to make anything highly custom.
- Speed vs. Control. The book stays on the surface. It doesn't solve the typical headache of trying to fix complex UI layouts in this framework.
- Real world use. The book gives you clear code to get apps running fast.
- Chatbot guides. The walkthroughs for building AI chat interfaces are a win.
- Broad coverage. It handles the basics of the Streamlit framework well.
The Reality Check
- Needs more honesty. I wanted a better look at what Streamlit can and can't do compared to other tools.
- The customization wall. You can build a basic app in minutes, but it still feels like a struggle to make anything highly custom.
- Speed vs. Control. The book stays on the surface. It doesn't solve the typical headache of trying to fix complex UI layouts in this framework.
June 12, 2026
Wonderful book , got a chance to review it while it was in making itself . It was quite useful for me and my team to rapidly build streamlit applications
Displaying 1 - 4 of 4 reviews