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Turn Python Scripts into Interactive AI-Powered Apps with Streamlit
Key Features ● Build complete Streamlit apps from data exploration to deployment ● Integrate ML models and AI chatbots into interactive Python apps ● Master caching, state management, cloud deployment, and multipage architecture
Book Description Streamlit has transformed how developers present data science and machine-learning work by making it effortless to turn Python scripts into interactive web applications. Building Data Apps with Streamlit provides a complete, hands-on roadmap to creating professional, production-ready apps using Streamlit's fast, intuitive, and Pythonic framework.
You begin with Streamlit's architecture, layout system, and component ecosystem, learning how to build clean, scalable apps with widgets, callbacks, caching, and session state. The book then guides you through handling secrets, managing configurations, working with APIs and databases, and building multipage workflows that feel polished and responsive.
By the end, you will build a full Streamlit solution that analyzes datasets, trains machine-learning models, and powers an AI chatbot using Google Gemini. With dedicated chapters on testing, optimization, and cloud deployment, this book equips you with the confidence and skills to create, iterate, and share high-quality Streamlit applications that bring your data and ideas to life.
What you will learn ● Build interactive data apps using Streamlit's core components ● Manage session state, caching, themes, and configurations effectively ● Connect apps to APIs, databases, and cloud services ● Integrate machine-learning workflows into Streamlit interfaces ● Create and deploy an AI chatbot using Google Gemini ● Test, deploy, and maintain Streamlit apps on the cloud
Table of Contents 1. Introduction to Streamlit 2. Setting Up the Development Environment 3. Creating and Deploying Your First Streamlit App 4. Exploring Streamlit's Flow and Architecture 5. Persisting Data and State Across App Reruns 6. Exploring Streamlit's Page Elements 7. Widget Keys and Callbacks 8. Introduction to Streamlit Caching and Connections 9. Managing Secrets in Streamlit 10. Advanced App Management Concepts 11. App Configuration Options 12. Building Multipage Streamlit Apps 13. Testing Streamlit Apps 14. Building a Data Analysis Streamlit App 15. Building a Machine Learning Streamlit App 16. Building a Chatbot on Streamlit Index
Siddhant Sadangi is a Developer Experience Engineer at neptune.ai, focusing on Python, MLOps, and Streamlit. A recognized Streamlit Creator and Community Moderator, he builds open-source tools and guides developers in turning data science projects into interactive web apps.
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