book

Build Python Web Apps with Streamlit

Name: Build Python Web Apps with Streamlit
Author: Aneev Kochakadan
ISBN: 9781633436015

by Aneev Kochakadan

May 2026

Intermediate

480 pages

13h 42m

English

Manning Publications

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Build Python Web Apps with Streamlit
copyright
contents
dedication
foreword
preface
acknowledgments
about this book
about the author
about the cover illustration

Part 1 Hitting the ground running
1 Introduction to Streamlit
1.1 Building web apps1.1.1 What do you need to build a web app?1.2 What is Streamlit?1.3 Ten reasons Streamlit is so popular1.3.1 Streamlit is pure Python1.3.2 Streamlit lets you go from idea to app in minutes1.3.3 Streamlit makes beautiful apps1.3.4 Streamlit lets you focus on your app, not UI details1.3.5 Streamlit’s syntax is simple, concise, and intuitive1.3.6 Streamlit works well with LLMs1.3.7 Streamlit has excellent support for data science and visualizations1.3.8 You can share your Streamlit apps for free, in record time1.3.9 Streamlit has a huge, friendly community1.3.10 You can extend Streamlit with third-party components or build your own1.4 What can you build with Streamlit?1.4.1 Data applications1.4.2 Apps that use Generative AI like LLMs1.4.3 Internal tools for your workplace1.4.4 Prototypes for large apps1.4.5 Anything else you can imagine1.5 When not to use Streamlit1.5.1 Complex, large-scale applications1.5.2 Apps that require a high degree of UI customization1.5.3 Native desktop or mobile apps1.6 How is Streamlit different from other technologies?1.6.1 Jupyter notebooks1.6.2 HTML, CSS, and JavaScript1.6.3 React1.6.4 Flask, Django, and FastAPI1.6.5 Tkinter and PyQt
2 Getting started with Streamlit
2.1 Getting Streamlit up and running2.2 Setting up your development environment2.2.1 Version control with Git2.2.2 Code editors2.2.3 Virtual environments2.3 Running Streamlit for the first time2.4 The Streamlit development workflow2.5 Building your first app2.5.1 A password checker2.5.2 Logic flow2.5.3 Going through the code2.5.4 Running the app2.5.5 Making changes to the app2.5.6 Killing and restarting the server
3 Taking an app from concept to code
3.1 Concept to code: A six-step process3.2 Stating our concept: A unit converter3.3 Defining the requirements3.3.1 Defining what the app won’t do3.4 Visualizing the user experience3.4.1 Creating a mock3.5 Brainstorming the implementation3.5.1 Converting units3.5.2 Tracking units and conversion factors3.5.3 Mapping the flow of logic3.6 Writing code3.6.1 Implementing the frontend3.6.2 Implementing the backend3.6.3 Wiring up the frontend and backend3.7 Iterating on the app3.7.1 Rounding conversion results3.7.2 Getting rid of the button
4 Streamlit’s execution model
4.1 A more complex app: Daily to-dos4.1.1 Stating the concept4.1.2 Defining the requirements and what is out of scope4.1.3 Visualizing the user experience4.1.4 Brainstorming the implementation4.2 Implementing and troubleshooting the app4.2.1 Displaying the task list4.2.2 Enabling actions4.3 How Streamlit executes an app4.3.1 Frontend and server4.3.2 App reruns4.3.3 Applying reruns to the app4.4 Persisting variables across reruns4.4.1 st.session_state4.5 Completing the app4.5.1 Adding session state4.5.2 Wiring up the Delete buttons4.5.3 What’s happening behind the scenes4.5.4 Triggering reruns automatically4.5.5 Wiring up the checkboxes4.5.6 An infinite rerun loop4.5.7 Preventing the infinite rerun4.5.8 Adding the completion progress indicator
5 Sharing your apps with the world
5.1 Deploying apps5.1.1 What is deployment?5.1.2 Options for deployment5.2 Deploying the to-do list app to Streamlit Community Cloud5.2.1 Prerequisites5.2.2 Deployment steps5.3 Deploying an app that uses an external service5.3.1 A joke of the day to entertain users5.3.2 Using the requests library to connect to an API5.3.3 Incorporating jokes in the app5.3.4 Accessing an API key safely with st.secrets5.4 Deploying changes to Streamlit Community Cloud5.4.1 Using a requirements.txt file to manage Python dependencies5.4.2 Using .gitignore to protect secrets.toml5.4.3 Configuring secrets in Community Cloud
Part 2 Diving into more complex apps
6 A dashboard fit for a CEO
6.1 A metrics dashboard6.1.1 Stating the concept6.1.2 Defining the requirements6.1.3 Visualizing the user experience6.1.4 Brainstorming the implementation6.2 Loading the data6.2.1 The pandas library6.2.2 Reading and displaying a dataframe6.2.3 Caching data6.3 Prepping and filtering the data6.3.1 Creating a filter panel6.3.2 Creating a date range selector6.4 Calculating and displaying metrics6.4.1 Calculating the metrics6.4.2 Setting up the metrics configuration6.4.3 Formatting the metrics6.4.4 Displaying the metrics6.5 Constructing visualizations6.5.1 Creating a time series chart6.5.2 Creating a pie chart
7 The CEO strikes back: Supercharging the dashboard
7.1 Feedback on the dashboard7.2 Granularity in the time series chart7.2.1 Enabling different time granularities7.2.2 Creating a time grain selector7.3 Interdependent filters7.4 Date range comparisons7.4.1 Adding another date range selector7.4.2 Showing the comparison in the metric bar7.5 A drilldown view7.5.1 Inserting a modal dialog7.5.2 Designing the content of the drilldown7.5.3 Implementing the drilldown view7.6 Enabling deeplinks7.6.1 Using st.query_params7.6.2 Setting widget defaults through st.session_state7.6.3 Implementing deeplinks7.7 Sourcing data from a data warehouse7.7.1 Getting data into BigQuery7.7.2 Setting up the Python-BigQuery connection7.7.3 Updating the dashboard to load data from BigQuery7.7.4 Notes on deployment to Streamlit Community Cloud
8 Building a CRUD app with Streamlit
8.1 Haiku Haven: A CRUD app in Streamlit8.1.1 Stating the concept and requirements8.1.2 Visualizing the user experience8.1.3 Brainstorming the implementation8.2 Setting up persistent storage8.2.1 Relational database concepts8.2.2 Haiku Haven’s data model8.2.3 PostgreSQL: A real relational database8.2.4 A crash course in SQL8.2.5 Connecting PostgreSQL to the app8.3 Creating user accounts8.3.1 Splitting the app into services8.3.2 Creating the user service8.4 Setting up a multi-page login flow8.4.1 Multi-page apps in Streamlit8.4.2 Implementing login8.4.3 Navigating between pages8.5 Creating, reading, updating, and deleting haikus8.5.1 Defining a HaikuService class8.5.2 Enabling users to create haikus8.5.3 The other CRUD operations: Read, Update, Delete8.6 Deploying Haiku Haven8.6.1 Setting up a managed PostgreSQL server in production8.6.2 Deploying to Community Cloud
Part 3 Streamlit and LLMs
9 Creating an AI-powered application
9.1 Fact Frenzy: An AI trivia game9.1.1 Stating the concept and requirements9.1.2 Visualizing the user experience9.1.3 Brainstorming the implementation9.2 Using AI to generate trivia questions9.2.1 Why use an LLM in Fact Frenzy?9.2.2 Setting up an OpenAI API key9.2.3 Calling the OpenAI API in Python9.2.4 Writing an LLM class9.2.5 The Game class9.2.6 Calling the Game class in the app9.3 Using AI to evaluate answers9.3.1 Structured Outputs9.4 Moving through game states9.5 Game mechanics: Keeping score, a New Game button, and Game Over9.5.1 Keeping score9.6 Including multiple questions9.6.1 Response variability, or lack thereof9.6.2 Implementing multiple questions9.7 Adding AI personalities9.7.1 Adding game settings
10 RAG and agentic apps with LangGraph and Streamlit
10.1 Nibby: A customer service bot10.1.1 Stating the concept and requirements10.1.2 Visualizing the user experience10.1.3 Brainstorming the implementation10.1.4 Installing dependencies10.2 Creating a basic chatbot10.2.1 Introduction to LangGraph and LangChain10.2.2 Graphs, nodes, edges, and state10.2.3 A one-node LLM graph10.3 Multi-turn conversations10.3.1 Adding memory to the graph10.3.2 Displaying the conversation history10.4 Restricting the bot to customer support10.4.1 Creating a base prompt10.4.2 Inserting a base context node in the graph10.5 Retrieval Augmented Generation10.5.1 What is Retrieval Augmented Generation?10.5.2 Implementing RAG in our app10.6 Turning the bot into an agent10.6.1 What are agents?10.6.2 The ReAct framework10.6.3 Creating tools an agent can use10.6.4 Making our graph agentic
Part 4 Testing and deploying apps
11 Testing Streamlit apps
11.1 MovieScout: An app to test11.1.1 Using the TMDB API to pull movie info11.1.2 The backend11.1.3 The frontend11.2 Testing the application11.2.1 Different kinds of tests11.2.2 Our testing approach11.2.3 A quick introduction to pytest11.3 Unit testing the backend11.3.1 How unit testing works11.3.2 Writing unit tests for the backend11.3.3 Running the tests11.4 Unit testing the Streamlit frontend11.4.1 Writing unit tests for the frontend11.4.2 Running frontend tests11.5 Testing the integration between components11.5.1 Writing integration tests11.6 Testing the end-to-end flow11.7 Evolving code and tests
12 Packaging and deploying Streamlit apps
12.1 Deployment considerations12.1.1 Why Streamlit Community Cloud may not work for some users12.1.2 Deployment alternatives12.1.3 Our approach12.2 Packaging the app using Docker12.2.1 Intro to Docker12.2.2 The app in a container12.2.3 Creating a Dockerfile12.2.4 Excluding files from an image12.2.5 Building an image and running a container12.2.6 Adding a Docker Compose configuration12.3 Deploying to AWS Elastic Beanstalk12.3.1 An overview of AWS12.3.2 Introducing Elastic Beanstalk12.3.3 A mental model of the deployed app12.3.4 Creating and preparing an AWS account12.3.5 Modifying your app setup for Elastic Beanstalk12.3.6 Creating an Elastic Beanstalk app12.4 Adding a custom domain and HTTPS12.4.1 Adding a domain12.4.2 Creating a certificate12.4.3 Adding the certificate to your load balancer12.5 Go forth and develop!
appendix Installing Python and Streamlit
A.1 Checking the current Python versionA.2 Installing the right Python versionA.2.1 WindowsA.2.2 macOSA.2.3 What if I want multiple Python versions on my system?A.3 Installing StreamlitA.4 Getting the streamlit command to workA.4.1 Editing environment variables on WindowsA.4.2 Editing environment variables on macOS

Content preview from Build Python Web Apps with Streamlit

6 A dashboard fit for a CEO

This chapter covers

Building an interactive metrics dashboard
Wrangling data using the pandas library
Caching the results of functions to improve Streamlit app performance
Creating filters, panels, and other widgets in a dashboard
Developing data visualizations and charts using Plotly

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In well-run companies, the answer—or part of it—is metrics. Executives rely on a ...

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Build Python Web Apps with Streamlit

by Aneev Kochakadan