1 Introduction to Streamlit

Streamlit makes it possible to build interactive, shareable web apps entirely in Python in minutes, lowering the barrier for developers, analysts, and enthusiasts who don’t want to learn a separate frontend stack. The chapter motivates why you might choose Streamlit—automating workplace tasks, showcasing skills, presenting data interactively, prototyping ideas, or exploring AI—and outlines a hands-on, project-driven path to mastering it. It frames web apps as approachable, browser-based interfaces that are easier to share than command-line tools, then positions Streamlit as the solution that merges backend logic with a clean, click-ready UI without requiring HTML, CSS, or JavaScript.

Streamlit’s popularity stems from its pure-Python simplicity, rapid idea-to-app workflow, and attractive, sensible defaults that free you to focus on app logic. Its intuitive API makes common patterns—inputs, state, charts, and layouts—concise, while first-class support for visualizations and dataframes integrates seamlessly with libraries like Matplotlib and Pandas. The framework also shines for LLM and generative AI use cases through chat-friendly UI elements, offers an easy path to public sharing via a free hosting option, benefits from an active, helpful community, and can be extended with third-party components when you need capabilities beyond the built-ins.

The chapter surveys what you can build: data dashboards, exploration tools, ML model frontends, AI apps, lightweight internal utilities, quick prototypes for larger systems, and creative experiments—essentially anything Python can power behind an interactive UI. It also lays out pragmatic limits: Streamlit isn’t ideal for massive, highly concurrent apps, pixel-perfect or heavily customized interfaces, or native desktop/mobile deployments. Finally, it clarifies how Streamlit differs from adjacent tools: it targets end-user apps rather than exploratory notebooks; abstracts the web triad of HTML/CSS/JS; trades React’s fine-grained control for Pythonic ease; complements backend frameworks like Flask, Django, and FastAPI by handling the frontend; and focuses on web delivery rather than desktop GUIs like Tkinter or PyQt.

A Google Trends chart showing the popularity of Streamlit over time (note: the periodic dips near the end of each year correspond to the week between Christmas and New Year's Day, when I assume relatively few people are working).

Streamlit unlocks web app development for anyone who knows Python and helps even full-stack developers prototype and build faster.

Tabs in Streamlit, illustrating how Streamlit makes UI choices for you.

Output of a die roll simulator in Streamlit.

A complete AI chatbot in Streamlit.

A histogram in Streamlit created using the popular Matplotlib library

An editable Pandas dataframe as displayed in Streamlit (see chapter_01/data_editor_example.py in the GitHub repo).

Dungeon, a game created with Streamlit (https://dungeon.streamlit.app/) created by Tomasz Hasiów.

• Streamlit is a framework for building web apps in pure Python without HTML, CSS, or JavaScript.
• Streamlit has been gaining popularity due to its simplicity, development velocity, LLM support, powerful visualizations, and integration with data science libraries, among other features.
• With Streamlit, you can create many types of applications: data apps, internal workplace tools, LLM apps, prototypes for larger apps, and more.
• You shouldn't use Streamlit for large-scale apps meant for millions of users, or apps that require a high level of UI customization.

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