Introduction to Streamlit: Python Web Apps Without HTML

One of the biggest challenges in data science and machine learning is sharing your work with people who don’t use Python. Jupyter notebooks require a running kernel; command-line scripts produce no visual output; and building a proper web frontend requires HTML, CSS, and JavaScript skills most data scientists don’t have. Streamlit solves this problem by letting you turn a plain Python script into a fully interactive web application — with no frontend code whatsoever. You write Python, run one command, and anyone with a browser can use your app.

What Is Streamlit?

Streamlit is an open-source Python library designed for building data dashboards, AI demos, and interactive internal tools. It is especially popular for:

AI chat interfaces — connect to a language model and wrap it in a UI
Data dashboards — upload a CSV, visualize it, and filter results interactively
ML model demos — let non-technical users adjust inputs and see predictions
Internal business tools — replace clunky spreadsheet workflows with polished apps

How Streamlit Works

Streamlit follows a simple client-server architecture:

Your Python script runs as a server process.
The browser displays the rendered interface as a client.
When the user interacts with any widget (button, slider, text field), the browser sends the event to the server.
Streamlit reruns your entire script from top to bottom to refresh the UI with the updated values.

This rerun model is the key mental model to internalize. Every interaction triggers a full script re-execution. This keeps the UI in sync with your Python logic automatically — but it also means you need to understand caching (covered in the Data Handling chapter) to avoid re-running expensive operations on every rerun.

A Simple Example

import streamlit as st

st.title("Hello Streamlit")
name = st.text_input("Your name")

if name:
    st.write(f"Hello, {name}!")

Every time you type a character in the text field, Streamlit reruns the script, evaluates the if block, and updates the greeting instantly.

Creating and Running an App

Recommended Project Structure

A simple Streamlit project typically looks like this:

my_project/
├── app.py          ← main app logic
├── data/           ← CSV or JSON data files
├── images/         ← local images used in the app
├── pages/          ← additional pages for multipage apps
└── requirements.txt

For multipage apps, place additional Python files inside a pages/ directory. Streamlit automatically detects them and adds navigation links in the sidebar — no routing code required.

What’s in This Module

The Streamlit module is organized into five sections that build on each other:

Section	What You’ll Learn
Displaying Content	Text, tables, images, code blocks, status messages
Widgets	Buttons, text inputs, sliders, file uploaders, date pickers
Data Handling	Loading CSVs, filtering DataFrames, Matplotlib charts
Building Apps	Full capstone project — a Student Performance Dashboard

SQL & Databases

Data Analysis

FastAPI

Frontend

Introduction to Streamlit: Python Web Apps Without HTML

What Is Streamlit?

How Streamlit Works

A Simple Example

Creating and Running an App

Recommended Project Structure

What’s in This Module

​What Is Streamlit?

​How Streamlit Works

​A Simple Example

​Creating and Running an App

​Recommended Project Structure

​What’s in This Module

What Is Streamlit?

How Streamlit Works

A Simple Example

Creating and Running an App

Recommended Project Structure

What’s in This Module