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What Is Streamlit? Build Data Apps in Pure Python

Streamlit turns a Python script into an interactive web app — charts, tables, inputs and chat interfaces — without writing HTML or JavaScript. How it works, a minimal example, what it's great for (dashboards, AI demos, internal tools), its limits, and how to deploy it.

Streamlit is an open-source Python library that turns a script into a web app. You write Python; Streamlit draws the interface — sliders, buttons, tables, charts, file uploads, chat boxes — in the browser. No HTML, CSS or JavaScript.

It's hugely popular with data scientists, analysts and anyone building AI demos.

A minimal example

# app.py
import streamlit as st
import pandas as pd

st.title("Sales explorer")

uploaded = st.file_uploader("Upload a CSV", type="csv")
if uploaded:
    df = pd.read_csv(uploaded)
    region = st.selectbox("Region", sorted(df["region"].unique()))
    filtered = df[df["region"] == region]
    st.metric("Total sales", f"{filtered['amount'].sum():,.0f}")
    st.bar_chart(filtered, x="month", y="amount")
    st.dataframe(filtered)

pip install streamlit pandas
streamlit run app.py

A browser opens with a working app.

How it works (and why it feels different)

Streamlit reruns your whole script from top to bottom every time someone interacts — moves a slider, clicks a button. Each st. call draws part of the page.

That makes it very simple to reason about, with two consequences:

  • Expensive work must be cached, or it reruns on every click:

    @st.cache_data
def load_data():
    return pd.read_csv("big.csv")

  • Values you want to keep between reruns go in st.session_state.

What it's great for

  • Dashboards over data or a database
  • AI demos and chat interfaces — st.chat_input and st.chat_message make a chatbot UI in a few lines (How to add an AI chatbot)
  • Internal tools — admin views, data cleaning, reports for a team
  • Prototypes to show stakeholders quickly
  • Machine-learning model front-ends

Its limits

  • Customising the look beyond its components is limited; it always looks like a Streamlit app.
  • Complex, multi-page products with intricate state get awkward.
  • Each user session holds a connection and memory on the server, so very high traffic needs more resources than a typical web app.
  • Authentication is basic; for anything sensitive you'll add proper login. (Add login to an AI-built app)

When an internal tool becomes a customer-facing product, teams often move the front-end to React and keep Python as an API. (What is FastAPI?)

Alternatives

  • Gradio — similar, focused on ML model demos.
  • Dash (Plotly) — more control, more code.
  • NiceGUI, Reflex, Panel — other Python-first UI frameworks.

Deploying it

  • Streamlit Community Cloud — free hosting for public apps from a GitHub repo; apps sleep when unused.
  • Your own server — run streamlit run app.py --server.port 8501 --server.address 127.0.0.1 as a service, behind a reverse proxy with HTTPS. Streamlit uses WebSockets, so the proxy must allow them. (WebSockets explained, Reverse proxies)
  • Docker — common for company deployments.

Keep secrets (database passwords, API keys) in environment variables or Streamlit's secrets.toml — never in the code you push. (Environment variables)

EasySpawn runs your Streamlit app on an always-on server — no sleeping — behind HTTPS with WebSockets working, next to the database it reads from. See how it works or join the waitlist.

Related: What Is Python? · What Is FastAPI? · How to Run a Python Script 24/7 · Python Virtual Environments

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