Streamlit turns a normal Python script into an interactive browser application. You can build dashboards, data tools, model demos and AI interfaces without writing a separate frontend for the basic workflow. This tutorial takes you from installation to a deployable CSV dashboard, then explains reruns, caching, session state, multipage structure, secrets and production trade-offs.
Streamlit is a strong fit for Python-first, data-centric applications. It is not a universal replacement for a conventional frontend and backend: highly branded consumer sites, complex transactional systems and very large workloads usually need a different architecture.
What is Streamlit?
Streamlit is an open-source Python framework that runs your script, captures Streamlit commands and renders the result in a browser. The framework provides the local web server, widgets, layouts and common data displays, so a basic app needs no hand-written HTML, CSS or JavaScript. See the official documentation and main concepts.
import streamlit as st
st.title("My first Streamlit app")
st.write("Hello from Python!")Running this script displays a local page with a title and text. Streamlit is particularly useful for dashboards, exploratory analysis, internal tools, machine-learning demos, evaluation interfaces, reports and chat applications.
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How Streamlit executes your code
The key idea is the rerun model. After a widget interaction, Streamlit normally executes the script from top to bottom again, then redraws the page.
- A user changes a widget or submits an interaction.
- The widget’s callback, if any, runs.
- The script reruns from the beginning.
- Streamlit renders the updated output for that browser session.
Do not expect an ordinary local variable to preserve a value between reruns. Widget keys and st.session_state preserve user-specific values; caching avoids repeating suitable expensive work. A button is true only for the interaction that triggered it, while most widgets return their current value on every run.
Read the current execution guidance in the fundamentals summary.
Install Streamlit and create a project
Prerequisites
- Basic Python, including imports, functions, lists and dictionaries.
- A terminal or command prompt and a code editor.
- A supported Python environment; verify compatibility in the current installation documentation.
- Optional familiarity with pandas for data work.
Create an isolated environment
mkdir streamlit-demo
cd streamlit-demo
python -m venv .venvActivate it on macOS or Linux:
source .venv/bin/activateOn Windows PowerShell:
.venvScriptsActivate.ps1Install and verify
pip install streamlit
python --version
pip show streamlit
streamlit version
streamlit helloCreate app.py in your editor. On macOS or Linux, touch app.py also works. Start the app with:
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streamlit run app.pyStreamlit starts a local server and typically opens a browser tab. The exact installed version is more useful than an unverified “latest” number, so check it locally.
Build a minimal app
import streamlit as st
st.set_page_config(
page_title="Streamlit Demo",
page_icon="🎈",
layout="centered",
)
st.title("Streamlit Tutorial")
st.subheader("A small Python web app")
st.write("This interface is rendered from a Python script.")
name = st.text_input("What is your name?")
if name:
st.success(f"Hello, {name}!")st.title()creates a prominent heading.st.write()displays text and many Python objects.st.text_input()creates an input widget.- The conditional block appears only after a name is entered.
Saving the file prompts a rerun or reruns automatically, depending on the current workflow and settings. The official walkthrough is Create an app.
Build a useful CSV dashboard
This complete example accepts a CSV, previews it, finds numeric columns and charts a selected column. It also handles no upload and files without numeric data.
import streamlit as st
import pandas as pd
st.set_page_config(page_title="Sales Dashboard", layout="wide")
st.title("Sales Dashboard")
uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
if uploaded_file is None:
st.info("Upload a CSV file to begin.")
st.stop()
df = pd.read_csv(uploaded_file)
st.subheader("Preview")
st.dataframe(df, use_container_width=True)
numeric_columns = df.select_dtypes(include="number").columns.tolist()
if not numeric_columns:
st.warning("The file contains no numeric columns for charting.")
st.stop()
column = st.selectbox("Choose a numeric column", numeric_columns)
st.subheader(f"Distribution of {column}")
st.bar_chart(df[column].value_counts().sort_index())An uploaded file is temporary input, not an automatic database. Use a database, object store or another external service when records must survive sessions or restarts.
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Widgets, forms and layout
Common input controls
import streamlit as st
st.header("Widget examples")
age = st.number_input("Age", min_value=0, max_value=120, value=30)
department = st.selectbox("Department", ["Sales", "Marketing", "Engineering"])
tags = st.multiselect("Interests", ["Python", "Data", "AI", "Visualization"])
agree = st.checkbox("I agree")
if st.button("Submit"):
if not agree:
st.error("Please confirm the checkbox.")
else:
st.success(f"Submitted: age={age}, department={department}, interests={tags}")Other built-in controls include st.radio, st.slider, st.text_input, st.text_area, st.date_input, st.file_uploader, st.data_editor and st.download_button.
Batch related inputs with a form
with st.form("profile_form"):
username = st.text_input("Username")
department = st.selectbox("Department", ["Sales", "Engineering", "Support"])
submitted = st.form_submit_button("Save")
if submitted:
if not username.strip():
st.error("Username is required.")
else:
st.success(f"Saved profile for {username}.")Forms are useful for searches, grouped filters, expensive calculations and multi-field submissions because widgets inside them do not trigger the operation independently.
Arrange the page
st.sidebar.header("Filters")
show_details = st.sidebar.checkbox("Show details", value=True)
left, right = st.columns(2)
with left:
st.metric("Revenue", "$125,000")
with right:
st.metric("Orders", "2,480", delta="8.4%")
tab1, tab2 = st.tabs(["Overview", "Raw data"])
with tab1:
st.write("Summary content goes here.")
with tab2:
st.write("Detailed content goes here.")
if show_details:
with st.expander("How this was calculated"):
st.write("Calculation notes.")Columns, tabs and expanders improve usability; they do not create independent routes or execution contexts. See the layout API.
Display data and charts
For tabular output, use st.dataframe(df) for an interactive table and st.table(df.head()) for a static one. Built-in chart methods include:
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st.line_chart(df)
st.bar_chart(df)
st.area_chart(df)
st.scatter_chart(df)
st.map(df)Plotly, Altair, Matplotlib, PyDeck and Graphviz can provide richer visualizations. Their event handling, browser behavior and deployment requirements differ, so test the chosen library in the target environment. The API reference lists current options.
Control reruns with caching and session state
Cache computed data
import streamlit as st
import pandas as pd
@st.cache_data
def load_data(path):
return pd.read_csv(path)Use st.cache_data for serializable results such as transformed data, API responses, query results and computed values. Cached results can become stale, consume memory or require invalidation, so include all relevant function arguments and use a suitable TTL when freshness matters.
Cache reusable resources
import streamlit as st
@st.cache_resource
def load_model():
return create_model()st.cache_resource is intended for expensive-to-initialize reusable objects such as models, database connections, clients and tokenizers. Consider whether the object is mutable or safe to share, and account for memory, concurrency and cold-start time. Neither decorator replaces a database, queue or durable state system. See caching concepts.
Preserve values for one session
import streamlit as st
if "count" not in st.session_state:
st.session_state.count = 0
if st.button("Increment"):
st.session_state.count += 1
st.write(f"Count: {st.session_state.count}")Session state persists across reruns within one browser session. It is not shared automatically with other users and is not durable across session loss, process restarts or deployments. Store durable records externally. The session-state API documents supported patterns.
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Use callbacks deliberately
import streamlit as st
def reset():
st.session_state.name = ""
if "name" not in st.session_state:
st.session_state.name = ""
st.text_input("Name", key="name")
st.button("Reset", on_click=reset)
st.write("Current value:", st.session_state.name)The callback runs before the subsequent full rerun. This ordering is useful for reset buttons, wizard steps and chat histories.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Organize a multipage app
my_app/
├── streamlit_app.py
└── pages/
├── 1_Overview.py
└── 2_Data.pystreamlit run streamlit_app.pyThe main script is the entry point. Files in pages/ become pages, and numeric prefixes can influence their display order. Put shared functions in deliberate utility modules and plan how session state is shared between pages. For more navigation control, verify the modern navigation APIs against the documentation for your installed version. See multipage tutorials.
Keep secrets out of source code
For local development, create .streamlit/secrets.toml:
api_key = "replace-me"import streamlit as st
api_key = st.secrets["api_key"]- Add
.streamlit/secrets.tomlto.gitignore. - Use the host’s secret-management interface after deployment.
- Never print credentials in logs or expose private keys to the browser.
- Separate development, staging and production credentials.
- Rotate a key immediately if it is committed publicly.
See secrets management.
Connect an API or database safely
import streamlit as st
import requests
@st.cache_data(ttl=300)
def get_data():
response = requests.get(
"https://api.example.com/data",
timeout=20,
)
response.raise_for_status()
return response.json()
try:
st.json(get_data())
except requests.RequestException as exc:
st.error(f"Could not load data: {exc}")Set timeouts, check non-success responses and cache only when five-minute-old data is acceptable. Add retries or a background job for unreliable or slow services. Keep credentials in secrets, and consider a dedicated data-access layer instead of embedding all SQL and network logic in page scripts.
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Deploy to Streamlit Community Cloud
- Put the application in a GitHub repository.
- Add a
requirements.txtfile, for example:
streamlit
pandas- Sign in to Streamlit Community Cloud and choose Deploy an app.
- Select the repository and the correct entry-point file.
- Configure secrets in the deployment interface.
- Inspect logs when startup or runtime errors occur.
For reproducibility, pin versions after testing:
streamlit==<tested-version>
pandas==<tested-version>Streamlit describes Community Cloud as a free, GitHub-connected platform that handles containerization; hosting, databases, APIs and other infrastructure can still cost money. Its published resource figures (dated February 2024) were approximately 0.078–2 CPU cores, 690 MB–2.7 GB memory and up to 50 GB storage, but the documentation says limits can change. Treat it as convenient managed hosting, not an unlimited production guarantee. See the overview, deployment steps and resource guidance.
Deployment troubleshooting
| Symptom | Likely cause | Fix |
|---|---|---|
ModuleNotFoundError |
Dependency absent remotely | Add it to requirements.txt and redeploy. |
| Wrong entry point | Deployment targets the wrong file | Select the actual app script. |
| Works locally, fails remotely | Missing secrets, system dependency, relative path or environment assumption | Check logs, configure secrets and use project-relative paths. |
| Slow startup | Model download or heavy initialization at import time | Use st.cache_resource, precompute where possible and reduce cold-start work. |
| Resource error | CPU, memory, storage or runtime limit | Reduce data/model size or move to infrastructure with suitable capacity. |
| Blank or broken page | Uncaught exception | Read deployment logs and reproduce with the same dependency versions. |
| Missing data file | File was not committed or path depends on the working directory | Commit required assets and resolve paths from __file__. |
| Private data exposed | Repository or app access is too broad | Review access controls; a hidden URL is not authentication. |
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent
data_path = BASE_DIR / "data" / "sales.csv"Private apps can restrict viewers through Community Cloud’s documented sign-in and sharing controls; verify current options in the sharing documentation.
Security, scale and durability checks
- Streamlit does not automatically provide authentication, authorization, row-level security, rate limiting, audit logging, SQL-injection protection or tenant isolation.
- Session state and caches are not durable business storage.
- Filter, aggregate or paginate large datasets instead of rendering everything.
- Long inference, file processing and API calls can block the interface; use progress indicators, caching, precomputation, queues or a separate service where appropriate.
- Do not cache user-specific secrets, hidden state or mutable objects that users could accidentally share.
- Model initialization should account for memory, concurrency and cold starts.
When Streamlit is the right choice—and when it is not
| Requirement | Good direction | Why |
|---|---|---|
| Python-first dashboard or internal tool | Streamlit | Fast development with built-in data widgets and charts. |
| Public machine-learning demo | Streamlit or Hugging Face Spaces | Convenient model interfaces; review hardware, privacy and usage limits. |
| Snowflake-centered governed data app | Streamlit in Snowflake | Integrated access, with Snowflake runtime and warehouse billing; see documentation. |
| API-first service or background jobs | FastAPI | Clear service separation and API-oriented architecture. |
| Conventional full Python application | Django | Built-in structure for models, authentication, administration and routing. |
| Highly customized frontend | React or Next.js with a backend | More control over client state, design systems, routing and interaction. |
| Alternative Python dashboards | Panel or Dash | Different component and plotting ecosystems may better match the project. |
| More hosting control | Render or a cloud provider | More networking and operational choices, with more configuration responsibility. |
Choose Streamlit when built-in widgets are sufficient, server-side reruns are acceptable and the audience is focused. Reconsider it for pixel-perfect marketing sites, native mobile experiences, complex permissions, sophisticated transactional workflows, very high concurrency or strict frontend accessibility and design-system requirements.
Quick Recap
Complete project checklist
- Create and activate a virtual environment.
- Install and verify Streamlit locally.
- Run the app with
streamlit run. - Explain reruns before adding complex interactions.
- Use forms for grouped submissions and session state for per-session workflow values.
- Apply
st.cache_dataandst.cache_resourceaccording to object type and freshness needs. - Keep durable records in an external store.
- Put secrets in configuration, never source code.
- Pin tested dependencies for deployment.
- Review logs, resource limits, privacy and access controls before sharing.
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