Streamlit
A Python framework for building data and ML apps as a script — widgets, charts, and layout from normal Python code.
Category
Visualization
Difficulty
Beginner
When to use
You want a quick internal tool or demo around a model and don't want to touch frontend code.
When not to use
You're building a customer-facing product or anything with custom interactions — a real frontend framework will serve you better.
Alternatives
Gradio Dash Panel Next.js
At a glance
| Field | Value |
|---|---|
| Category | Data / ML app framework |
| Difficulty | Beginner |
| When to use | Internal tools, demos, data exploration UIs |
| When not to use | Customer-facing products, complex interactions |
| Alternatives | Gradio, Dash, Panel |
What it is
A Streamlit app is a Python script that runs top-to-bottom every time a widget changes. Calls like st.button, st.slider, st.dataframe, and st.plotly_chart render widgets and output. Caching decorators (@st.cache_data, @st.cache_resource) avoid recomputing on every rerun.
When we reach for it at Ephizen
- Internal dashboards for a model’s predictions on incoming data.
- Demos to stakeholders of a new classifier or RAG prototype.
- Quick “let me click around my data” UIs that would otherwise be a one-off notebook.
- Sharing a model with non-technical teammates who don’t want to run code.
Getting started
import streamlit as st
from my_model import predict
st.title("Sentiment Demo")
text = st.text_area("Text", "I love this product")
if st.button("Classify"):
label, score = predict(text)
st.metric(label, f"{score:.2f}")streamlit run app.pyGotchas
- The rerun-the-script model is simple but awkward for stateful flows — use
st.session_stateexplicitly. - Not designed for heavy concurrent traffic. If you hit scaling problems, you’re probably past the point where Streamlit is the right tool.
- Theming and layout are limited. For pixel-perfect UIs, use a real frontend.