Gradio vs Streamlit: Which to Use

Both turn a Python script into a shareable web app with no frontend code — here’s the real difference in what each is actually built for

For a quick demo of a single ML model, Gradio is the faster path; for a multi-page dashboard or internal tool, Streamlit is the better fit. Both eliminate frontend code entirely, but they’re built around different shapes of app.

At a glance

What Gradio is built for

Gradio wraps a single function’s inputs and outputs — image in, prediction out; text in, generated text out — with components purpose-built for ML I/O: image upload/display, audio recording, file handling, chat interfaces. A gr.Interface() call around a predict() function is often a complete, working demo in under 10 lines. It’s backed by Hugging Face and is the default way models get an interactive demo on Hugging Face Spaces.

What Streamlit is built for

Streamlit is a more general app-building framework. Layout primitives — sidebars, columns, tabs, session state — and native multi-page app support make it better suited to dashboards, internal tools, and apps with more than one screen or a model-agnostic structure, not just a single prediction interface.

Where they overlap

Both eliminate the need to write HTML/CSS/JS, both re-run the script top-to-bottom on interaction (with their own caching mechanisms to avoid redundant recomputation), and both deploy free on their respective platforms — Gradio to Hugging Face Spaces, Streamlit to Streamlit Community Cloud. For a genuinely simple single-model demo, either can technically do the job; the difference shows up as the app grows.

Chat apps and quick sharing

Both libraries now cover the most common modern request, a chat front end for a language model. Gradio’s gr.ChatInterface is aimed at turning a chat function into a UI; Streamlit gives you st.chat_message and st.chat_input as building blocks, which suits a chat that sits inside a bigger app. For showing a colleague a demo without deploying anything, Gradio’s launch(share=True) returns a public link to the app running on your machine; for a permanent link its own docs point to Hugging Face Spaces.

The practical pick

• Demoing one ML model’s inputs/outputs, publishing to Hugging Face Spaces → Gradio.
• Building a multi-page dashboard, internal tool, or anything beyond one model’s interface → Streamlit.
• Already have a Streamlit app and just need to add a model demo page → Streamlit’s own components are usually enough; no need to introduce Gradio alongside it.