Gradio ML Showcase

No. Gradio's Python function runs directly behind the generated interface, so there's no separate API or database layer to build unless the model itself needs one.

Free CPU Spaces are enough for lightweight models or ones already exported to a fast runtime. A paid GPU Space is worth it once inference on CPU takes more than a couple of seconds per request.

Gradio when the app is the model: an input goes in, a prediction comes out, and the interface exists so people can try it. Its components are built for that loop (image and audio inputs, chat windows, labelled outputs), every app gets an API for free, and Hugging Face Spaces hosts it with no setup. Streamlit, which the Python Dashboard Starter stack is built on, suits apps where the data is the point: dashboards, filters, charts over a dataset, multi-page tools. A model demo with a few supporting charts fits either; an analytics tool with a model inside it is Streamlit's territory.

Because Gradio queues requests and, by default, runs each event handler for one request at a time, so a model that takes three seconds per prediction makes the tenth visitor wait about half a minute. If the model can safely handle parallel calls, raise the event's concurrency limit. If it runs on a GPU, batching is often the better fix: mark the function as batched with a maximum batch size, and Gradio groups waiting requests into one model call. On free CPU hardware neither change creates more compute, so a popular demo eventually needs faster hardware.

Yes, in a few ways depending on who the audience is. A private Space is visible only to you or your organization on Hugging Face, which suits internal model reviews. Gradio's launch settings accept a username and password for simple shared access when hosting it yourself, and Spaces support signing in with Hugging Face accounts when you need to know who each user is. Keep in mind that the auto-generated API follows the same access rules as the interface, so a public Space means a public API too.