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transformers/docs/source/en/pipeline_gradio.md

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+
+# Machine learning apps
+
+[Gradio](https://www.gradio.app/), a fast and easy library for building and sharing machine learning apps, is integrated with [`Pipeline`] to quickly create a simple interface for inference.
+
+Before you begin, make sure Gradio is installed.
+
+```py
+!pip install gradio
+```
+
+Create a pipeline for your task, and then pass it to Gradio's [Interface.from_pipeline](https://www.gradio.app/docs/gradio/interface#interface-from_pipeline) function to create the interface. Gradio automatically determines the appropriate input and output components for a [`Pipeline`].
+
+Add [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to create a web server and start up the app.
+
+```py
+from transformers import pipeline
+import gradio as gr
+
+pipeline = pipeline("image-classification", model="google/vit-base-patch16-224")
+gr.Interface.from_pipeline(pipeline).launch()
+```
+
+The web app runs on a local server by default. To share the app with other users, set `share=True` in [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to generate a temporary public link. For a more permanent solution, host the app on Hugging Face [Spaces](https://hf.co/spaces).
+
+```py
+gr.Interface.from_pipeline(pipeline).launch(share=True)
+```
+
+The Space below is created with the code above and hosted on Spaces.
+
+<iframe
+	src="https://stevhliu-gradio-pipeline-demo.hf.space"
+	frameborder="0"
+	width="850"
+	height="850"
+></iframe>