53 lines
2.2 KiB
Markdown
53 lines
2.2 KiB
Markdown
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# Machine learning apps
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[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.
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Before you begin, make sure Gradio is installed.
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```py
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!pip install gradio
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```
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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`].
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Add [launch](https://www.gradio.app/main/docs/gradio/blocks#blocks-launch) to create a web server and start up the app.
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```py
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from transformers import pipeline
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import gradio as gr
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pipeline = pipeline("image-classification", model="google/vit-base-patch16-224")
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gr.Interface.from_pipeline(pipeline).launch()
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```
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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).
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```py
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gr.Interface.from_pipeline(pipeline).launch(share=True)
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```
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The Space below is created with the code above and hosted on Spaces.
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<iframe
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src="https://stevhliu-gradio-pipeline-demo.hf.space"
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frameborder="0"
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width="850"
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height="850"
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></iframe>
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