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Update the pipeline tutorial to include gradio.Interface.from_pipeline (#29684)

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* Update pipeline_tutorial.md to include gradio

* Update pipeline_tutorial.md

* Update docs/source/en/pipeline_tutorial.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/pipeline_tutorial.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/pipeline_tutorial.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/pipeline_tutorial.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update pipeline_tutorial.md

* Update docs/source/en/pipeline_tutorial.md

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Abubakar Abid
2024-03-18 09:17:41 -07:00
committed by GitHub
parent c852d4fba6
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@@ -314,4 +314,30 @@ pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"loa
output = pipe("This is a cool example!", do_sample=True, top_p=0.95) output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
``` ```
Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM! Note that you can replace the checkpoint with any Hugging Face model that supports large model loading, such as BLOOM.
## Creating web demos from pipelines with `gradio`
Pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:
```
pip install gradio
```
Then, you can create a web demo around an image classification pipeline (or any other pipeline) in a single line of code by calling Gradio's [`Interface.from_pipeline`](https://www.gradio.app/docs/interface#interface-from-pipeline) function to launch the pipeline. This creates an intuitive drag-and-drop interface in your browser:
```py
from transformers import pipeline
import gradio as gr
pipe = pipeline("image-classification", model="google/vit-base-patch16-224")
gr.Interface.from_pipeline(pipe).launch()
```
![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/panda-classification.png)
By default, the web demo runs on a local server. If you'd like to share it with others, you can generate a temporary public
link by setting `share=True` in `launch()`. You can also host your demo on [Hugging Face Spaces](https://huggingface.co/spaces) for a permanent link.