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Delete embedded Lite apps in the doc (#8232)

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Yuichiro Tachibana (Tsuchiya) 2024-05-10 15:50:38 +09:00 committed by GitHub
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guides/08_gradio-clients-and-lite/06_gradio-lite-and-transformers-js.md
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@ -49,29 +49,13 @@ transformers-js-py
</html>
```
Here is a running example of the code above (after the app has loaded, you could disconnect your Internet connection and the app will still work since its running entirely in your browser):
<gradio-lite shared-worker>
import gradio as gr
from transformers_js_py import pipeline
pipe = await pipeline('sentiment-analysis')
demo = gr.Interface.from_pipeline(pipe)
demo.launch()
<gradio-requirements>
transformers-js-py
</gradio-requirements>
</gradio-lite>
And you you can open your HTML file in a browser to see the Gradio app running!
You can open your HTML file in a browser to see the Gradio app running!
The Python code inside the `<gradio-lite>` tag is the Gradio application code. For more details on this part, please refer to [this article](./gradio-lite).
The `<gradio-requirements>` tag is used to specify packages to be installed in addition to Gradio-Lite and its dependencies. In this case, we are using Transformers.js.py (`transformers-js-py`), so it is specified here.
Let's break down the code:
`pipe = await pipeline('sentiment-analysis')` creates a Transformers.js pipeline.
In this example, we create a sentiment analysis pipeline.
For more information on the available pipeline types and usage, please refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).
@ -87,39 +71,9 @@ You can modify the line `pipe = await pipeline('sentiment-analysis')` in the sam
For example, if you change it to `pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')`, you can test the same sentiment analysis task but with a different model. The second argument of the `pipeline` function specifies the model name.
If it's not specified like in the first example, the default model is used. For more details on these specs, refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).
<gradio-lite shared-worker>
import gradio as gr
from transformers_js_py import pipeline
pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')
demo = gr.Interface.from_pipeline(pipe)
demo.launch()
<gradio-requirements>
transformers-js-py
</gradio-requirements>
</gradio-lite>
As another example, changing it to `pipe = await pipeline('image-classification')` creates a pipeline for image classification instead of sentiment analysis.
In this case, the interface created with `demo = gr.Interface.from_pipeline(pipe)` will have a UI for uploading an image and displaying the classification result. The `gr.Interface.from_pipeline` function automatically creates an appropriate UI based on the type of pipeline.
<gradio-lite shared-worker>
import gradio as gr
from transformers_js_py import pipeline
pipe = await pipeline('image-classification')
demo = gr.Interface.from_pipeline(pipe)
demo.launch()
<gradio-requirements>
transformers-js-py
</gradio-requirements>
</gradio-lite>
<br>
**Note**: If you use an audio pipeline, such as `automatic-speech-recognition`, you will need to put `transformers-js-py[audio]` in your `<gradio-requirements>` as there are additional requirements needed to process audio files.
@ -164,28 +118,6 @@ transformers-js-py
In this example, we modified the code to construct the Gradio user interface manually so that we could output the result as JSON.
<gradio-lite shared-worker>
import gradio as gr
from transformers_js_py import pipeline
pipe = await pipeline('sentiment-analysis')
async def fn(text):
result = await pipe(text)
return result
demo = gr.Interface(
fn=fn,
inputs=gr.Textbox(),
outputs=gr.JSON(),
)
demo.launch()
<gradio-requirements>
transformers-js-py
</gradio-requirements>
</gradio-lite>
## Conclusion
By combining Gradio-Lite and Transformers.js (and Transformers.js.py), you can create serverless machine learning applications that run entirely in the browser.