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<!DOCTYPE html> |
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<html> |
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<head> |
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<meta charset="utf-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1"> |
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<title>Gradio-Lite: Serverless Gradio Running Entirely in Your Browser</title> |
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<meta name="description" content="Gradio-Lite: Serverless Gradio Running Entirely in Your Browser"> |
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<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> |
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> |
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<style> |
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html, body { |
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margin: 0; |
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padding: 0; |
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height: 100%; |
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} |
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</style> |
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</head> |
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<body> |
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<gradio-lite> |
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<gradio-file name="app.py" entrypoint> |
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from transformers_js import import_transformers_js, as_url |
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import gradio as gr |
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transformers = await import_transformers_js() |
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pipeline = transformers.pipeline |
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pipe = await pipeline('object-detection', "Xenova/yolos-tiny") |
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async def detect(input_image): |
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result = await pipe(as_url(input_image)) |
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gradio_labels = [ |
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# List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]] |
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( |
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( |
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int(item["box"]["xmin"]), |
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int(item["box"]["ymin"]), |
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int(item["box"]["xmax"]), |
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int(item["box"]["ymax"]), |
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), |
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item["label"], |
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) |
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for item in result |
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] |
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annotated_image_data = input_image, gradio_labels |
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return annotated_image_data, result |
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demo = gr.Interface( |
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detect, |
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gr.Image(type="filepath"), |
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[ |
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gr.AnnotatedImage(), |
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gr.JSON(), |
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], |
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examples=[ |
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["cats.jpg"] |
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] |
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) |
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demo.launch() |
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</gradio-file> |
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<gradio-file name="cats.jpg" url="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg" /> |
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<gradio-requirements> |
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transformers_js_py |
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</gradio-requirements> |
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</gradio-lite> |
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</body> |
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</html> |
