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Browse files- index.html +28 -20
index.html
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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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import gradio as gr
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def
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demo = gr.Interface(
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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="filters.py">
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from skimage.color import rgb2gray
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def as_gray(image):
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return rgb2gray(image)
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</gradio-file>
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<gradio-file name="lion.jpg" url="https://raw.githubusercontent.com/gradio-app/gradio/main/gradio/test_data/lion.jpg" />
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<gradio-file name="logo.png" url="https://raw.githubusercontent.com/gradio-app/gradio/main/guides/assets/logo.png" />
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<gradio-requirements>
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scikit-image
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</gradio-requirements>
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</gradio-lite>
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</body>
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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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)
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demo.launch()
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</gradio-file>
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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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