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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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demo = gr.Interface(
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)
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demo.launch()
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</gradio-file>
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<gradio-file name="
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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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depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-large-hf');
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async def estimate(input_image):
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output = await depth_estimator(as_url(input_image))
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# print("blob:", output.depth.toBlob())
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return output
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demo = gr.Interface(
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fn=estimate,
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inputs=[
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gr.Image(type="filepath")
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],
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outputs=[
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gr.JSON(),
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],
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examples=[
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["bread_small.png"]
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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="bread_small.png" url="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/bread_small.png" />
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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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