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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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depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-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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depth_image = output["depth"].to_pil() |
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tensor = output["predicted_depth"] |
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tensor_data = { |
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"dims": tensor.dims, |
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"type": tensor.type, |
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"data": tensor.data, |
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"size": tensor.size, |
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} |
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return depth_image, tensor_data |
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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.Image(label="Depth Image"), |
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gr.JSON(label="Tensor"), |
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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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</html> |
