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<!DOCTYPE html> |
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<html> |
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<head> |
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<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto&display=swap" > |
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<style> |
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body { |
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font-family: 'Roboto', sans-serif; |
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font-size: 16px; |
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} |
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.logo { |
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height: 1em; |
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vertical-align: middle; |
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margin-bottom: 0.1em; |
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} |
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</style> |
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<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/[email protected]/dist/lite.js"></script> |
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/[email protected]/dist/lite.css" /> |
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</head> |
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<body> |
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<h2> |
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<img src="lite-logo.png" alt="logo" class="logo"> |
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Gradio-lite (Gradio running entirely in your browser!) |
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</h2> |
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<p>Try it out! Once the Gradio app loads (can take 10-15 seconds), disconnect your Wifi and the machine learning model will still work!</p> |
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<gradio-lite> |
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<gradio-requirements> |
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transformers_js_py |
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</gradio-requirements> |
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<gradio-file name="app.py" entrypoint> |
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from transformers_js import import_transformers_js |
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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('sentiment-analysis') |
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async def classify(text): |
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return await pipe(text) |
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demo = gr.Interface(classify, "textbox", "json", examples=["It's a happy day in the neighborhood", "I'm an evil penguin", "It wasn't a bad film."]) |
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demo.launch() |
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</gradio-file> |
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</gradio-lite> |
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</body> |
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</html> |
