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index.html
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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-file name="app.py" entrypoint>
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import gradio as gr
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output_image = as_gray(input_image)
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return output_image
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"
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examples=["lion.jpg", "logo.png"],
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)
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demo.launch()
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</gradio-file>
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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-requirements>
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# Same syntax as requirements.txt
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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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</html>
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<html>
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<head>
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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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</head>
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<body>
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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 pipeline
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import gradio as gr
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import numpy as np
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speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
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synthesizer = await pipeline(
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'text-to-speech',
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'Xenova/mms-tts-ara',
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{ "quantized": False }
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)
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async def synthesize(text):
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out = await synthesizer(text, { "speaker_embeddings": speaker_embeddings });
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audio_data_memory_view = out["audio"]
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sampling_rate = out["sampling_rate"]
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audio_data = np.frombuffer(audio_data_memory_view, dtype=np.float32)
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audio_data_16bit = (audio_data * 32767).astype(np.int16)
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return sampling_rate, audio_data_16bit
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demo = gr.Interface(synthesize, "textbox", "audio")
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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>
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