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Update index.html

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  1. index.html +30 -39
index.html CHANGED
@@ -1,57 +1,48 @@
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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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-
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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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-
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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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- import gradio as gr
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- from filters import as_gray
 
 
 
 
 
 
 
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- def process(input_image):
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- output_image = as_gray(input_image)
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- return output_image
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- demo = gr.Interface(
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- process,
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- "image",
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- "image",
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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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- <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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- # 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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  <!DOCTYPE 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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+
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+ <gradio-file name="app.py" entrypoint>
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+ from transformers_js_py import import_transformers_js
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+ import gradio as gr
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+ import numpy as np
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+ transformers_js = await import_transformers_js("3.0.2")
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+ pipeline = transformers_js.pipeline
 
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+ synthesizer = await pipeline(
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+ 'text-to-speech',
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+ 'Xenova/speecht5_tts',
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+ { "quantized": False }
 
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  )
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+ speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
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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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+
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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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+
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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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+
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+ </gradio-lite>
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  </body>
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  </html>