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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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import gradio as gr |
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from transformers_js_py import pipeline |
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from filters import convert |
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# 모델과 tokenizer 파일 경로 |
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model_path = 'model/' |
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tokenizer_path = 'tokenizer.json' |
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# 모델 및 tokenizer 로드 |
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pipe = await pipeline('sentiment-analysis', model_path, tokenizer=tokenizer_path) |
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async def predict(text): |
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result = await pipe(text) |
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return result |
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demo = gr.Interface( |
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fn=predict, |
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inputs="text", |
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outputs="text", |
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title="KoELECTRA Sentiment Analysis" |
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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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def convert(input_data): |
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# Initialize the output list |
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result_labels = [] |
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# Iterate over each item in the input data |
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for item in input_data: |
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# Extract the label |
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label = item['label'] |
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# Extract the bounding box coordinates |
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xmin = item['box']['xmin'] |
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ymin = item['box']['ymin'] |
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xmax = item['box']['xmax'] |
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ymax = item['box']['ymax'] |
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# Convert coordinates into the required output format (list of coordinates) |
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coordinates = [xmin, ymin, xmax, ymax] |
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# Append the tuple of coordinates and label to the output list |
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result_labels.append((coordinates, label)) |
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# Return the output list |
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return result_labels |
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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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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> |
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