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Create app.py

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  1. app.py +24 -0
app.py ADDED
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+ from transformers import pipeline
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+ import gradio as gr
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+
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+ models = {
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+ 'devngho/ko_edu_classifier_v2_lemon-mint_LaBSE-EnKo-Nano-Preview-v0.3': pipeline("text-classification", model="devngho/ko_edu_classifier_v2_lemon-mint_LaBSE-EnKo-Nano-Preview-v0.3"),
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+ 'devngho/ko_edu_classifier_v2_LaBSE': pipeline("text-classification", model="devngho/ko_edu_classifier_v2_LaBSE")
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+ }
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+
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+ import gradio as gr
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+
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+ def evaluate_model(input_text):
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+ return [model(input_text) for model in models.values()]
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+
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+ # Gradio interface
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+ with gr.Blocks() as demo:
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+ input_text = gr.Textbox(label="Input Text")
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+ submit_button = gr.Button("Evaluate")
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+ output_scores = [gr.Number(f'Score by {name}') for name in models.keys()]
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+
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+ # Action to perform on button click
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+ submit_button.click(evaluate_model, inputs=input_text, outputs=output_scores)
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+
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+ # Launch the app
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+ demo.launch()