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import gradio as gr |
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from transformers import pipeline |
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from utils import * |
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pipe = pipeline(model="raminass/scotus-v10", top_k=13, padding=True, truncation=True) |
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max_textboxes = 100 |
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def greet(opinion): |
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chunks = chunk_data(remove_citations(opinion))["text"].to_list() |
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result = average_text(chunks, pipe) |
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k = len(chunks) |
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wrt_boxes = [] |
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for i in range(k): |
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wrt_boxes.append(gr.Textbox(chunks[i], visible=True)) |
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wrt_boxes.append(gr.Label(value=result[1][i], visible=True)) |
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return ( |
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[result[0]] |
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+ wrt_boxes |
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+ [gr.Textbox(visible=False), gr.Label(visible=False)] * (max_textboxes - k) |
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) |
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with gr.Blocks() as demo: |
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opinion = gr.Textbox(label="Opinion") |
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op_level = gr.outputs.Label(num_top_classes=13, label="Overall") |
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greet_btn = gr.Button("Predict") |
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textboxes = [] |
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for i in range(max_textboxes): |
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t = gr.Textbox(f"Textbox {i}", visible=False, label=f"Paragraph {i+1} Text") |
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par_level = gr.Label( |
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num_top_classes=5, label=f"Paragraph {i+1} Prediction", visible=False |
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) |
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textboxes.append(t) |
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textboxes.append(par_level) |
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greet_btn.click( |
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fn=greet, |
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inputs=opinion, |
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outputs=[op_level] + textboxes, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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