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import gradio as gr |
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from gradio.components import Label, Textbox |
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from transformers import pipeline |
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from utils import * |
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from datasets import load_dataset |
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import json |
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pipe = pipeline(model="raminass/british", top_k=2, padding=True, truncation=True) |
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df = pd.read_csv("data.csv", sep="\t") |
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choices = [] |
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for index, row in df.iterrows(): |
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choices.append((f"""{row["case"]}""", [row["text"], row["author"]])) |
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def greet(opinion): |
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opinion = opinion.replace("(", "").replace(")", "") |
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chunks = chunk_data(opinion)["text"].to_list() |
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result = average_text(chunks, pipe) |
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return result[0] |
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def set_input(drop): |
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return drop[0], drop[1], gr.Slider(visible=True) |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(scale=2): |
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drop = gr.Dropdown( |
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choices=sorted(choices), |
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label="List of Cases", |
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info="Select a case from the dropdown menu and press the Predict Button", |
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) |
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opinion = gr.Textbox( |
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label="Opinion", |
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info="Paste opinion text here and press the Predict Button", |
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) |
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with gr.Column(scale=1): |
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with gr.Row(): |
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clear_btn = gr.Button("Clear") |
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greet_btn = gr.Button("Predict") |
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op_level = Label(num_top_classes=9, label="Predicted author of opinion") |
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drop.select(set_input, inputs=drop, outputs=[opinion]) |
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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], |
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) |
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clear_btn.click( |
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fn=lambda: [None, 1994, gr.Slider(visible=True), None, None], |
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outputs=[opinion, drop, op_level], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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