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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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from datasets import load_dataset |
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import json |
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pipe = pipeline(model="raminass/SCOTUS_AI_14", top_k=14, padding=True, truncation=True) |
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all = load_dataset("raminass/full_opinions_1994_2020") |
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df = pd.DataFrame(all["train"]) |
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choices = [] |
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for index, row in df[df.category == "per_curiam"].iterrows(): |
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if len(row["text"]) > 1000: |
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choices.append((f"""{row["case_name"]}""", [row["text"], row["year_filed"]])) |
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with open("j_year.json", "r") as j: |
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judges_by_year = json.loads(j.read()) |
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judges_by_year = {int(k): v for k, v in judges_by_year.items()} |
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def greet(opinion, judges_l): |
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chunks = chunk_data(remove_citations(opinion))["text"].to_list() |
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result = average_text(chunks, pipe, judges_l) |
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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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def update_year(year): |
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return gr.CheckboxGroup( |
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judges_by_year[year], |
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value=judges_by_year[year], |
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label="Select Justices", |
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) |
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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 Per Curiam Opinions", |
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info="Select a per curiam opinion from the dropdown menu and press the Predict Button", |
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) |
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year = gr.Slider( |
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1994, |
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2023, |
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step=1, |
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label="Year", |
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info="Select the year of the opinion if you manually paste the opinion below", |
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) |
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exc_judg = gr.CheckboxGroup( |
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judges_by_year[year.value], |
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value=judges_by_year[year.value], |
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label="Select Justices", |
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info="Select justices to consider in prediction", |
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) |
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opinion = gr.Textbox( |
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label="Opinion", 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 = gr.outputs.Label( |
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num_top_classes=9, label="Predicted author of opinion" |
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) |
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year.release( |
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update_year, |
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inputs=[year], |
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outputs=[exc_judg], |
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) |
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year.change( |
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update_year, |
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inputs=[year], |
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outputs=[exc_judg], |
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) |
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drop.select(set_input, inputs=drop, outputs=[opinion, year, year]) |
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greet_btn.click( |
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fn=greet, |
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inputs=[opinion, exc_judg], |
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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, year, year, drop, op_level], |
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) |
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if __name__ == "__main__": |
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demo.launch( |
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auth=("sc2024", "sc2024"), |
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auth_message="To request access, please email [email protected]", |
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) |
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