paper_version
Browse filesremove chuncks
add judge selection
more description
app.py
CHANGED
@@ -11,74 +11,89 @@ 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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# https://www.gradio.app/guides/controlling-layout
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def greet(opinion,
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judges_l = (
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df[(df["year_filed"] == year) & (df["category"] != "per_curiam")]
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.author_name.unique()
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.tolist()
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)
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if year == 1994:
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judges_l.extend(["Justice Breyer", "Justice Kennedy"])
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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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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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def set_input(drop):
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return drop[0], drop[1], gr.Slider(visible=
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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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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textboxes = []
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for i in range(max_textboxes):
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with gr.Row():
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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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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,
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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] + textboxes,
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)
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if __name__ == "__main__":
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demo.launch()
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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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unique_judges_by_year = (
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df[df.author_name != "per_curiam"].groupby("year_filed")["author_name"].unique()
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)
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additional_judges = ["Justice Breyer", "Justice Kennedy"]
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unique_judges_by_year[1994] = list(unique_judges_by_year[1994]) + additional_judges
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# https://www.gradio.app/guides/controlling-layout
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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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unique_judges_by_year[year].tolist(),
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value=unique_judges_by_year[year].tolist(),
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label="Select Judges",
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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():
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drop = gr.Dropdown(
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choices=sorted(choices),
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label="Per Curiam Opinions",
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info="Select a per curiam opinion to use as input",
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)
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year = gr.Slider(
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1994,
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2020,
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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 pass the opinion below",
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)
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exc_judg = gr.CheckboxGroup(
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unique_judges_by_year[year.value],
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value=unique_judges_by_year[year.value],
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label="Select Judges",
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info="Select judges 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 or select from dropdown"
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)
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with gr.Column():
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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(debug=True)
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