SCOTUS_AI_15_CURCUIT

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Rami Nasser commited on Dec 31, 2023

Commit

8fbc997

•

1 Parent(s): dd034f2

judge list new model

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Files changed (3) hide show

app.py +11 -12
j_year.json +331 -0
utils/__init__.py +0 -16

app.py CHANGED Viewed

@@ -2,8 +2,9 @@ import gradio as gr
 from transformers import pipeline
 from utils import *
 from datasets import load_dataset
-pipe = pipeline(model="raminass/scotus-v10", top_k=13, padding=True, truncation=True)
 all = load_dataset("raminass/full_opinions_1994_2020")
 df = pd.DataFrame(all["train"])
 choices = []
@@ -11,11 +12,9 @@ for index, row in df[df.category == "per_curiam"].iterrows():
     if len(row["text"]) > 1000:
         choices.append((f"""{row["case_name"]}""", [row["text"], row["year_filed"]]))
-unique_judges_by_year = (
-    df[df.author_name != "per_curiam"].groupby("year_filed")["author_name"].unique()
-)
-additional_judges = ["Justice Breyer", "Justice Kennedy"]
-unique_judges_by_year[1994] = list(unique_judges_by_year[1994]) + additional_judges
 # https://www.gradio.app/guides/controlling-layout
@@ -32,8 +31,8 @@ def set_input(drop):
 def update_year(year):
     return gr.CheckboxGroup(
-        unique_judges_by_year[year].tolist(),
-        value=unique_judges_by_year[year].tolist(),
         label="Select Judges",
     )
@@ -48,14 +47,14 @@ with gr.Blocks() as demo:
             )
             year = gr.Slider(
                 1994,
-                2020,
                 step=1,
                 label="Year",
                 info="Select the year of the opinion if you manually pass the opinion below",
             )
             exc_judg = gr.CheckboxGroup(
-                unique_judges_by_year[year.value],
-                value=unique_judges_by_year[year.value],
                 label="Select Judges",
                 info="Select judges to consider in prediction",
             )
@@ -96,4 +95,4 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
-    demo.launch(debug=True)

 from transformers import pipeline
 from utils import *
 from datasets import load_dataset
+import json
+pipe = pipeline(model="raminass/94-23", top_k=17, padding=True, truncation=True)
 all = load_dataset("raminass/full_opinions_1994_2020")
 df = pd.DataFrame(all["train"])
 choices = []
     if len(row["text"]) > 1000:
         choices.append((f"""{row["case_name"]}""", [row["text"], row["year_filed"]]))
+with open("j_year.json", "r") as j:
+    judges_by_year = json.loads(j.read())
+judges_by_year = {int(k): v for k, v in judges_by_year.items()}
 # https://www.gradio.app/guides/controlling-layout
 def update_year(year):
     return gr.CheckboxGroup(
+        judges_by_year[year],
+        value=judges_by_year[year],
         label="Select Judges",
     )
             )
             year = gr.Slider(
                 1994,
+                2023,
                 step=1,
                 label="Year",
                 info="Select the year of the opinion if you manually pass the opinion below",
             )
             exc_judg = gr.CheckboxGroup(
+                judges_by_year[year.value],
+                value=judges_by_year[year.value],
                 label="Select Judges",
                 info="Select judges to consider in prediction",
             )
 if __name__ == "__main__":
+    demo.launch()

j_year.json ADDED Viewed

	@@ -0,0 +1,331 @@

+{
+    "1994": [
+        "Rehnquist",
+        "Stevens",
+        "Scalia",
+        "O'Connor",
+        "Ginsburg",
+        "Thomas",
+        "Souter",
+        "Breyer",
+        "Kennedy"
+    ],
+    "1995": [
+        "Scalia",
+        "Stevens",
+        "Ginsburg",
+        "Breyer",
+        "Souter",
+        "O'Connor",
+        "Thomas",
+        "Kennedy",
+        "Rehnquist"
+    ],
+    "1996": [
+        "Scalia",
+        "Stevens",
+        "Rehnquist",
+        "Souter",
+        "O'Connor",
+        "Kennedy",
+        "Breyer",
+        "Ginsburg",
+        "Thomas"
+    ],
+    "1997": [
+        "Ginsburg",
+        "Stevens",
+        "Souter",
+        "Thomas",
+        "Rehnquist",
+        "Breyer",
+        "Scalia",
+        "O'Connor",
+        "Kennedy"
+    ],
+    "1998": [
+        "Scalia",
+        "Thomas",
+        "Stevens",
+        "Breyer",
+        "Rehnquist",
+        "Souter",
+        "Kennedy",
+        "O'Connor",
+        "Ginsburg"
+    ],
+    "1999": [
+        "Breyer",
+        "Rehnquist",
+        "Souter",
+        "Thomas",
+        "Stevens",
+        "Kennedy",
+        "Scalia",
+        "Ginsburg",
+        "O'Connor"
+    ],
+    "2000": [
+        "O'Connor",
+        "Breyer",
+        "Ginsburg",
+        "Kennedy",
+        "Rehnquist",
+        "Scalia",
+        "Souter",
+        "Stevens",
+        "Thomas"
+    ],
+    "2001": [
+        "Stevens",
+        "Kennedy",
+        "Thomas",
+        "Scalia",
+        "Breyer",
+        "Ginsburg",
+        "Rehnquist",
+        "O'Connor",
+        "Souter"
+    ],
+    "2002": [
+        "Rehnquist",
+        "Stevens",
+        "Thomas",
+        "Souter",
+        "Breyer",
+        "Ginsburg",
+        "Scalia",
+        "Kennedy",
+        "O'Connor"
+    ],
+    "2003": [
+        "Scalia",
+        "Souter",
+        "Kennedy",
+        "Stevens",
+        "Thomas",
+        "O'Connor",
+        "Breyer",
+        "Rehnquist",
+        "Ginsburg"
+    ],
+    "2004": [
+        "Ginsburg",
+        "Stevens",
+        "Kennedy",
+        "Thomas",
+        "Scalia",
+        "Breyer",
+        "Souter",
+        "Rehnquist",
+        "O'Connor"
+    ],
+    "2005": [
+        "Ginsburg",
+        "Thomas",
+        "Stevens",
+        "Breyer",
+        "Souter",
+        "Kennedy",
+        "Roberts",
+        "Scalia",
+        "O'Connor",
+        "Rehnquist"
+    ],
+    "2006": [
+        "Breyer",
+        "Stevens",
+        "Souter",
+        "Thomas",
+        "Ginsburg",
+        "Scalia",
+        "Alito",
+        "Kennedy",
+        "Roberts",
+        "O'Connor"
+    ],
+    "2007": [
+        "Ginsburg",
+        "Scalia",
+        "Thomas",
+        "Alito",
+        "Stevens",
+        "Kennedy",
+        "Breyer",
+        "Souter",
+        "Roberts"
+    ],
+    "2008": [
+        "Thomas",
+        "Breyer",
+        "Ginsburg",
+        "Kennedy",
+        "Roberts",
+        "Scalia",
+        "Stevens",
+        "Souter",
+        "Alito"
+    ],
+    "2009": [
+        "Scalia",
+        "Kennedy",
+        "Breyer",
+        "Ginsburg",
+        "Thomas",
+        "Stevens",
+        "Alito",
+        "Sotomayor",
+        "Roberts",
+        "Souter"
+    ],
+    "2010": [
+        "Breyer",
+        "Scalia",
+        "Kennedy",
+        "Stevens",
+        "Sotomayor",
+        "Alito",
+        "Ginsburg",
+        "Thomas",
+        "Roberts"
+    ],
+    "2011": [
+        "Kennedy",
+        "Breyer",
+        "Scalia",
+        "Thomas",
+        "Ginsburg",
+        "Sotomayor",
+        "Alito",
+        "Kagan",
+        "Roberts"
+    ],
+    "2012": [
+        "Kennedy",
+        "Ginsburg",
+        "Breyer",
+        "Roberts",
+        "Alito",
+        "Thomas",
+        "Sotomayor",
+        "Scalia",
+        "Kagan"
+    ],
+    "2013": [
+        "Ginsburg",
+        "Alito",
+        "Breyer",
+        "Kennedy",
+        "Scalia",
+        "Roberts",
+        "Thomas",
+        "Sotomayor",
+        "Kagan"
+    ],
+    "2014": [
+        "Roberts",
+        "Thomas",
+        "Breyer",
+        "Kagan",
+        "Ginsburg",
+        "Scalia",
+        "Sotomayor",
+        "Kennedy",
+        "Alito"
+    ],
+    "2015": [
+        "Breyer",
+        "Sotomayor",
+        "Roberts",
+        "Thomas",
+        "Alito",
+        "Scalia",
+        "Kagan",
+        "Ginsburg",
+        "Kennedy"
+    ],
+    "2016": [
+        "Ginsburg",
+        "Thomas",
+        "Roberts",
+        "Alito",
+        "Breyer",
+        "Sotomayor",
+        "Kagan",
+        "Kennedy",
+        "Scalia"
+    ],
+    "2017": [
+        "Ginsburg",
+        "Thomas",
+        "Sotomayor",
+        "Kagan",
+        "Kennedy",
+        "Alito",
+        "Breyer",
+        "Roberts"
+    ],
+    "2018": [
+        "Kennedy",
+        "Kagan",
+        "Ginsburg",
+        "Thomas",
+        "Breyer",
+        "Alito",
+        "Sotomayor",
+        "Roberts"
+    ],
+    "2019": [
+        "Thomas",
+        "Ginsburg",
+        "Alito",
+        "Breyer",
+        "Sotomayor",
+        "Kagan",
+        "Roberts"
+    ],
+    "2020": [
+        "Alito",
+        "Kagan",
+        "Breyer",
+        "Sotomayor",
+        "Ginsburg",
+        "Thomas",
+        "Roberts",
+        "Kavanaugh",
+        "Gorsuch"
+    ],
+    "2021": [
+        "Kagan",
+        "Breyer",
+        "Sotomayor",
+        "Gorsuch",
+        "Roberts",
+        "Thomas",
+        "Alito",
+        "Kavanaugh",
+        "Barrett"
+    ],
+    "2022": [
+        "Kagan",
+        "Breyer",
+        "Sotomayor",
+        "Gorsuch",
+        "Roberts",
+        "Thomas",
+        "Alito",
+        "Kavanaugh",
+        "Barrett"
+    ],
+    "2023": [
+        "Kagan",
+        "Jackson",
+        "Sotomayor",
+        "Gorsuch",
+        "Roberts",
+        "Thomas",
+        "Alito",
+        "Kavanaugh",
+        "Barrett"
+    ]
+}

utils/__init__.py CHANGED Viewed

@@ -37,19 +37,3 @@ def average_text(text, model, judges):
     sumary = {k: round(sum(v) / len(v), 2) for k, v in pred.items()}
     sumary = normaliz_dict(sumary)
     return dict(sorted(sumary.items(), key=lambda x: x[1], reverse=True)), new_res
-# def find_case_by_name(df, name):
-#     return display(
-#         HTML(
-#             df[df["case_name"].str.contains(name)]
-#             .iloc[:, :-1]
-#             .to_html(render_links=True, escape=False)
-#         )
-#     )
-# def head_df(df):
-#     return display(
-#         HTML(df.iloc[:, :-1].head().to_html(render_links=True, escape=False))
-#     )

     sumary = {k: round(sum(v) / len(v), 2) for k, v in pred.items()}
     sumary = normaliz_dict(sumary)
     return dict(sorted(sumary.items(), key=lambda x: x[1], reverse=True)), new_res