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Rami Nasser commited on
Commit
b27cd71
·
1 Parent(s): f3b8201

remove years

Browse files
Files changed (3) hide show
  1. app.py +17 -48
  2. j_year.json +0 -323
  3. utils/__init__.py +2 -3
app.py CHANGED
@@ -1,19 +1,23 @@
1
  import gradio as gr
 
 
2
  from transformers import pipeline
3
  from utils import *
4
  from datasets import load_dataset
5
  import json
6
 
7
  pipe = pipeline(model="raminass/british", top_k=2, padding=True, truncation=True)
8
- df = pd.read_csv('data.csv', sep='\t')
9
  choices = []
10
  for index, row in df.iterrows():
11
  choices.append((f"""{row["case"]}""", [row["text"], row["author"]]))
12
 
 
13
  # https://www.gradio.app/guides/controlling-layout
14
- def greet(opinion, judges_l):
15
- chunks = chunk_data(remove_citations(opinion))["text"].to_list()
16
- result = average_text(chunks, pipe, judges_l)
 
17
 
18
  return result[0]
19
 
@@ -22,73 +26,38 @@ def set_input(drop):
22
  return drop[0], drop[1], gr.Slider(visible=True)
23
 
24
 
25
- def update_year(year):
26
- return gr.CheckboxGroup(
27
- judges_by_year[year],
28
- value=judges_by_year[year],
29
- label="Select Justices",
30
- )
31
-
32
-
33
  with gr.Blocks() as demo:
34
  with gr.Row():
35
  with gr.Column(scale=2):
36
  drop = gr.Dropdown(
37
  choices=sorted(choices),
38
- label="List of Per Curiam Opinions",
39
- info="Select a per curiam opinion from the dropdown menu and press the Predict Button",
40
- )
41
- year = gr.Slider(
42
- 1994,
43
- 2023,
44
- step=1,
45
- label="Year",
46
- info="Select the year of the opinion if you manually paste the opinion below",
47
- )
48
- exc_judg = gr.CheckboxGroup(
49
- judges_by_year[year.value],
50
- value=judges_by_year[year.value],
51
- label="Select Justices",
52
- info="Select justices to consider in prediction",
53
  )
54
 
55
  opinion = gr.Textbox(
56
- label="Opinion", info="Paste opinion text here and press the Predict Button"
 
57
  )
58
  with gr.Column(scale=1):
59
  with gr.Row():
60
  clear_btn = gr.Button("Clear")
61
  greet_btn = gr.Button("Predict")
62
- op_level = gr.outputs.Label(
63
- num_top_classes=9, label="Predicted author of opinion"
64
- )
65
 
66
- year.release(
67
- update_year,
68
- inputs=[year],
69
- outputs=[exc_judg],
70
- )
71
- year.change(
72
- update_year,
73
- inputs=[year],
74
- outputs=[exc_judg],
75
- )
76
- drop.select(set_input, inputs=drop, outputs=[opinion, year, year])
77
 
78
  greet_btn.click(
79
  fn=greet,
80
- inputs=[opinion, exc_judg],
81
  outputs=[op_level],
82
  )
83
 
84
  clear_btn.click(
85
  fn=lambda: [None, 1994, gr.Slider(visible=True), None, None],
86
- outputs=[opinion, year, year, drop, op_level],
87
  )
88
 
89
 
90
  if __name__ == "__main__":
91
- demo.launch(
92
- auth=("sc2024", "sc2024"),
93
- auth_message="To request access, please email [email protected]",
94
- )
 
1
  import gradio as gr
2
+ from gradio.components import Label, Textbox
3
+
4
  from transformers import pipeline
5
  from utils import *
6
  from datasets import load_dataset
7
  import json
8
 
9
  pipe = pipeline(model="raminass/british", top_k=2, padding=True, truncation=True)
10
+ df = pd.read_csv("data.csv", sep="\t")
11
  choices = []
12
  for index, row in df.iterrows():
13
  choices.append((f"""{row["case"]}""", [row["text"], row["author"]]))
14
 
15
+
16
  # https://www.gradio.app/guides/controlling-layout
17
+ def greet(opinion):
18
+ opinion = opinion.replace("(", "").replace(")", "")
19
+ chunks = chunk_data(opinion)["text"].to_list()
20
+ result = average_text(chunks, pipe)
21
 
22
  return result[0]
23
 
 
26
  return drop[0], drop[1], gr.Slider(visible=True)
27
 
28
 
 
 
 
 
 
 
 
 
29
  with gr.Blocks() as demo:
30
  with gr.Row():
31
  with gr.Column(scale=2):
32
  drop = gr.Dropdown(
33
  choices=sorted(choices),
34
+ label="List of Cases",
35
+ info="Select a case from the dropdown menu and press the Predict Button",
 
 
 
 
 
 
 
 
 
 
 
 
 
36
  )
37
 
38
  opinion = gr.Textbox(
39
+ label="Opinion",
40
+ info="Paste opinion text here and press the Predict Button",
41
  )
42
  with gr.Column(scale=1):
43
  with gr.Row():
44
  clear_btn = gr.Button("Clear")
45
  greet_btn = gr.Button("Predict")
46
+ op_level = Label(num_top_classes=9, label="Predicted author of opinion")
 
 
47
 
48
+ drop.select(set_input, inputs=drop, outputs=[opinion])
 
 
 
 
 
 
 
 
 
 
49
 
50
  greet_btn.click(
51
  fn=greet,
52
+ inputs=[opinion],
53
  outputs=[op_level],
54
  )
55
 
56
  clear_btn.click(
57
  fn=lambda: [None, 1994, gr.Slider(visible=True), None, None],
58
+ outputs=[opinion, drop, op_level],
59
  )
60
 
61
 
62
  if __name__ == "__main__":
63
+ demo.launch()
 
 
 
j_year.json DELETED
@@ -1,323 +0,0 @@
1
- {
2
- "1994": [
3
- "Rehnquist",
4
- "Stevens",
5
- "Scalia",
6
- "O'Connor",
7
- "Ginsburg",
8
- "Thomas",
9
- "Souter",
10
- "Breyer",
11
- "Kennedy"
12
- ],
13
- "1995": [
14
- "Scalia",
15
- "Stevens",
16
- "Ginsburg",
17
- "Breyer",
18
- "Souter",
19
- "O'Connor",
20
- "Thomas",
21
- "Kennedy",
22
- "Rehnquist"
23
- ],
24
- "1996": [
25
- "Scalia",
26
- "Stevens",
27
- "Rehnquist",
28
- "Souter",
29
- "O'Connor",
30
- "Kennedy",
31
- "Breyer",
32
- "Ginsburg",
33
- "Thomas"
34
- ],
35
- "1997": [
36
- "Ginsburg",
37
- "Stevens",
38
- "Souter",
39
- "Thomas",
40
- "Rehnquist",
41
- "Breyer",
42
- "Scalia",
43
- "O'Connor",
44
- "Kennedy"
45
- ],
46
- "1998": [
47
- "Scalia",
48
- "Thomas",
49
- "Stevens",
50
- "Breyer",
51
- "Rehnquist",
52
- "Souter",
53
- "Kennedy",
54
- "O'Connor",
55
- "Ginsburg"
56
- ],
57
- "1999": [
58
- "Breyer",
59
- "Rehnquist",
60
- "Souter",
61
- "Thomas",
62
- "Stevens",
63
- "Kennedy",
64
- "Scalia",
65
- "Ginsburg",
66
- "O'Connor"
67
- ],
68
- "2000": [
69
- "O'Connor",
70
- "Breyer",
71
- "Ginsburg",
72
- "Kennedy",
73
- "Rehnquist",
74
- "Scalia",
75
- "Souter",
76
- "Stevens",
77
- "Thomas"
78
- ],
79
- "2001": [
80
- "Stevens",
81
- "Kennedy",
82
- "Thomas",
83
- "Scalia",
84
- "Breyer",
85
- "Ginsburg",
86
- "Rehnquist",
87
- "O'Connor",
88
- "Souter"
89
- ],
90
- "2002": [
91
- "Rehnquist",
92
- "Stevens",
93
- "Thomas",
94
- "Souter",
95
- "Breyer",
96
- "Ginsburg",
97
- "Scalia",
98
- "Kennedy",
99
- "O'Connor"
100
- ],
101
- "2003": [
102
- "Scalia",
103
- "Souter",
104
- "Kennedy",
105
- "Stevens",
106
- "Thomas",
107
- "O'Connor",
108
- "Breyer",
109
- "Rehnquist",
110
- "Ginsburg"
111
- ],
112
- "2004": [
113
- "Ginsburg",
114
- "Stevens",
115
- "Kennedy",
116
- "Thomas",
117
- "Scalia",
118
- "Breyer",
119
- "Souter",
120
- "Rehnquist",
121
- "O'Connor"
122
- ],
123
- "2005": [
124
- "Ginsburg",
125
- "Thomas",
126
- "Stevens",
127
- "Breyer",
128
- "Souter",
129
- "Kennedy",
130
- "Roberts",
131
- "Scalia",
132
- "O'Connor",
133
- "Rehnquist"
134
- ],
135
- "2006": [
136
- "Breyer",
137
- "Stevens",
138
- "Souter",
139
- "Thomas",
140
- "Ginsburg",
141
- "Scalia",
142
- "Alito",
143
- "Kennedy",
144
- "Roberts",
145
- "O'Connor"
146
- ],
147
- "2007": [
148
- "Ginsburg",
149
- "Scalia",
150
- "Thomas",
151
- "Alito",
152
- "Stevens",
153
- "Kennedy",
154
- "Breyer",
155
- "Souter",
156
- "Roberts"
157
- ],
158
- "2008": [
159
- "Thomas",
160
- "Breyer",
161
- "Ginsburg",
162
- "Kennedy",
163
- "Roberts",
164
- "Scalia",
165
- "Stevens",
166
- "Souter",
167
- "Alito"
168
- ],
169
- "2009": [
170
- "Scalia",
171
- "Kennedy",
172
- "Breyer",
173
- "Ginsburg",
174
- "Thomas",
175
- "Stevens",
176
- "Alito",
177
- "Sotomayor",
178
- "Roberts",
179
- "Souter"
180
- ],
181
- "2010": [
182
- "Breyer",
183
- "Scalia",
184
- "Kennedy",
185
- "Stevens",
186
- "Sotomayor",
187
- "Alito",
188
- "Ginsburg",
189
- "Thomas",
190
- "Roberts"
191
- ],
192
- "2011": [
193
- "Kennedy",
194
- "Breyer",
195
- "Scalia",
196
- "Thomas",
197
- "Ginsburg",
198
- "Sotomayor",
199
- "Alito",
200
- "Kagan",
201
- "Roberts"
202
- ],
203
- "2012": [
204
- "Kennedy",
205
- "Ginsburg",
206
- "Breyer",
207
- "Roberts",
208
- "Alito",
209
- "Thomas",
210
- "Sotomayor",
211
- "Scalia",
212
- "Kagan"
213
- ],
214
- "2013": [
215
- "Ginsburg",
216
- "Alito",
217
- "Breyer",
218
- "Kennedy",
219
- "Scalia",
220
- "Roberts",
221
- "Thomas",
222
- "Sotomayor",
223
- "Kagan"
224
- ],
225
- "2014": [
226
- "Roberts",
227
- "Thomas",
228
- "Breyer",
229
- "Kagan",
230
- "Ginsburg",
231
- "Scalia",
232
- "Sotomayor",
233
- "Kennedy",
234
- "Alito"
235
- ],
236
- "2015": [
237
- "Breyer",
238
- "Sotomayor",
239
- "Roberts",
240
- "Thomas",
241
- "Alito",
242
- "Scalia",
243
- "Kagan",
244
- "Ginsburg",
245
- "Kennedy"
246
- ],
247
- "2016": [
248
- "Ginsburg",
249
- "Thomas",
250
- "Roberts",
251
- "Alito",
252
- "Breyer",
253
- "Sotomayor",
254
- "Kagan",
255
- "Kennedy",
256
- "Scalia"
257
- ],
258
- "2017": [
259
- "Ginsburg",
260
- "Thomas",
261
- "Sotomayor",
262
- "Kagan",
263
- "Kennedy",
264
- "Alito",
265
- "Breyer",
266
- "Roberts"
267
- ],
268
- "2018": [
269
- "Kennedy",
270
- "Kagan",
271
- "Ginsburg",
272
- "Thomas",
273
- "Breyer",
274
- "Alito",
275
- "Sotomayor",
276
- "Roberts"
277
- ],
278
- "2019": [
279
- "Thomas",
280
- "Ginsburg",
281
- "Alito",
282
- "Breyer",
283
- "Sotomayor",
284
- "Kagan",
285
- "Roberts"
286
- ],
287
- "2020": [
288
- "Alito",
289
- "Kagan",
290
- "Breyer",
291
- "Sotomayor",
292
- "Ginsburg",
293
- "Thomas",
294
- "Roberts",
295
- "Gorsuch"
296
- ],
297
- "2021": [
298
- "Kagan",
299
- "Breyer",
300
- "Sotomayor",
301
- "Gorsuch",
302
- "Roberts",
303
- "Thomas",
304
- "Alito"
305
- ],
306
- "2022": [
307
- "Kagan",
308
- "Breyer",
309
- "Sotomayor",
310
- "Gorsuch",
311
- "Roberts",
312
- "Thomas",
313
- "Alito"
314
- ],
315
- "2023": [
316
- "Kagan",
317
- "Sotomayor",
318
- "Gorsuch",
319
- "Roberts",
320
- "Thomas",
321
- "Alito"
322
- ]
323
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
utils/__init__.py CHANGED
@@ -16,14 +16,13 @@ def normaliz_dict(d, target=1.0):
16
  return {key: value * factor for key, value in d.items()}
17
 
18
 
19
- def average_text(text, model, judges):
20
  result = model(text)
21
  new_res = []
22
  for d in result:
23
  p = {}
24
  for dicts in d:
25
- if dicts["label"] in judges:
26
- p[dicts["label"]] = dicts["score"]
27
  p = normaliz_dict(p)
28
  new_res.append(p)
29
 
 
16
  return {key: value * factor for key, value in d.items()}
17
 
18
 
19
+ def average_text(text, model):
20
  result = model(text)
21
  new_res = []
22
  for d in result:
23
  p = {}
24
  for dicts in d:
25
+ p[dicts["label"]] = dicts["score"]
 
26
  p = normaliz_dict(p)
27
  new_res.append(p)
28