DontPlanToEnd commited on
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7d3a3f0
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1 Parent(s): c2d384b

Update app.py

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Files changed (1) hide show
  1. app.py +19 -77
app.py CHANGED
@@ -1,7 +1,6 @@
1
  import gradio as gr
2
  import pandas as pd
3
  import numpy as np
4
- import json
5
  from functools import partial
6
 
7
  custom_css = """
@@ -36,72 +35,8 @@ custom_css = """
36
  .default-underline {
37
  text-decoration: underline !important;
38
  }
39
- .customrangeslider {
40
- display: flex;
41
- flex-direction: column;
42
- width: 100%;
43
- }
44
- .customrangeslider input[type="range"] {
45
- -webkit-appearance: none;
46
- width: 100%;
47
- height: 10px;
48
- border-radius: 5px;
49
- background: #d3d3d3;
50
- outline: none;
51
- opacity: 0.7;
52
- transition: opacity .2s;
53
- }
54
- .customrangeslider input[type="range"]::-webkit-slider-thumb {
55
- -webkit-appearance: none;
56
- appearance: none;
57
- width: 20px;
58
- height: 20px;
59
- border-radius: 50%;
60
- background: #4CAF50;
61
- cursor: pointer;
62
- }
63
- .customrangeslider input[type="range"]::-moz-range-thumb {
64
- width: 20px;
65
- height: 20px;
66
- border-radius: 50%;
67
- background: #4CAF50;
68
- cursor: pointer;
69
- }
70
  """
71
 
72
- class CustomRangeSlider(gr.components.Component):
73
- def __init__(self, minimum, maximum, value, step, label):
74
- super().__init__(self)
75
- self.minimum = minimum
76
- self.maximum = maximum
77
- self.value = value
78
- self.step = step
79
- self.label = label
80
-
81
- def get_template_context(self):
82
- return {
83
- "min": self.minimum,
84
- "max": self.maximum,
85
- "value": self.value,
86
- "step": self.step,
87
- "label": self.label,
88
- }
89
-
90
- @classmethod
91
- def get_component_instance(cls, props):
92
- return cls(**props)
93
-
94
- def preprocess(self, x):
95
- return json.loads(x)
96
-
97
- def postprocess(self, y):
98
- return json.dumps(y)
99
-
100
- def get_block_name(self):
101
- return "customrangeslider"
102
-
103
- gr.components.Component.register_component("customrangeslider")(CustomRangeSlider)
104
-
105
  # Define the columns for the different leaderboards
106
  UGI_COLS = ['#P', 'Model', 'UGI πŸ†', 'W/10 πŸ‘', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
107
  WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore πŸ†', 'Reg+Int πŸ†', 'MyScore πŸ†', 'ASSS⬇️', 'SMOG⬆️', 'Yule⬇️']
@@ -128,7 +63,7 @@ def load_leaderboard_data(csv_file_path):
128
  return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
129
 
130
  # Update the leaderboard table based on the search query and parameter range filters
131
- def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list, w10_range: list) -> pd.DataFrame:
132
  filtered_df = df.copy()
133
  if param_ranges:
134
  param_mask = pd.Series(False, index=filtered_df.index)
@@ -156,7 +91,7 @@ def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list
156
 
157
  # Apply W/10 filtering
158
  if 'W/10 πŸ‘' in filtered_df.columns:
159
- filtered_df = filtered_df[(filtered_df['W/10 πŸ‘'] >= w10_range[0]) & (filtered_df['W/10 πŸ‘'] <= w10_range[1])]
160
 
161
  return filtered_df[columns]
162
 
@@ -191,7 +126,8 @@ with GraInter:
191
  elem_id="filter-columns-size",
192
  )
193
  with gr.Row():
194
- w10_range = CustomRangeSlider(minimum=0, maximum=10, value=[0, 10], step=0.1, label="W/10 Range")
 
195
 
196
  # Load the initial leaderboard data
197
  leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
@@ -311,36 +247,42 @@ with GraInter:
311
  **NA:** When models either reply with one number for every anime, give ratings not between 1 and 10, or don't give every anime in the list a rating.
312
  """)
313
 
314
- def update_all_tables(query, param_ranges, w10_range):
315
- ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_range)
316
 
317
  ws_df = leaderboard_df.sort_values(by='Reg+MyScore πŸ†', ascending=False)
318
- ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_range)
319
 
320
  arp_df = leaderboard_df.sort_values(by='Score πŸ†', ascending=False)
321
  arp_df_na = arp_df[arp_df[['Dif', 'Cor']].isna().any(axis=1)]
322
  arp_df = arp_df[~arp_df[['Dif', 'Cor']].isna().any(axis=1)]
323
 
324
- arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS, w10_range)
325
- arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_range).fillna('NA')
326
 
327
  return ugi_table, ws_table, arp_table, arp_na_table
328
 
329
  search_bar.change(
330
  fn=update_all_tables,
331
- inputs=[search_bar, filter_columns_size, w10_range],
332
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
333
  )
334
 
335
  filter_columns_size.change(
336
  fn=update_all_tables,
337
- inputs=[search_bar, filter_columns_size, w10_range],
 
 
 
 
 
 
338
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
339
  )
340
 
341
- w10_range.change(
342
  fn=update_all_tables,
343
- inputs=[search_bar, filter_columns_size, w10_range],
344
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
345
  )
346
 
 
1
  import gradio as gr
2
  import pandas as pd
3
  import numpy as np
 
4
  from functools import partial
5
 
6
  custom_css = """
 
35
  .default-underline {
36
  text-decoration: underline !important;
37
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38
  """
39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40
  # Define the columns for the different leaderboards
41
  UGI_COLS = ['#P', 'Model', 'UGI πŸ†', 'W/10 πŸ‘', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
42
  WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore πŸ†', 'Reg+Int πŸ†', 'MyScore πŸ†', 'ASSS⬇️', 'SMOG⬆️', 'Yule⬇️']
 
63
  return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
64
 
65
  # Update the leaderboard table based on the search query and parameter range filters
66
+ def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list, w10_min: float, w10_max: float) -> pd.DataFrame:
67
  filtered_df = df.copy()
68
  if param_ranges:
69
  param_mask = pd.Series(False, index=filtered_df.index)
 
91
 
92
  # Apply W/10 filtering
93
  if 'W/10 πŸ‘' in filtered_df.columns:
94
+ filtered_df = filtered_df[(filtered_df['W/10 πŸ‘'] >= w10_min) & (filtered_df['W/10 πŸ‘'] <= w10_max)]
95
 
96
  return filtered_df[columns]
97
 
 
126
  elem_id="filter-columns-size",
127
  )
128
  with gr.Row():
129
+ w10_min = gr.Slider(minimum=0, maximum=10, value=0, step=0.1, label="Min W/10")
130
+ w10_max = gr.Slider(minimum=0, maximum=10, value=10, step=0.1, label="Max W/10")
131
 
132
  # Load the initial leaderboard data
133
  leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
 
247
  **NA:** When models either reply with one number for every anime, give ratings not between 1 and 10, or don't give every anime in the list a rating.
248
  """)
249
 
250
+ def update_all_tables(query, param_ranges, w10_min, w10_max):
251
+ ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_min, w10_max)
252
 
253
  ws_df = leaderboard_df.sort_values(by='Reg+MyScore πŸ†', ascending=False)
254
+ ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_min, w10_max)
255
 
256
  arp_df = leaderboard_df.sort_values(by='Score πŸ†', ascending=False)
257
  arp_df_na = arp_df[arp_df[['Dif', 'Cor']].isna().any(axis=1)]
258
  arp_df = arp_df[~arp_df[['Dif', 'Cor']].isna().any(axis=1)]
259
 
260
+ arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max)
261
+ arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max).fillna('NA')
262
 
263
  return ugi_table, ws_table, arp_table, arp_na_table
264
 
265
  search_bar.change(
266
  fn=update_all_tables,
267
+ inputs=[search_bar, filter_columns_size, w10_min, w10_max],
268
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
269
  )
270
 
271
  filter_columns_size.change(
272
  fn=update_all_tables,
273
+ inputs=[search_bar, filter_columns_size, w10_min, w10_max],
274
+ outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
275
+ )
276
+
277
+ w10_min.change(
278
+ fn=update_all_tables,
279
+ inputs=[search_bar, filter_columns_size, w10_min, w10_max],
280
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
281
  )
282
 
283
+ w10_max.change(
284
  fn=update_all_tables,
285
+ inputs=[search_bar, filter_columns_size, w10_min, w10_max],
286
  outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
287
  )
288