rzanoli commited on
Commit
ea6af72
Β·
1 Parent(s): 5a8f6c4

Small changes

Browse files
Files changed (3) hide show
  1. app.py +29 -1
  2. src/display/css_html_js.py +1 -0
  3. src/display/utils.py +4 -15
app.py CHANGED
@@ -11,7 +11,6 @@ from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REP
11
  from src.populate import get_evaluation_queue_df, get_leaderboard_df
12
  from src.submission.submit import add_new_eval
13
 
14
-
15
  # Define task metadata (icons, names, descriptions)
16
  TASK_METADATA = {
17
  "TE": {"icon": "πŸ“Š", "name": "Textual Entailment", "tooltip": ""},
@@ -30,6 +29,33 @@ def restart_space():
30
  """Restart the Hugging Face space."""
31
  API.restart_space(repo_id=REPO_ID)
32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
  # Helper function for leaderboard initialization
34
  def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
35
  """Initialize and return a leaderboard."""
@@ -53,6 +79,7 @@ def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
53
  bool_checkboxgroup_label="Hide models",
54
  interactive=False,
55
  )
 
56
 
57
 
58
  def download_snapshot(repo, local_dir):
@@ -80,6 +107,7 @@ with demo:
80
  gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
81
 
82
  with gr.Tabs(elem_classes="tab-buttons") as tabs:
 
83
  # Main leaderboard tab
84
  with gr.TabItem("πŸ… EVALITA-LLM Benchmark"):
85
 
 
11
  from src.populate import get_evaluation_queue_df, get_leaderboard_df
12
  from src.submission.submit import add_new_eval
13
 
 
14
  # Define task metadata (icons, names, descriptions)
15
  TASK_METADATA = {
16
  "TE": {"icon": "πŸ“Š", "name": "Textual Entailment", "tooltip": ""},
 
29
  """Restart the Hugging Face space."""
30
  API.restart_space(repo_id=REPO_ID)
31
 
32
+
33
+ def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
34
+ """Initialize and return a leaderboard."""
35
+ if dataframe is None or dataframe.empty:
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+ raise ValueError("Leaderboard DataFrame is empty or None.")
37
+
38
+ field_list = fields(AutoEvalColumn)
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+
40
+ return Leaderboard(
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+ value=dataframe,
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+ datatype=[c.type for c in field_list],
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+ select_columns=SelectColumns(
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+ default_selection=default_selection or [c.name for c in field_list if c.displayed_by_default],
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+ cant_deselect=[c.name for c in field_list if c.never_hidden],
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+ label="Select Columns to Display:",
47
+ ),
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+ search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
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+ hide_columns=hidden_columns or [c.name for c in field_list if c.hidden],
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+ filter_columns=[
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+ ColumnFilter(AutoEvalColumn.fewshot_type.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)"),
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+ ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"),
53
+ ],
54
+ bool_checkboxgroup_label="Hide models",
55
+ interactive=False,
56
+ )
57
+
58
+ '''
59
  # Helper function for leaderboard initialization
60
  def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
61
  """Initialize and return a leaderboard."""
 
79
  bool_checkboxgroup_label="Hide models",
80
  interactive=False,
81
  )
82
+ '''
83
 
84
 
85
  def download_snapshot(repo, local_dir):
 
107
  gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
108
 
109
  with gr.Tabs(elem_classes="tab-buttons") as tabs:
110
+
111
  # Main leaderboard tab
112
  with gr.TabItem("πŸ… EVALITA-LLM Benchmark"):
113
 
src/display/css_html_js.py CHANGED
@@ -94,6 +94,7 @@ custom_css = """
94
  #box-filter > .form{
95
  border: 0
96
  }
 
97
  """
98
 
99
  get_window_url_params = """
 
94
  #box-filter > .form{
95
  border: 0
96
  }
97
+
98
  """
99
 
100
  get_window_url_params = """
src/display/utils.py CHANGED
@@ -89,8 +89,6 @@ class ModelType(Enum):
89
  return ModelType.IFT
90
  return ModelType.Unknown
91
 
92
-
93
-
94
  @dataclass
95
  class FewShotDetails:
96
  name: str
@@ -113,10 +111,6 @@ class FewShotType(Enum):
113
  return FewShotType.FS
114
  return FewShotType.Unknown
115
 
116
-
117
-
118
-
119
-
120
  class WeightType(Enum):
121
  Adapter = ModelDetails("Adapter")
122
  Original = ModelDetails("Original")
@@ -142,9 +136,7 @@ EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
142
 
143
  BENCHMARK_COLS = [t.value.col_name for t in Tasks]
144
 
145
-
146
- # Roberto
147
-
148
  # Nuovi valori per CPS, AVERAGE, BEST, e ID nella tabella
149
  @dataclass
150
  class NewColumnContent:
@@ -153,18 +145,15 @@ class NewColumnContent:
153
  displayed_by_default: bool
154
  hidden: bool = False
155
  never_hidden: bool = False
 
156
 
157
- # Inizializza i nuovi valori
158
  new_column_dict = []
159
  # Aggiungi CPS, VERAGE, BEST, ID
160
  new_column_dict.append(["CPS", NewColumnContent, NewColumnContent("CPS", "number", True)])
161
  new_column_dict.append(["AVERAGE", NewColumnContent, NewColumnContent("Average ⬆️", "number", True)])
162
  new_column_dict.append(["BEST", NewColumnContent, NewColumnContent("Best Performance", "number", True)])
163
  new_column_dict.append(["ID", NewColumnContent, NewColumnContent("ID", "str", True)])
164
-
165
- # Puoi usare make_dataclass per creare la classe dinamicamente come per AutoEvalColumn
166
  NewColumn = make_dataclass("NewColumn", new_column_dict, frozen=True)
167
-
168
- # Includi questi nuovi valori nei COLS o in altre variabili di configurazione, se necessario
169
  NEW_COLS = [c.name for c in fields(NewColumn) if not c.hidden]
170
-
 
89
  return ModelType.IFT
90
  return ModelType.Unknown
91
 
 
 
92
  @dataclass
93
  class FewShotDetails:
94
  name: str
 
111
  return FewShotType.FS
112
  return FewShotType.Unknown
113
 
 
 
 
 
114
  class WeightType(Enum):
115
  Adapter = ModelDetails("Adapter")
116
  Original = ModelDetails("Original")
 
136
 
137
  BENCHMARK_COLS = [t.value.col_name for t in Tasks]
138
 
139
+ '''
 
 
140
  # Nuovi valori per CPS, AVERAGE, BEST, e ID nella tabella
141
  @dataclass
142
  class NewColumnContent:
 
145
  displayed_by_default: bool
146
  hidden: bool = False
147
  never_hidden: bool = False
148
+ '''
149
 
150
+ '''
151
  new_column_dict = []
152
  # Aggiungi CPS, VERAGE, BEST, ID
153
  new_column_dict.append(["CPS", NewColumnContent, NewColumnContent("CPS", "number", True)])
154
  new_column_dict.append(["AVERAGE", NewColumnContent, NewColumnContent("Average ⬆️", "number", True)])
155
  new_column_dict.append(["BEST", NewColumnContent, NewColumnContent("Best Performance", "number", True)])
156
  new_column_dict.append(["ID", NewColumnContent, NewColumnContent("ID", "str", True)])
 
 
157
  NewColumn = make_dataclass("NewColumn", new_column_dict, frozen=True)
 
 
158
  NEW_COLS = [c.name for c in fields(NewColumn) if not c.hidden]
159
+ '''