Refactor leaderboard column definitions to use field defaults for better clarity and maintainability

main.py

@@ -1,6 +0,0 @@
-def main():
-    print("Hello from tunisianleaderboard!")
-
-
-if __name__ == "__main__":
-    main()

src/display/utils.py

@@ -1,8 +1,6 @@
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass, make_dataclass, field
 from enum import Enum
 
-import pandas as pd
-
 from src.about import Tasks
 
 def fields(raw_class):
@@ -23,22 +21,22 @@ class ColumnContent:
 ## Leaderboard columns
 auto_eval_column_dict = []
 # Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+auto_eval_column_dict.append(["model_type_symbol", ColumnContent, field(default_factory=lambda: ColumnContent("T", "str", True, never_hidden=True))])
+auto_eval_column_dict.append(["model", ColumnContent, field(default_factory=lambda: ColumnContent("Model", "markdown", True, never_hidden=True))])
 #Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
+auto_eval_column_dict.append(["average", ColumnContent, field(default_factory=lambda: ColumnContent("Average ⬆️", "number", True))])
 for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
+    auto_eval_column_dict.append([task.name, ColumnContent, field(default_factory=lambda task=task: ColumnContent(task.value.col_name, "number", True))])
 # Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
+auto_eval_column_dict.append(["model_type", ColumnContent, field(default_factory=lambda: ColumnContent("Type", "str", False))])
+auto_eval_column_dict.append(["architecture", ColumnContent, field(default_factory=lambda: ColumnContent("Architecture", "str", False))])
+auto_eval_column_dict.append(["weight_type", ColumnContent, field(default_factory=lambda: ColumnContent("Weight type", "str", False, True))])
+auto_eval_column_dict.append(["precision", ColumnContent, field(default_factory=lambda: ColumnContent("Precision", "str", False))])
+auto_eval_column_dict.append(["license", ColumnContent, field(default_factory=lambda: ColumnContent("Hub License", "str", False))])
+auto_eval_column_dict.append(["params", ColumnContent, field(default_factory=lambda: ColumnContent("#Params (B)", "number", False))])
+auto_eval_column_dict.append(["likes", ColumnContent, field(default_factory=lambda: ColumnContent("Hub ❤️", "number", False))])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, field(default_factory=lambda: ColumnContent("Available on the hub", "bool", False))])
+auto_eval_column_dict.append(["revision", ColumnContent, field(default_factory=lambda: ColumnContent("Model sha", "str", False, False))])
 
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)