fix: use default_factory for mutable defaults in dataclasses for Python 3.13

Python 3.13 disallows mutable default values in dataclasses. Changed
ColumnContent instances to use field(default_factory=...) in both
make_dataclass and EvalQueueColumn.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/display/utils.py

@@ -1,4 +1,4 @@
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass, field, make_dataclass
 from enum import Enum
 
 from src.about import Tasks
@@ -23,22 +23,22 @@ 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 t=task: ColumnContent(t.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)
@@ -47,12 +47,12 @@ AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict)
 ## For the queue columns in the submission tab
 @dataclass
 class EvalQueueColumn:  # Queue column
-    model = ColumnContent("model", "markdown", True)
-    revision = ColumnContent("revision", "str", True)
-    private = ColumnContent("private", "bool", True)
-    precision = ColumnContent("precision", "str", True)
-    weight_type = ColumnContent("weight_type", "str", "Original")
-    status = ColumnContent("status", "str", True)
+    model: ColumnContent = field(default_factory=lambda: ColumnContent("model", "markdown", True))
+    revision: ColumnContent = field(default_factory=lambda: ColumnContent("revision", "str", True))
+    private: ColumnContent = field(default_factory=lambda: ColumnContent("private", "bool", True))
+    precision: ColumnContent = field(default_factory=lambda: ColumnContent("precision", "str", True))
+    weight_type: ColumnContent = field(default_factory=lambda: ColumnContent("weight_type", "str", "Original"))
+    status: ColumnContent = field(default_factory=lambda: ColumnContent("status", "str", True))
 
 
 ## All the model information that we might need