Update src/display/utils.py

src/display/utils.py

@@ -1,10 +1,11 @@
-from dataclasses import dataclass, field, make_dataclass
+from dataclasses import dataclass, field
 from enum import Enum
 
 import pandas as pd
 
 from src.about import Tasks
 
+
 def fields(raw_class):
     return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
 
@@ -20,39 +21,45 @@ class ColumnContent:
     hidden: bool = False
     never_hidden: bool = False
 
-## Leaderboard columns
-auto_eval_column_dict = []
-# Init
-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, field(default_factory=lambda: ColumnContent("Average ⬆️", "number", True))])
+
+# ── AutoEvalColumn ────────────────────────────────────────────────────────
+# Built as a plain class with class-level attributes so that
+# AutoEvalColumn.precision.name  (class-level access used in read_evals.py)
+# works correctly on all Python versions.
+# Previously used make_dataclass() which only supports instance-level access.
+class AutoEvalColumn:
+    # Identity
+    model_type_symbol = ColumnContent("T",              "str",      True,  never_hidden=True)
+    model             = ColumnContent("Model",          "markdown", True,  never_hidden=True)
+    # Scores
+    average           = ColumnContent("Average ⬆️",    "number",   True)
+    # Model information
+    model_type        = ColumnContent("Type",                       "str",    False)
+    architecture      = ColumnContent("Architecture",               "str",    False)
+    weight_type       = ColumnContent("Weight type",                "str",    False, True)
+    precision         = ColumnContent("Precision",                  "str",    False)
+    license           = ColumnContent("Hub License",                "str",    False)
+    params            = ColumnContent("#Params (B)",                "number", False)
+    likes             = ColumnContent("Hub ❤️",                    "number", False)
+    still_on_hub      = ColumnContent("Available on the hub",       "bool",   False)
+    revision          = ColumnContent("Model sha",                  "str",    False, False)
+
+
+# Dynamically add task score columns from Tasks enum
 for task in Tasks:
-    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, 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)
+    setattr(AutoEvalColumn, task.name, ColumnContent(task.value.col_name, "number", True))
+
 
 ## For the queue columns in the submission tab
-@dataclass(frozen=True)
-class EvalQueueColumn:  # Queue column
+class EvalQueueColumn:
     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")
+    weight_type = ColumnContent("weight_type", "str",      True)
     status      = ColumnContent("status",      "str",      True)
 
+
 ## All the model information that we might need
 @dataclass
 class ModelDetails:
@@ -98,8 +105,7 @@ class Precision(Enum):
 
 
 # Column selection
-COLS       = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
-EVAL_COLS  = [c.name for c in fields(EvalQueueColumn)]
-EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
-
+COLS           = [c.name for c in fields(AutoEvalColumn)   if not c.hidden]
+EVAL_COLS      = [c.name for c in fields(EvalQueueColumn)]
+EVAL_TYPES     = [c.type for c in fields(EvalQueueColumn)]
 BENCHMARK_COLS = [t.value.col_name for t in Tasks]