fix: display bug in gadio

src/display/utils.py

@@ -1,11 +1,13 @@
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass, field, make_dataclass
 from enum import Enum
 
 import pandas as pd
 
 
 def fields(raw_class):
-    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
+    return [
+        v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"
+    ]
 
 
 @dataclass
@@ -16,16 +18,18 @@ class Task:
 
 
 class Tasks(Enum):
-
     # medqa = Task("medqa", "acc_norm", "MedQA") # medqa_4options?
     # am i just manually going to include everything? hmm for display, idk how easily do i want to be able to tick this on and off?
-    # where does the acc_norm come from 
-    medmcqa = Task("medmcqa", "acc_norm", "MedMCQA") 
+    # where does the acc_norm come from
+    medmcqa = Task("medmcqa", "acc_norm", "MedMCQA")
     pubmedqa = Task("pubmedqa", "acc", "PubMedQA")
-    # task2 = Task("pubmedqa_no_context", "PubMedQA_no_context", 0) 
-    pubmedqa_no_context = Task("pubmedqa_no_context", "acc", "PubMedQA_no_context") # adding this throws an error. -> value=leaderboard_df[
+    # task2 = Task("pubmedqa_no_context", "PubMedQA_no_context", 0)
+    pubmedqa_no_context = Task(
+        "pubmedqa_no_context", "acc", "PubMedQA_no_context"
+    )  # adding this throws an error. -> value=leaderboard_df[
     biolama_umls = Task("biolama_umls", "acc", "BioLAMA-UMLS")
-    
+
+
 # These classes are for user facing column names,
 # to avoid having to change them all around the code
 # when a modif is needed
@@ -39,29 +43,103 @@ class ColumnContent:
     dummy: bool = False
     is_task: bool = False
 
+
+# Define a function to generate ColumnContent instances
+def column_content_factory(
+    name: str,
+    type: str,
+    displayed_by_default: bool,
+    hidden: bool = False,
+    never_hidden: bool = False,
+    dummy: bool = False,
+    is_task: bool = False,
+):
+    return lambda: ColumnContent(
+        name=name,
+        type=type,
+        displayed_by_default=displayed_by_default,
+        hidden=hidden,
+        never_hidden=never_hidden,
+        dummy=dummy,
+        is_task=is_task,
+    )
+
+
 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)])
-#Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Avg", "number", True)])
+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),
+    ]
+)
+# Scores
+auto_eval_column_dict.append(
+    ["average", ColumnContent, ColumnContent("Avg", "number", True)]
+)
 for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True, is_task=True)]) # hidden was true by default
+    auto_eval_column_dict.append(
+        [
+            task.name,
+            ColumnContent,
+            ColumnContent(task.value.col_name, "number", True, is_task=True),
+        ]
+    )  # hidden was true by default
 # 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, 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)]
+)
 # Dummy column for the search bar (hidden by the custom CSS)
-auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
+# Define the structure of your dataclass fields with default_factory for mutable defaults
+auto_eval_column_fields = [
+    (
+        "model_type_symbol",
+        ColumnContent,
+        field(
+            default_factory=column_content_factory("T", "str", True, never_hidden=True)
+        ),
+    ),
+    # Add other fields similarly...
+]
 
 # We use make dataclass to dynamically fill the scores from Tasks
-AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
+AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_fields, frozen=True)
 
 
 @dataclass(frozen=True)
@@ -109,9 +187,6 @@ class WeightType(Enum):
     Delta = ModelDetails("Delta")
 
 
-
-
-
 class Precision(Enum):
     float32 = ModelDetails("float32")
     float16 = ModelDetails("float16")
@@ -136,13 +211,17 @@ class Precision(Enum):
         if precision in ["GPTQ", "None"]:
             return Precision.qt_GPTQ
         return Precision.Unknown
-        
+
 
 # Column selection
 COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
 TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
-COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
-TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
+COLS_LITE = [
+    c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
+]
+TYPES_LITE = [
+    c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
+]
 
 EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
 EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]