debug

app.py

@@ -255,69 +255,69 @@ from src.about import TasksMib_Subgraph
 
 
 
-# def init_leaderboard_mib_subgraph(dataframe, track):
-#     """Initialize the subgraph leaderboard with grouped column selection for gradio-leaderboard 0.0.13"""
-#     if dataframe is None or dataframe.empty:
-#         raise ValueError("Leaderboard DataFrame is empty or None.")
-
-#     # Get all unique tasks and models
-#     tasks = [task.value.benchmark for task in TasksMib_Subgraph]
-#     models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
-    
-#     # Create two selection groups: one for tasks and one for models
-#     # In 0.0.13, we can only have one SelectColumns, so we'll combine them
-#     selection_choices = [
-#         *[f"Task: {task}" for task in tasks],  # Prefix with 'Task:' for clarity
-#         *[f"Model: {model}" for model in models]  # Prefix with 'Model:' for clarity
-#     ]
-
-#     return Leaderboard(
-#         value=dataframe,
-#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
-#         select_columns=SelectColumns(
-#             default_selection=selection_choices,  # Show all by default
-#             choices=selection_choices,
-#             cant_deselect=["Method"],  # Method column always visible
-#             label="Select Tasks or Models:",
-#         ),
-#         search_columns=["Method"],
-#         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
-#         bool_checkboxgroup_label="Hide models",
-#         interactive=False,
-#     )
-
-
 def init_leaderboard_mib_subgraph(dataframe, track):
-    """Initialize the subgraph leaderboard focusing only on task and model filtering.
-    
-    This implementation creates a focused view where users can select which task-model
-    combinations they want to see, making the analysis of results more straightforward.
-    """
+    """Initialize the subgraph leaderboard with grouped column selection for gradio-leaderboard 0.0.13"""
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
+
+    # Get all unique tasks and models
+    tasks = [task.value.benchmark for task in TasksMib_Subgraph]
+    models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
     
-    # Get all task-model combinations that actually exist in our data
-    task_model_columns = []
-    for task in TasksMib_Subgraph:
-        for model in task.value.models:
-            col_name = f"{task.value.benchmark}_{model}"
-            if col_name in dataframe.columns:
-                task_model_columns.append(col_name)
+    # Create two selection groups: one for tasks and one for models
+    # In 0.0.13, we can only have one SelectColumns, so we'll combine them
+    selection_choices = [
+        *[f"Task: {task}" for task in tasks],  # Prefix with 'Task:' for clarity
+        *[f"Model: {model}" for model in models]  # Prefix with 'Model:' for clarity
+    ]
 
     return Leaderboard(
         value=dataframe,
         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
         select_columns=SelectColumns(
-            default_selection=task_model_columns,
-            label="Select Task-Model Combinations:",
+            default_selection=selection_choices,  # Show all by default
+            choices=selection_choices,
+            cant_deselect=["Method"],  # Method column always visible
+            label="Select Tasks or Models:",
         ),
-        search_columns=["Method"],  # Keep Method searchable but not in column selection
-        hide_columns=[],  # We don't need to hide any columns
+        search_columns=["Method"],
+        hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
         bool_checkboxgroup_label="Hide models",
         interactive=False,
     )
 
 
+# def init_leaderboard_mib_subgraph(dataframe, track):
+#     """Initialize the subgraph leaderboard focusing only on task and model filtering.
+    
+#     This implementation creates a focused view where users can select which task-model
+#     combinations they want to see, making the analysis of results more straightforward.
+#     """
+#     if dataframe is None or dataframe.empty:
+#         raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+#     # Get all task-model combinations that actually exist in our data
+#     task_model_columns = []
+#     for task in TasksMib_Subgraph:
+#         for model in task.value.models:
+#             col_name = f"{task.value.benchmark}_{model}"
+#             if col_name in dataframe.columns:
+#                 task_model_columns.append(col_name)
+
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+#         select_columns=SelectColumns(
+#             default_selection=task_model_columns,
+#             label="Select Task-Model Combinations:",
+#         ),
+#         search_columns=["Method"],  # Keep Method searchable but not in column selection
+#         hide_columns=[],  # We don't need to hide any columns
+#         bool_checkboxgroup_label="Hide models",
+#         interactive=False,
+#     )
+
+