debug

app.py

@@ -255,33 +255,64 @@ 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 with grouped column selection for gradio-leaderboard 0.0.13"""
+    """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 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
-    ]
+    # 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=selection_choices,  # Show all by default
-            choices=selection_choices,
-            cant_deselect=["Method"],  # Method column always visible
-            label="Select Tasks or Models:",
+            default_selection=task_model_columns,
+            label="Select Task-Model Combinations:",
         ),
-        search_columns=["Method"],
-        hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
+        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,
     )
@@ -294,6 +325,7 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
 
 
+
 def init_leaderboard_mib_causalgraph(dataframe, track):
     # print("Debugging column issues:")
     # print("\nActual DataFrame columns:")