debug

app.py

@@ -255,36 +255,36 @@ 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.")
+# 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))
+#     # 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
-    ]
+#     # 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,
-    )
+#     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):
@@ -318,6 +318,61 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 #     )
 
 
+def init_leaderboard_mib_subgraph(dataframe, track):
+    """Initialize the subgraph leaderboard with verified task/model column selection"""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+    # First, let's identify which columns actually exist in our dataframe
+    print("Available columns in dataframe:", dataframe.columns.tolist())
+    
+    # Create task selections based on TasksMib_Subgraph definition
+    task_selections = []
+    for task in TasksMib_Subgraph:
+        task_cols = []
+        for model in task.value.models:
+            col_name = f"{task.value.benchmark}_{model}"
+            if col_name in dataframe.columns:
+                task_cols.append(col_name)
+        
+        if task_cols:  # Only add tasks that have data
+            print(f"Task {task.value.benchmark} has columns:", task_cols)
+            task_selections.append(f"Task: {task.value.benchmark}")
+    
+    # Create model selections by checking which models appear in columns
+    model_selections = []
+    all_models = list(set(model for task in TasksMib_Subgraph for model in task.value.models))
+    
+    for model in all_models:
+        model_cols = []
+        for task in TasksMib_Subgraph:
+            if model in task.value.models:
+                col_name = f"{task.value.benchmark}_{model}"
+                if col_name in dataframe.columns:
+                    model_cols.append(col_name)
+        
+        if model_cols:  # Only add models that have data
+            print(f"Model {model} has columns:", model_cols)
+            model_selections.append(f"Model: {model}")
+    
+    # Combine all selections
+    selections = task_selections + model_selections
+    print("Final selection options:", selections)
+    
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+        select_columns=SelectColumns(
+            default_selection=selections,
+            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,
+    )
+
+