debug

app.py

@@ -200,50 +200,85 @@ from src.about import TasksMib_Subgraph
 #         interactive=False,
 #     )
 
+
+
+# def init_leaderboard_mib_subgraph(dataframe, track):
+#     """Initialize the subgraph leaderboard with grouped column selection."""
+#     if dataframe is None or dataframe.empty:
+#         raise ValueError("Leaderboard DataFrame is empty or None.")
+
+#     # Get tasks and models using the new class methods
+#     tasks = TasksMib_Subgraph.get_all_tasks()
+#     models = TasksMib_Subgraph.get_all_models()
+
+#     # Create a mapping from selection to actual column names
+#     selection_map = {}
+    
+#     # Add task mappings - when a task is selected, show all its columns
+#     for task in tasks:
+#         # For each task, find all valid task_model combinations
+#         valid_combos = []
+#         for model in models:
+#             col_name = f"{task}_{model}"
+#             if col_name in dataframe.columns:
+#                 valid_combos.append(col_name)
+#         if valid_combos:
+#             selection_map[task] = valid_combos
+    
+#     # Add model mappings - when a model is selected, show all its columns
+#     for model in models:
+#         # For each model, find all valid task_model combinations
+#         valid_combos = []
+#         for task in tasks:
+#             col_name = f"{task}_{model}"
+#             if col_name in dataframe.columns:
+#                 valid_combos.append(col_name)
+#         if valid_combos:
+#             selection_map[model] = valid_combos
+
+#     return Leaderboard(
+#         value=dataframe,
+#         datatype=[c.type for c in fields(AutoEvalColumn_mib_subgraph)],
+#         select_columns=SelectColumns(
+#             choices=[tasks, models],  # Two groups of choices
+#             labels=["Tasks", "Models"],  # Labels for each group
+#             default_selection=[*tasks, *models],  # Show everything by default
+#             cant_deselect=["Method"],  # Method column always visible
+#             label="Filter by Tasks or Models:",
+#             selection_map=selection_map  # Map selections to actual 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 with grouped column selection."""
+    """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 tasks and models using the new class methods
-    tasks = TasksMib_Subgraph.get_all_tasks()
-    models = TasksMib_Subgraph.get_all_models()
-
-    # Create a mapping from selection to actual column names
-    selection_map = {}
+    # 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))
     
-    # Add task mappings - when a task is selected, show all its columns
-    for task in tasks:
-        # For each task, find all valid task_model combinations
-        valid_combos = []
-        for model in models:
-            col_name = f"{task}_{model}"
-            if col_name in dataframe.columns:
-                valid_combos.append(col_name)
-        if valid_combos:
-            selection_map[task] = valid_combos
-    
-    # Add model mappings - when a model is selected, show all its columns
-    for model in models:
-        # For each model, find all valid task_model combinations
-        valid_combos = []
-        for task in tasks:
-            col_name = f"{task}_{model}"
-            if col_name in dataframe.columns:
-                valid_combos.append(col_name)
-        if valid_combos:
-            selection_map[model] = valid_combos
+    # 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(
-            choices=[tasks, models],  # Two groups of choices
-            labels=["Tasks", "Models"],  # Labels for each group
-            default_selection=[*tasks, *models],  # Show everything by default
+            default_selection=selection_choices,  # Show all by default
+            choices=selection_choices,
             cant_deselect=["Method"],  # Method column always visible
-            label="Filter by Tasks or Models:",
-            selection_map=selection_map  # Map selections to actual columns
+            label="Select Tasks or Models:",
         ),
         search_columns=["Method"],
         hide_columns=[c.name for c in fields(AutoEvalColumn_mib_subgraph) if c.hidden],
@@ -259,10 +294,6 @@ def init_leaderboard_mib_subgraph(dataframe, track):
 
 
 
-
-
-
-
 def init_leaderboard_mib_causalgraph(dataframe, track):
     # print("Debugging column issues:")
     # print("\nActual DataFrame columns:")

requirements.txt

@@ -4,7 +4,7 @@ datasets
 fastapi==0.112.2
 gradio
 gradio[oauth]
-gradio_leaderboard==0.0.15
+gradio_leaderboard==0.0.13
 gradio_client
 huggingface-hub>=0.18.0
 matplotlib