Update app.py

app.py

@@ -117,24 +117,24 @@ def init_leaderboard_mib(dataframe, track):
     )
 
 
-def init_leaderboard_mib_causal(json_data, task_type):
-    """Creates a summary leaderboard showing best layer performance for each method"""
-    if not json_data or 'results' not in json_data:
-        raise ValueError("Invalid JSON data structure")
-
+Ah, I see the issue - the function is receiving a DataFrame but the code expects JSON data. Let me fix the function to work with the DataFrame input:
+pythonCopydef init_leaderboard_mib_causal(dataframe, track):
+    """Creates a leaderboard summary for causal intervention results"""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    
+    # Read and process JSON data
+    json_data = json.loads(dataframe.to_json(orient='records'))[0]
+    
     # Process results into summary format
     summary_data = []
     method_name = json_data['method_name']
     
-    # Extract model and task data
+    # Extract scores for MCQA task
     for model_result in json_data['results']:
         model_id = model_result['model_id']
+        task_data = model_result['task_scores']['MCQA']
         
-        # Get scores for the specified task
-        task_data = model_result['task_scores'].get(task_type, [])
-        if not task_data:
-            continue
-            
         # Calculate best layer performance
         best_scores = calculate_best_layer_scores(task_data)
         
@@ -148,14 +148,14 @@ def init_leaderboard_mib_causal(json_data, task_type):
         summary_data.append(summary_row)
     
     # Convert to DataFrame
-    df = pd.DataFrame(summary_data)
+    results_df = pd.DataFrame(summary_data)
     
     # Round numeric columns to 3 decimal places
     numeric_cols = ['Best Output Token Score', 'Best Output Location Score']
-    df[numeric_cols] = df[numeric_cols].round(3)
+    results_df[numeric_cols] = results_df[numeric_cols].round(3)
     
     return Leaderboard(
-        value=df,
+        value=results_df,
         datatype=['text', 'text', 'number', 'number', 'number'],
         select_columns=SelectColumns(
             default_selection=['Method', 'Model', 'Best Output Token Score', 'Best Output Location Score', 'Best Layer'],