debug

app.py

@@ -120,20 +120,25 @@ def init_leaderboard_mib_subgraph(dataframe, track):
     )
 
 def init_leaderboard_mib_causalgraph(dataframe, track):
-    print(f"init_leaderboard_mib: dataframe head before loc is {dataframe.head()}\n")
+    print("init_leaderboard_mib: starting...")
+    print(f"DataFrame columns after entering function: {dataframe.columns.tolist()}")
     
     if dataframe is None or dataframe.empty:
         raise ValueError("Leaderboard DataFrame is empty or None.")
 
-    # Print all columns for debugging
-    print("Available columns in dataframe:", dataframe.columns.tolist())
-    print("Expected columns from AutoEvalColumn_mib_causalgraph:", [c.name for c in fields(AutoEvalColumn_mib_causalgraph) if not c.hidden])
+    # Let's check our expected columns
+    expected_cols = [c.name for c in fields(AutoEvalColumn_mib_causalgraph)]
+    print(f"Expected columns: {expected_cols}")
     
-    # Remove this line since we don't need track filtering for causalgraph
-    # dataframe = dataframe.loc[dataframe["Track"] == track]
-
-    print(f"init_leaderboard_mib: dataframe head after loc is {dataframe.head()}\n")
+    # Check if every expected column exists
+    missing_cols = [col for col in expected_cols if col not in dataframe.columns]
+    if missing_cols:
+        print(f"Missing columns: {missing_cols}")
     
+    # Print first few rows to see the data structure
+    print("\nFirst few rows of DataFrame:")
+    print(dataframe.head().to_string())
+
     return Leaderboard(
         value=dataframe,
         datatype=[c.type for c in fields(AutoEvalColumn_mib_causalgraph)],