categories fix

app.py

@@ -191,6 +191,10 @@ def init_leaderboard(dataframe, visible_columns=None):
     visible_columns = ['model_name'] + visible_columns
     display_df = dataframe[visible_columns].copy()
 
+    print(f"--- DataFrame inside init_leaderboard (before rounding) ---")
+    print(display_df[['model_name', 'macro_accuracy', 'macro_recall', 'total_evals_count']].head() if all(c in display_df.columns for c in ['model_name', 'macro_accuracy', 'macro_recall', 'total_evals_count']) else "Relevant columns not present")
+    print(f"-------------------------------------------------------------")
+
     # Round numeric columns to 3 decimal places for display
     numeric_cols = display_df.select_dtypes(include=np.number).columns
     for col in numeric_cols:

src/populate.py

@@ -8,6 +8,7 @@ import pandas as pd
 import tempfile
 from typing import Dict, List, Optional
 from datetime import datetime
+import numpy as np
 
 from huggingface_hub import hf_hub_download, HfApi
 from datasets import load_dataset
@@ -201,17 +202,17 @@ def get_category_leaderboard_df(category: str, version="v0") -> pd.DataFrame:
             if accuracy_values:
                 filtered_entry["macro_accuracy"] = sum(accuracy_values) / len(accuracy_values)
             else:
-                filtered_entry["macro_accuracy"] = pd.NA
+                filtered_entry["macro_accuracy"] = np.nan
 
             if category_recall_values:
                 filtered_entry["macro_recall"] = sum(category_recall_values) / len(category_recall_values)
             else:
-                filtered_entry["macro_recall"] = pd.NA
+                filtered_entry["macro_recall"] = np.nan
 
             if total_samples > 0:
                 filtered_entry["total_evals_count"] = total_samples
             else:
-                filtered_entry["total_evals_count"] = pd.NA
+                filtered_entry["total_evals_count"] = np.nan
 
             filtered_entries.append(filtered_entry)