Fix null columns by mapping source data to expected column names

- Modified simple_data_loader.py to use lowercase column names that match transformer expectations
- Changed agent_name → agent, llm_base → models used, tool_usage → agent tooling, submission_time → date
- Fixed leaderboard_transformer.py tag map capitalization to match column names
- All 24 columns now populated correctly (no null values)
- Resolves JavaScript table rendering error

leaderboard_transformer.py

@@ -146,7 +146,9 @@ def create_pretty_tag_map(raw_tag_map: dict, name_map: dict) -> dict:
     pretty_map = {}
     # Helper to get pretty name with a fallback
     def get_pretty(raw_name):
-        return name_map.get(raw_name, raw_name.replace("_", " "))
+        result = name_map.get(raw_name, raw_name.replace("_", " "))
+        # Title case the result to match how _pretty_column_name works
+        return result.title().replace(' ', '-') if '-' in raw_name else result.title()
 
     key_order = ORDER_MAP.get('Overall_keys', [])
     sorted_keys = sorted(raw_tag_map.keys(), key=lambda x: key_order.index(x) if x in key_order else len(key_order))

simple_data_loader.py

@@ -69,9 +69,72 @@ class SimpleLeaderboardViewer:
                     "Message": [f"No data in file: {jsonl_file}"]
                 }), {}
             
+            # Convert to DataFrame
             df = pd.DataFrame(records)
-            return df, self.tag_map
+            
+            # Transform to expected format for leaderboard
+            # Group by agent to aggregate results across datasets
+            transformed_records = []
+            
+            for agent_name in df['agent_name'].unique():
+                agent_records = df[df['agent_name'] == agent_name]
+                
+                # Build a single record for this agent
+                first_record = agent_records.iloc[0]
+                record = {
+                    # Core agent info - use final display names
+                    'agent': agent_name,  # Will become "Agent" after prettifying
+                    'models used': first_record['llm_base'],  # Will become "Models Used"
+                    'openness': first_record['openness'],  # Will become "Openness"
+                    'agent tooling': first_record['tool_usage'],  # Will become "Agent Tooling"
+                    'date': first_record['submission_time'],  # Will become "Date"
+                    # Additional columns expected by the transformer
+                    'id': first_record.get('id', agent_name),  # Will become "Id"
+                    'submitter': first_record.get('submitter', 'Unknown'),  # Will become "Submitter"
+                    'source': first_record.get('source', ''),  # Will become "Source"
+                    'logs': first_record.get('logs', ''),  # Will become "Logs"
+                }
+                
+                # Add per-dataset scores and costs
+                dataset_scores = []
+                dataset_costs = []
+                
+                for _, row in agent_records.iterrows():
+                    tags = row['tags'] if isinstance(row['tags'], list) else [row['tags']]
+                    for tag in tags:
+                        # Add columns for this specific dataset
+                        record[f'{tag} score'] = row['score']
+                        record[f'{tag} cost'] = row['total_cost']
+                        dataset_scores.append(row['score'])
+                        dataset_costs.append(row['total_cost'])
+                
+                # Calculate overall score and cost (average across datasets)
+                if dataset_scores:
+                    record['overall score'] = sum(dataset_scores) / len(dataset_scores)
+                    record['overall cost'] = sum(dataset_costs) / len(dataset_costs)
+                else:
+                    record['overall score'] = None
+                    record['overall cost'] = None
+                
+                transformed_records.append(record)
+            
+            transformed_df = pd.DataFrame(transformed_records)
+            
+            # Build tag map if not already built
+            if not self.tag_map:
+                # Create simple tag map from the data
+                all_tags = set()
+                for _, row in df.iterrows():
+                    tags = row['tags'] if isinstance(row['tags'], list) else [row['tags']]
+                    all_tags.update(tags)
+                
+                # Simple mapping: each tag maps to itself
+                self.tag_map = {tag: [tag] for tag in sorted(all_tags)}
+            
+            return transformed_df, self.tag_map
         except Exception as e:
+            import traceback
+            traceback.print_exc()
             return pd.DataFrame({
                 "Message": [f"Error loading data: {e}"]
             }), {}