update

pages/leaderboard.py

@@ -16,35 +16,35 @@ df = pd.DataFrame(data)
 def update_leaderboard(sort_by):
     # In a real implementation, this would filter the data based on the category
     sorted_df = df.sort_values(by=sort_by, ascending=False)
-    
+
     # Update ranks based on new sorting
     sorted_df['Rank'] = range(1, len(sorted_df) + 1)
-    
+
     # Convert DataFrame to HTML with clickable headers for sorting
     html = sorted_df.to_html(index=False, escape=False)
-    
+
     # Add sorting links to column headers
     for column in sorted_df.columns:
         html = html.replace(f'<th>{column}</th>', 
                             f'<th><a href="#" onclick="sortBy(\'{column}\'); return false;">{column}</a></th>')
-    
+
     return html
 
 def create_leaderboard():
     with gr.Blocks(css="#leaderboard table { width: 100%; } #leaderboard th, #leaderboard td { padding: 8px; text-align: left; }") as demo:
         gr.Markdown("# 🏆 Chris-Project Summarization Arena Leaderboard")
-        
+
         with gr.Row():
             gr.Markdown("[Blog](placeholder) | [GitHub](placeholder) | [Paper](placeholder) | [Dataset](placeholder) | [Twitter](placeholder) | [Discord](placeholder)")
-        
+
         gr.Markdown("Welcome to our open platform for evaluating LLM summarization capabilities. We use the DATASET_NAME_PLACEHOLDER dataset to generate summaries with MODEL_NAME_PLACEHOLDER. These summaries are then evaluated by STRONGER_MODEL_NAME_PLACEHOLDER using the METRIC1_PLACEHOLDER and METRIC2_PLACEHOLDER metrics")
-        
+
         sort_by = gr.Dropdown(list(df.columns), label="Sort by", value="Rank")
-        
+
         gr.Markdown("**Performance**\n\n**methods**: 4,   **questions**: 150")
-        
+
         leaderboard = gr.HTML(update_leaderboard("Rank"), elem_id="leaderboard")
-        
+
         sort_by.change(update_leaderboard, inputs=[sort_by], outputs=[leaderboard])
 
     return demo