Update app.py

app.py

@@ -486,45 +486,67 @@ with gr.Column(visible=True) as leaderboard_tab:
         datatype=["markdown", "markdown", "markdown"] + ["markdown"] * (len(category_choices)+1)  # Support markdown in all columns
     )
 
-def create_category_chart(selected_models, selected_categories):
-    if not selected_models:
-        fig = px.bar(title='Please select at least one model for comparison')
-        fig.update_layout(showlegend=True)  # Ensure legend remains visible
+def create_category_chart(selected_systems, selected_categories):
+    if not selected_systems:
+        # Create an empty figure with a prompt message
+        df = pd.DataFrame({'AI System': [], 'Category': [], 'Evaluations Completed': []})
+        fig = px.bar(df, 
+                    x='AI System',
+                    y='Evaluations Completed',
+                    title='Please select at least one AI system for comparison')
+        fig.update_layout(showlegend=True)
         return fig
     
     # Sort categories before processing
     selected_categories = sort_categories(selected_categories)
     
     data = []
-    for model in selected_models:
+    for system_name in selected_systems:
         for category in selected_categories:
-            if category in models[model]['scores']:
-                total_score = 0
-                total_questions = 0
+            if category in models[system_name]['scores']:
+                completed = 0
+                total = 0
                 
-                for section in models[model]['scores'][category].values():
+                for section in models[system_name]['scores'][category].values():
                     if section['status'] != 'N/A':
                         questions = section.get('questions', {})
-                        total_score += sum(1 for q in questions.values() if q)
-                        total_questions += len(questions)
+                        completed += sum(1 for q in questions.values() if q)
+                        total += len(questions)
                 
-                score_percentage = (total_score / total_questions * 100) if total_questions > 0 else 0
-                data.append({
-                    'AI System': model,
-                    'Category': category,
-                    'Completion Rate': score_percentage
-                })
+                if total > 0:  # Only add if there are evaluations to do
+                    data.append({
+                        'AI System': system_name,
+                        'Category': category.split('.')[1].strip(),
+                        'Evaluations Completed': completed,
+                        'Total Evaluations': total
+                    })
     
     df = pd.DataFrame(data)
+    
     if df.empty:
-        fig = px.bar(title='No data available for the selected models and categories')
+        fig = px.bar(title='No data available for the selected AI systems and categories')
     else:
-        fig = px.bar(df, x='AI System', y='Completion Rate', color='Category',
-                     title='AI System Evaluation Completion Rates by Category',
-                     labels={'Completion Rate': 'Completion Rate (%)'},
-                     category_orders={"Category": selected_categories})
+        fig = px.bar(
+            df,
+            x='AI System',
+            y='Evaluations Completed',
+            color='Category',
+            title='Number of Evaluations Completed by Category',
+            labels={
+                'Evaluations Completed': 'Evaluations Completed',
+                'AI System': 'AI System Name',
+                'Category': 'Evaluation Category'
+            },
+            hover_data=['Total Evaluations']
+        )
+        
+        fig.update_layout(
+            showlegend=True,
+            xaxis_title="AI System Name",
+            yaxis_title="Number of Evaluations Completed",
+            # hovermode='x unified'
+        )
     
-    fig.update_layout(showlegend=True)  # Ensure legend remains visible
     return fig
 
 def update_detailed_scorecard(model, selected_categories):