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app.py

@@ -368,32 +368,51 @@ def create_leaderboard(selected_categories):
         for category_name, category in data['scores'].items():
             category_score = 0
             category_total = 0
+            all_na = True
             
             for section in category.values():
                 if section['status'] != 'N/A':
+                    all_na = False
                     questions = section.get('questions', {})
                     category_score += sum(1 for q in questions.values() if q)
                     category_total += len(questions)
             
             if category_total > 0:
                 score_by_category[category_name] = (category_score / category_total) * 100
-                total_score += category_score
-                total_questions += category_total
+            elif all_na:
+                score_by_category[category_name] = "N/A"
+            total_score += category_score
+            total_questions += category_total
         
         # Calculate overall score
-        score_percentage = (total_score / total_questions * 100) if total_questions > 0 else 0
+        overall_all_na = all(
+            all(section['status'] == 'N/A' for section in category.values())
+            for category_name, category in data['scores'].items()
+            if category_name in selected_categories
+        )
+        
+        score_percentage = "N/A" if overall_all_na else (
+            (total_score / total_questions * 100) if total_questions > 0 else 0
+        )
         
         # Get model type and URL
         model_type = data['metadata'].get('Type', 'Unknown')
         model_url = data['metadata'].get('URL', '')
         
+        # Get modalities and create badges
+        modalities = data['metadata'].get('Modalities', [])
+        modality_badges = " ".join(
+            f"<span class='modality-badge'>{get_modality_icon(m)} {m}</span>"
+            for m in modalities
+        ) if modalities else "<span class='modality-badge'>💫 Unknown</span>"
+        
         # Create model name with HTML link if URL exists
         model_display = f'<a href="{model_url}" target="_blank">{model}</a>' if model_url else model
         
         # Create entry with numerical scores
         model_entry = {
             'AI System': model_display,
-            'Type': model_type,
+            'Modality': f"<div class='modality-container'>{modality_badges}</div>",
             'Overall Completion Rate': score_percentage
         }
         
@@ -418,17 +437,40 @@ def create_leaderboard(selected_categories):
     # Convert to DataFrame
     df = pd.DataFrame(scores)
     
-    # Sort by Overall Completion Rate descending
-    df = df.sort_values('Overall Completion Rate', ascending=False)
+    # Sort by Overall Completion Rate descending, putting N/A at the end
+    df['_sort_value'] = df['Overall Completion Rate'].apply(
+        lambda x: -float('inf') if x == "N/A" else float(x)
+    )
+    df = df.sort_values('_sort_value', ascending=False)
+    df = df.drop('_sort_value', axis=1)
     
     # Add rank column based on current sort
     df.insert(0, 'Rank', range(1, len(df) + 1))
     
-    # Format scores with % after sorting
-    numeric_columns = ['Overall Completion Rate'] + list(category_map.values())
-    for col in df.columns:
-        if col in numeric_columns:
-            df[col] = df[col].apply(lambda x: f"{x:.1f}%")
+    # Get completion rate columns (Overall + category-specific)
+    completion_rate_columns = ['Overall Completion Rate'] + [
+        display_name for full_cat_name, display_name in category_map.items()
+        if full_cat_name in selected_categories
+    ]
+    
+    # Format non-completion rate columns
+    df['Rank'] = df['Rank'].astype(str)
+    
+    # Identify and format highest values for completion rate columns
+    for col in completion_rate_columns:
+        if col in df.columns:
+            # Filter out N/A values to find the maximum numerical value
+            numeric_values = df[df[col] != "N/A"][col]
+            if not numeric_values.empty:
+                max_value = numeric_values.max()
+                df[col] = df.apply(
+                    lambda row: "N/A" if row[col] == "N/A"
+                    else f"**{row[col]:.1f}%**" if row[col] == max_value 
+                    else f"{row[col]:.1f}%", 
+                    axis=1
+                )
+            else:
+                df[col] = df[col].apply(lambda x: "N/A")
     
     return df
 
@@ -436,10 +478,11 @@ first_model = next(iter(models.values()))
 category_choices = list(first_model['scores'].keys())
 
 with gr.Column(visible=True) as leaderboard_tab:
-        leaderboard_output = gr.DataFrame(
-            value=create_leaderboard(category_choices),  # Initialize with all categories selected
-            interactive=False,
-            wrap=True
+    leaderboard_output = gr.DataFrame(
+        value=create_leaderboard(category_choices),
+        interactive=False,
+        wrap=True,
+        datatype=["markdown", "markdown", "markdown"] + ["markdown"] * (len(category_choices)+1)  # Support markdown in all columns
     )
 
 def create_category_chart(selected_models, selected_categories):
@@ -645,7 +688,7 @@ with gr.Blocks(css=css) as demo:
             value=create_leaderboard(category_choices),
             interactive=False,
             wrap=True,
-            datatype=["markdown", "markdown", "markdown"] + ["markdown"] * len(category_choices)  # Set markdown type for all columns
+            datatype=["markdown", "markdown", "markdown"] + ["markdown"] * (len(category_choices)+1)  # Support markdown in all columns
         )
     
     with gr.Column(visible=False) as category_analysis_tab:

dashboard.css

@@ -616,4 +616,26 @@
 
 .dark .leaderboard-table tr:hover {
   background-color: #2d2d2d;
+}
+.dataframe .modality-container {
+  margin-top: 4px;
+}
+
+.dataframe .modality-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+  padding: 2px 6px;
+  background-color: #f0f7ff;
+  border: 1px solid #cce3ff;
+  border-radius: 12px;
+  font-size: 0.85em;
+  color: #0066cc;
+  margin: 2px;
+}
+
+.dark .dataframe .modality-badge {
+  background-color: #1a2733;
+  border-color: #2c3e50;
+  color: #99ccff;
 }