Update app.py

app.py

@@ -2606,33 +2606,33 @@ with gr.Blocks(
                     
                     output += f"""
             
-            ---
-            
-            ### 💡 Key Insights
-            
-            - **Biggest Single Opportunity:** ${max(results, key=lambda x: x['missed_savings'])['missed_savings']:.2f} at {max(results, key=lambda x: x['missed_savings'])['merchant']}
-            - **Most Common Category:** {max(set([r['category'] for r in results]), key=[r['category'] for r in results].count)}
-            - **Average Transaction:** ${total_spending / len(results):.2f}
-            - **Optimization Potential:** {((total_optimal_rewards - total_rewards_earned) / total_rewards_earned * 100):.1f}% more rewards possible
-            
-            ---
-            
-            <div style="background: linear-gradient(135deg, #fff3cd 0%, #fff8e1 100%); padding: 20px; border-radius: 12px; border-left: 4px solid #ffc107; margin: 20px 0;">
-                <h4 style="margin: 0 0 10px 0; color: #856404;">💡 What This Means</h4>
-                <p style="margin: 0; color: #5d4037; font-size: 15px;">
-                    If you had used our AI recommendations for these {len(results)} transactions, you would have earned 
-                    <strong style="color: #e65100;">${total_missed:.2f} more</strong> in rewards. 
-                    Over a full year, that's <strong style="color: #e65100;">${total_missed * (365 / days):.0f}+</strong> in extra rewards!
-                </p>
-            </div>
-            
-            ---
-            
-            <div style="background: #e8f5e9; padding: 20px; border-radius: 10px; border-left: 4px solid #4caf50;">
-                <strong>🚀 Powered by Modal:</strong> This analysis processed {len(results)} transactions in parallel using serverless compute. 
-                In production, Modal can handle 1000+ transactions in seconds with automatic scaling.
-            </div>
-            """
+---
+
+### 💡 Key Insights
+
+- **Biggest Single Opportunity:** ${biggest_opp:.2f} at {merchant}
+- **Most Common Category:** {category}
+- **Average Transaction:** ${avg_trans:.2f}
+- **Optimization Potential:** {potential:.1f}% more rewards possible
+
+---
+
+<div style="background: linear-gradient(135deg, #fff3cd 0%, #fff8e1 100%); padding: 20px; border-radius: 12px; border-left: 4px solid #ffc107; margin: 20px 0;">
+    <h4 style="margin: 0 0 10px 0; color: #856404;">💡 What This Means</h4>
+    <p style="margin: 0; color: #5d4037; font-size: 15px;">
+        If you had used our AI recommendations for these {count} transactions, you would have earned 
+        <strong style="color: #e65100;">${extra:.2f} more</strong> in rewards. 
+        Over a full year, that's <strong style="color: #e65100;">${yearly:.0f}+</strong> in extra rewards!
+    </p>
+</div>
+
+---
+
+<div style="background: #e8f5e9; padding: 20px; border-radius: 10px; border-left: 4px solid #4caf50;">
+    <strong>🚀 Powered by Modal:</strong> This analysis processed {count} transactions in parallel using serverless compute. 
+    In production, Modal can handle 1000+ transactions in seconds with automatic scaling.
+</div>
+"""
                     
                     # Create charts
                     import plotly.graph_objects as go