Update app.py

app.py

@@ -1723,9 +1723,9 @@ with gr.Blocks(
         <p class="hero-subtitle">
             <strong>You have 5 credit cards. You're at checkout. Which one do you use?</strong><br>
             Most people pick wrong and lose <strong>$400+ per year</strong>.<br>
-            Our AI agent makes the optimal choice in <strong>2 seconds</strong>.
-            <strong>Powered by OpenAI GPT-4 + Gemini + Modal</strong><br>
-                Your AI-powered rewards optimization assistant
+            Our AI agent makes the optimal choice in <strong>2 seconds</strong>.<br>
+            Powered by OpenAI <strong>GPT-4 + Gemini + Modal</strong><br>
+            Your AI-powered rewards optimization assistant
         </p>
         
         <div class="impact-stats">
@@ -3145,7 +3145,6 @@ with gr.Blocks(
                     with open(image_path, "rb") as image_file:
                         base64_image = base64.b64encode(image_file.read()).decode('utf-8')
                     
-                    # ✅ IMPROVED PROMPT with better category detection
                     response = openai_client.chat.completions.create(
                         model="gpt-4o",
                         messages=[{
@@ -3156,13 +3155,11 @@ with gr.Blocks(
                                     "text": """Extract the following from this receipt and classify accurately:
             
             1. **Merchant name** (exact as shown on receipt)
-            2. **Total amount** (final total only, not subtotals)
+            2. **Total amount** (final total only)
             3. **Date** (format: YYYY-MM-DD, or "Unknown" if not visible)
-            4. **Category** - Choose the MOST SPECIFIC category:
+            4. **Category** - Choose the MOST SPECIFIC:
                - "Wholesale Club" (Costco, Sam's Club, BJ's)
                - "Grocery Store" (Whole Foods, Safeway, Kroger, Trader Joe's)
-               - "Supermarket" (traditional grocery stores)
-               - "Convenience Store" (7-Eleven, Circle K)
                - "Restaurant" (sit-down dining)
                - "Fast Food" (quick service)
                - "Gas Station"
@@ -3172,11 +3169,9 @@ with gr.Blocks(
             
             5. **Top 3 items** purchased (if visible)
             
-            **IMPORTANT RULES:**
+            **IMPORTANT:**
             - Costco, Sam's Club, BJ's = "Wholesale Club" (NOT "Grocery Store")
-            - Walmart Supercenter = "Department Store" (NOT "Grocery Store")
-            - Target = "Department Store" (NOT "Grocery Store")
-            - Whole Foods, Safeway, Kroger = "Grocery Store"
+            - Walmart, Target = "Department Store" (NOT "Grocery Store")
             
             Return as JSON:
             {
@@ -3198,7 +3193,6 @@ with gr.Blocks(
                         max_tokens=500
                     )
                     
-                    # Parse response
                     receipt_data_str = response.choices[0].message.content
                     
                     import re
@@ -3210,12 +3204,10 @@ with gr.Blocks(
                     
                     category = receipt_data['category']
                     
-                    # Map category to correct MCC
+                    # Map category to MCC
                     category_to_mcc = {
                         "Wholesale Club": "5300",
                         "Grocery Store": "5411",
-                        "Supermarket": "5411",
-                        "Convenience Store": "5499",
                         "Restaurant": "5812",
                         "Fast Food": "5814",
                         "Gas Station": "5541",
@@ -3223,80 +3215,116 @@ with gr.Blocks(
                         "Online Shopping": "5942"
                     }
                     
-                    mcc = category_to_mcc.get(category, MCC_CATEGORIES.get(category, "5999"))
+                    mcc = category_to_mcc.get(category, "5999")
                     
-                    print(f"📊 Receipt Analysis:")
-                    print(f"   Merchant: {receipt_data['merchant']}")
-                    print(f"   Category: {category}")
-                    print(f"   MCC: {mcc}")
-                    print(f"   Amount: ${receipt_data['amount']:.2f}")
+                    print(f"📊 Receipt: {receipt_data['merchant']} | {category} | MCC {mcc} | ${receipt_data['amount']:.2f}")
                     
-                    # Get card recommendation with correct MCC
+                    # Get primary recommendation
                     rec_result = client.get_recommendation(
                         user_id=user_id,
                         merchant=receipt_data['merchant'],
-                        category=category,  # Use detected category
+                        category=category,
                         amount=float(receipt_data['amount']),
-                        mcc=mcc  # Use correct MCC
+                        mcc=mcc
                     )
                     
                     if rec_result.get('success'):
                         data = normalize_recommendation_data(rec_result.get('data', {}))
                         
-                        # ✅ Add helpful context for special cases
+                        # ✅ FIX: Generate alternatives if not provided
+                        alternatives = data.get('alternatives', [])
+                        
+                        if not alternatives or len(alternatives) < 2:
+                            # Manually create alternatives based on category
+                            alternatives = generate_card_alternatives(
+                                category=category,
+                                amount=float(receipt_data['amount']),
+                                primary_card=data['recommended_card']
+                            )
+                        
+                        # Add context for special merchants
                         context_note = ""
-                        if "costco" in receipt_data['merchant'].lower():
+                        merchant_lower = receipt_data['merchant'].lower()
+                        
+                        if "costco" in merchant_lower:
                             context_note = """
-            > **💡 Costco Tip:** Costco only accepts Visa cards. Amex and Mastercard are not accepted at warehouse locations.
-            > The Costco Anywhere Visa Card offers 2% cashback at Costco and Costco.com.
-            """
+---
+### 💡 Costco Shopping Tip
+**Accepted Payment:** Costco only accepts **Visa cards** at warehouse locations. Amex and Mastercard are not accepted.
+
+**Best Card:** Costco Anywhere Visa Card offers **2% cashback** at Costco and Costco.com.
+
+---
+"""
+                        elif "whole foods" in merchant_lower or "amazon" in merchant_lower:
+                            context_note = """
+---
+### 💡 Whole Foods Tip
+**Amazon Prime Members:** Get an extra **10% off** sale items at Whole Foods with Prime membership.
+
+**Best Card:** Amazon Prime Visa offers **5% cashback** at Whole Foods for Prime members.
+
+---
+"""
                         
+                        # Build output
                         output = f"""## 📸 Receipt Analysis
             
 ### 🧾 Extracted Information
 - **Merchant:** {receipt_data['merchant']}
 - **Amount:** ${receipt_data['amount']:.2f}
 - **Date:** {receipt_data['date']}
-- **Category:** {receipt_data['category']} (MCC: {mcc})
+- **Category:** {receipt_data['category']}
 
 **Items Purchased:**
 """
                         for item in receipt_data.get('items', []):
                             output += f"- {item}\n"
                         
+                        output += f"\n{context_note}\n"
+                        
                         output += f"""
-            
-            {context_note}
-            
-            ---
-            
-### 💳 Optimal Card Recommendation
+### 💳 Best Card for This Purchase
 
-**Use: {data['recommended_card']}**
+**🏆 {data['recommended_card']}**
 
-- **Rewards Earned:** ${data['rewards_earned']:.2f} ({data['rewards_rate']})
+- **Rewards Earned:** ${data['rewards_earned']:.2f}
+- **Rewards Rate:** {data['rewards_rate']}
 - **Reasoning:** {data['reasoning']}
-- **Annual Potential:** ${data['annual_potential']:.2f}/year
+- **Annual Potential:** ${data['annual_potential']:.2f}/year (based on similar spending)
+
+"""
+                        
+                        # ✅ Always show alternatives
+                        if alternatives and len(alternatives) > 0:
+                            output += """
+### 🔄 Other Card Options
 
 """
+                            for i, alt in enumerate(alternatives[:3], 1):
+                                card_name = alt.get('card', 'Unknown Card')
+                                rewards = alt.get('rewards', 0)
+                                rate = alt.get('rate', '0%')
+                                
+                                output += f"{i}. **{card_name}**\n"
+                                output += f"   - Rewards: ${rewards:.2f} ({rate})\n"
+                                if 'note' in alt:
+                                    output += f"   - Note: {alt['note']}\n"
+                                output += "\n"
                         
-                        if data['warnings']:
-                            output += "### ⚠️ Warnings\n\n"
+                        # Add warnings
+                        if data.get('warnings'):
+                            output += "\n### ⚠️ Important Notices\n\n"
                             for warning in data['warnings']:
                                 output += f"- {warning}\n"
                         
-                        if data.get('alternatives'):
-                            output += "\n### 🔄 Alternative Options\n\n"
-                            for alt in data['alternatives'][:3]:
-                                output += f"- **{alt['card']}:** ${alt['rewards']:.2f} ({alt['rate']})\n"
-                        
-                        # Create chart
+                        # Create comparison chart
                         chart = create_rewards_comparison_chart(data)
                         
                         return output, chart
                     
                     else:
-                        return f"✅ Receipt scanned successfully!\n\n{json.dumps(receipt_data, indent=2)}\n\n❌ Could not get card recommendation.", None
+                        return f"✅ Receipt scanned!\n\n```json\n{json.dumps(receipt_data, indent=2)}\n```\n\n❌ Could not get card recommendation.", None
                 
                 except Exception as e:
                     error_details = traceback.format_exc()