Update app.py

app.py

@@ -3141,12 +3141,11 @@ with gr.Blocks(
                     return "❌ Please upload a receipt image first.", None
                 
                 try:
-                    # Read and encode image
                     import base64
                     with open(image_path, "rb") as image_file:
                         base64_image = base64.b64encode(image_file.read()).decode('utf-8')
                     
-                    # ✅ FEATURE 2: GPT-4 Vision for Receipt Analysis
+                    # ✅ IMPROVED PROMPT with better category detection
                     response = openai_client.chat.completions.create(
                         model="gpt-4o",
                         messages=[{
@@ -3154,22 +3153,39 @@ with gr.Blocks(
                             "content": [
                                 {
                                     "type": "text",
-                                    "text": """Extract the following from this receipt:
-                                    
-        1. Merchant name
-        2. Total amount (just the final total, not itemized)
-        3. Date
-        4. Category (grocery, dining, gas, shopping, etc.)
-        5. Top 3 items purchased (if visible)
-        
-        Return as JSON in this exact format:
-        {
-          "merchant": "Store Name",
-          "amount": 127.50,
-          "date": "2025-01-15",
-          "category": "Groceries",
-          "items": ["Item 1", "Item 2", "Item 3"]
-        }"""
+                                    "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)
+            3. **Date** (format: YYYY-MM-DD, or "Unknown" if not visible)
+            4. **Category** - Choose the MOST SPECIFIC category:
+               - "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"
+               - "Department Store" (Target, Walmart)
+               - "Online Shopping"
+               - "Other"
+            
+            5. **Top 3 items** purchased (if visible)
+            
+            **IMPORTANT RULES:**
+            - 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"
+            
+            Return as JSON:
+            {
+              "merchant": "Store Name",
+              "amount": 127.50,
+              "date": "2025-01-28",
+              "category": "Wholesale Club",
+              "items": ["Item 1", "Item 2", "Item 3"]
+            }"""
                                 },
                                 {
                                     "type": "image_url",
@@ -3185,7 +3201,6 @@ with gr.Blocks(
                     # Parse response
                     receipt_data_str = response.choices[0].message.content
                     
-                    # Extract JSON from response (GPT sometimes adds extra text)
                     import re
                     json_match = re.search(r'\{.*\}', receipt_data_str, re.DOTALL)
                     if json_match:
@@ -3193,50 +3208,88 @@ with gr.Blocks(
                     else:
                         raise ValueError("Could not extract JSON from response")
                     
-                    # Get card recommendation for this transaction
+                    category = receipt_data['category']
+                    
+                    # Map category to correct MCC
+                    category_to_mcc = {
+                        "Wholesale Club": "5300",
+                        "Grocery Store": "5411",
+                        "Supermarket": "5411",
+                        "Convenience Store": "5499",
+                        "Restaurant": "5812",
+                        "Fast Food": "5814",
+                        "Gas Station": "5541",
+                        "Department Store": "5311",
+                        "Online Shopping": "5942"
+                    }
+                    
+                    mcc = category_to_mcc.get(category, MCC_CATEGORIES.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}")
+                    
+                    # Get card recommendation with correct MCC
                     rec_result = client.get_recommendation(
                         user_id=user_id,
                         merchant=receipt_data['merchant'],
-                        category=receipt_data['category'],
+                        category=category,  # Use detected category
                         amount=float(receipt_data['amount']),
-                        mcc=MCC_CATEGORIES.get(receipt_data['category'], "5999")
+                        mcc=mcc  # Use correct MCC
                     )
                     
                     if rec_result.get('success'):
                         data = normalize_recommendation_data(rec_result.get('data', {}))
                         
+                        # ✅ Add helpful context for special cases
+                        context_note = ""
+                        if "costco" in receipt_data['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.
+            """
+                        
                         output = f"""## 📸 Receipt Analysis
-        
-        ### 🧾 Extracted Information
-        - **Merchant:** {receipt_data['merchant']}
-        - **Amount:** ${receipt_data['amount']:.2f}
-        - **Date:** {receipt_data['date']}
-        - **Category:** {receipt_data['category']}
-        
-        **Items Purchased:**
-        """
+            
+### 🧾 Extracted Information
+- **Merchant:** {receipt_data['merchant']}
+- **Amount:** ${receipt_data['amount']:.2f}
+- **Date:** {receipt_data['date']}
+- **Category:** {receipt_data['category']} (MCC: {mcc})
+
+**Items Purchased:**
+"""
                         for item in receipt_data.get('items', []):
                             output += f"- {item}\n"
                         
                         output += f"""
-        
-        ---
-        
-        ### 💳 Optimal Card Recommendation
-        
-        **Use: {data['recommended_card']}**
-        
-        - **Rewards Earned:** ${data['rewards_earned']:.2f} ({data['rewards_rate']})
-        - **Reasoning:** {data['reasoning']}
-        - **Annual Potential:** ${data['annual_potential']:.2f}/year
-        
-        """
+            
+            {context_note}
+            
+            ---
+            
+### 💳 Optimal Card Recommendation
+
+**Use: {data['recommended_card']}**
+
+- **Rewards Earned:** ${data['rewards_earned']:.2f} ({data['rewards_rate']})
+- **Reasoning:** {data['reasoning']}
+- **Annual Potential:** ${data['annual_potential']:.2f}/year
+
+"""
                         
                         if data['warnings']:
                             output += "### ⚠️ Warnings\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
                         chart = create_rewards_comparison_chart(data)