Update app.py

app.py

@@ -188,7 +188,7 @@ def get_recommendation_with_agent(user_id, merchant, category, amount):
         
         print(f"🔍 KEYS: {list(result.keys())}")
         
-        # ✅ FIX: Data is at top level, not nested
+        # Extract data
         card_id = result.get('recommended_card', 'Unknown')
         rewards_earned = float(result.get('rewards_earned', 0))
         rewards_rate = result.get('rewards_rate', 'N/A')
@@ -207,8 +207,183 @@ def get_recommendation_with_agent(user_id, merchant, category, amount):
         }
         card_name = card_name_map.get(card_id, card_id.replace('c_', '').replace('_', ' ').title())
         
+        # Extract card-specific details from API
+        card_details = result.get('card_details', {})
+        reward_rate_value = card_details.get('reward_rate', 0)  # e.g., 5 for 5%
+        monthly_cap = card_details.get('monthly_cap', None)  # e.g., 500
+        annual_cap = card_details.get('annual_cap', None)
+        base_rate = card_details.get('base_rate', 1)  # Fallback rate after cap
+        annual_fee = card_details.get('annual_fee', 0)
+        
         print(f"✅ PARSED: {card_name}, ${rewards_earned}, {rewards_rate}, {confidence}")
         
+        # ========== DYNAMIC CALCULATION ==========
+        
+        # Get actual potential savings and score from API
+        potential_savings = annual_impact.get('potential_savings', 0)
+        optimization_score = annual_impact.get('optimization_score', 0)
+        
+        # Calculate annual projection dynamically
+        amount_float = float(amount)
+        
+        # Determine frequency assumption (you can make this smarter based on category)
+        frequency_map = {
+            'Groceries': 52,  # Weekly
+            'Restaurants': 52,  # Weekly
+            'Gas Stations': 52,  # Weekly
+            'Fast Food': 52,  # Weekly
+            'Airlines': 4,  # Quarterly
+            'Hotels': 12,  # Monthly
+            'Online Shopping': 24,  # Bi-weekly
+            'Entertainment': 24,  # Bi-weekly
+        }
+        frequency = frequency_map.get(category, 26)  # Default: bi-weekly
+        frequency_label = {
+            52: 'weekly',
+            26: 'bi-weekly',
+            24: 'bi-weekly',
+            12: 'monthly',
+            4: 'quarterly'
+        }.get(frequency, f'{frequency}x per year')
+        
+        annual_spend = amount_float * frequency
+        
+        # Calculate rewards based on card structure
+        if monthly_cap:
+            # Card has monthly cap (e.g., Citi Custom Cash: $500/month at 5%)
+            monthly_cap_annual = monthly_cap * 12
+            
+            if annual_spend <= monthly_cap_annual:
+                # All spending is within cap
+                high_rate_spend = annual_spend
+                low_rate_spend = 0
+            else:
+                # Some spending exceeds cap
+                high_rate_spend = monthly_cap_annual
+                low_rate_spend = annual_spend - monthly_cap_annual
+            
+            high_rate_rewards = high_rate_spend * (reward_rate_value / 100)
+            low_rate_rewards = low_rate_spend * (base_rate / 100)
+            total_rewards = high_rate_rewards + low_rate_rewards
+            
+            # Baseline comparison (1% card)
+            baseline_rewards = annual_spend * 0.01
+            net_benefit = total_rewards - baseline_rewards - annual_fee
+            
+            # Build calculation table
+            calc_table = f"""
+| Spending Tier | Annual Amount | Rate | Rewards |
+|---------------|---------------|------|---------|
+| First ${monthly_cap}/month | ${high_rate_spend:.2f} | {reward_rate_value}% | ${high_rate_rewards:.2f} |
+| Remaining spend | ${low_rate_spend:.2f} | {base_rate}% | ${low_rate_rewards:.2f} |
+| **Subtotal** | **${annual_spend:.2f}** | - | **${total_rewards:.2f}** |
+| Annual fee | - | - | -${annual_fee:.2f} |
+| **Net Rewards** | - | - | **${total_rewards - annual_fee:.2f}** |
+"""
+            
+            comparison_text = f"""
+**With {card_name}:**
+- High rate earnings: ${high_rate_rewards:.2f}
+- Base rate earnings: ${low_rate_rewards:.2f}
+- Annual fee: -${annual_fee:.2f}
+- **Net total: ${total_rewards - annual_fee:.2f}/year**
+
+**With Baseline 1% Card:**
+- All spending at 1%: ${baseline_rewards:.2f}/year
+
+**Net Benefit: ${net_benefit:.2f}/year**
+"""
+        
+        elif annual_cap:
+            # Card has annual cap (e.g., Amex Gold: $25,000/year at 4x)
+            if annual_spend <= annual_cap:
+                high_rate_spend = annual_spend
+                low_rate_spend = 0
+            else:
+                high_rate_spend = annual_cap
+                low_rate_spend = annual_spend - annual_cap
+            
+            high_rate_rewards = high_rate_spend * (reward_rate_value / 100)
+            low_rate_rewards = low_rate_spend * (base_rate / 100)
+            total_rewards = high_rate_rewards + low_rate_rewards
+            
+            baseline_rewards = annual_spend * 0.01
+            net_benefit = total_rewards - baseline_rewards - annual_fee
+            
+            calc_table = f"""
+| Spending Tier | Annual Amount | Rate | Rewards |
+|---------------|---------------|------|---------|
+| Up to ${annual_cap:,.0f}/year | ${high_rate_spend:.2f} | {reward_rate_value}% | ${high_rate_rewards:.2f} |
+| Above cap | ${low_rate_spend:.2f} | {base_rate}% | ${low_rate_rewards:.2f} |
+| **Subtotal** | **${annual_spend:.2f}** | - | **${total_rewards:.2f}** |
+| Annual fee | - | - | -${annual_fee:.2f} |
+| **Net Rewards** | - | - | **${total_rewards - annual_fee:.2f}** |
+"""
+            
+            comparison_text = f"""
+**With {card_name}:**
+- High rate earnings: ${high_rate_rewards:.2f}
+- Base rate earnings: ${low_rate_rewards:.2f}
+- Annual fee: -${annual_fee:.2f}
+- **Net total: ${total_rewards - annual_fee:.2f}/year**
+
+**With Baseline 1% Card:**
+- All spending at 1%: ${baseline_rewards:.2f}/year
+
+**Net Benefit: ${net_benefit:.2f}/year**
+"""
+        
+        else:
+            # No cap - flat rate on all spending
+            total_rewards = annual_spend * (reward_rate_value / 100)
+            baseline_rewards = annual_spend * 0.01
+            net_benefit = total_rewards - baseline_rewards - annual_fee
+            
+            calc_table = f"""
+| Spending Tier | Annual Amount | Rate | Rewards |
+|---------------|---------------|------|---------|
+| All spending | ${annual_spend:.2f} | {reward_rate_value}% | ${total_rewards:.2f} |
+| Annual fee | - | - | -${annual_fee:.2f} |
+| **Net Rewards** | - | - | **${total_rewards - annual_fee:.2f}** |
+"""
+            
+            comparison_text = f"""
+**With {card_name}:**
+- Earnings at {reward_rate_value}%: ${total_rewards:.2f}
+- Annual fee: -${annual_fee:.2f}
+- **Net total: ${total_rewards - annual_fee:.2f}/year**
+
+**With Baseline 1% Card:**
+- All spending at 1%: ${baseline_rewards:.2f}/year
+
+**Net Benefit: ${net_benefit:.2f}/year**
+"""
+        
+        # Dynamic optimization score breakdown
+        score_breakdown = annual_impact.get('score_breakdown', {})
+        if not score_breakdown:
+            # Generate based on optimization_score
+            score_breakdown = {
+                'reward_rate': min(30, int(optimization_score * 0.3)),
+                'cap_availability': min(25, int(optimization_score * 0.25)),
+                'annual_fee': min(20, int(optimization_score * 0.2)),
+                'category_match': min(20, int(optimization_score * 0.2)),
+                'penalties': max(-5, int((optimization_score - 100) * 0.05))
+            }
+        
+        score_details = f"""
+**Score Components:**
+- {"✅" if score_breakdown.get('reward_rate', 0) > 20 else "⚠️"} Reward rate: **+{score_breakdown.get('reward_rate', 0)} points**
+- {"✅" if score_breakdown.get('cap_availability', 0) > 15 else "⚠️"} Cap availability: **+{score_breakdown.get('cap_availability', 0)} points**
+- {"✅" if score_breakdown.get('annual_fee', 0) > 15 else "⚠️"} Annual fee value: **+{score_breakdown.get('annual_fee', 0)} points**
+- {"✅" if score_breakdown.get('category_match', 0) > 15 else "⚠️"} Category match: **+{score_breakdown.get('category_match', 0)} points**
+- {"⚠️" if score_breakdown.get('penalties', 0) < 0 else "✅"} Limitations: **{score_breakdown.get('penalties', 0)} points**
+
+**Total: {optimization_score}/100**
+"""
+        
+        # ========== FORMAT OUTPUT ==========
+        
         output = f"""## 🤖 AI Agent Recommendation
 
 ### 💳 Recommended Card: **{card_name}**
@@ -225,6 +400,7 @@ def get_recommendation_with_agent(user_id, merchant, category, amount):
 ---
 """
         
+        # Add alternatives
         if alternatives:
             output += "\n### 🔄 Alternative Options:\n\n"
             for alt in alternatives[:3]:
@@ -233,25 +409,74 @@ def get_recommendation_with_agent(user_id, merchant, category, amount):
                 alt_reason = alt.get('reason', '')
                 output += f"**{alt_card_name}:**\n{alt_reason}\n\n"
         
+        # Add warnings
         if warnings:
             output += "\n### ⚠️ Important Warnings:\n\n"
             for warning in warnings:
                 output += f"- {warning}\n"
         
+        # Add dynamic annual impact
         if annual_impact:
-            potential_savings = annual_impact.get('potential_savings', 0)
-            optimization_score = annual_impact.get('optimization_score', 0)
-            output += f"\n### 💰 Annual Impact:\n"
-            output += f"- **Potential Savings:** ${potential_savings:.2f}/year\n"
-            output += f"- **Optimization Score:** {optimization_score}/100\n"
-        
-        output += f"""
+            output += f"""
+### 💰 Annual Impact
+
+- **Potential Savings:** ${potential_savings:.2f}/year
+- **Optimization Score:** {optimization_score}/100
+
+<details>
+<summary>📊 <b>How is this calculated?</b> (Click to expand)</summary>
+
+#### 💡 Calculation Assumptions:
+
+**Step 1: Estimate Annual Spending**
+
+Current transaction: amountfloat:.2fatmerchantCategory:categoryFrequencyassumption:frequencylabel.capitalize()Annualestimate:{amount_float:.2f} at {merchant}
+Category: {category}
+Frequency assumption: {frequency_label.capitalize()}
+Annual estimate: amountf​loat:.2fatmerchantCategory:categoryFrequencyassumption:frequencyl​abel.capitalize()Annualestimate:{amount_float:.2f} × {frequency} = ${annual_spend:.2f}
+**Step 2: Calculate Rewards with {card_name}**
+
+{calc_table}
+
+**Step 3: Compare to Baseline**
+
+{comparison_text}
+
+---
+
+#### 📈 Optimization Score: {optimization_score}/100
+
+{score_details}
+
+**Score Ranges:**
+- 90-100: Optimal choice ✅
+- 80-89: Great choice 👍
+- 70-79: Good choice 👌
+- 60-69: Acceptable ⚠️
+- <60: Suboptimal ❌
+
 ---
 
+#### 🔍 Card Details:
+- **Reward Rate:** {reward_rate_value}% on {category}
+- **Monthly Cap:** {"$" + str(monthly_cap) if monthly_cap else "None"}
+- **Annual Cap:** {"$" + str(annual_cap) if annual_cap else "None"}
+- **Base Rate:** {base_rate}% (after cap)
+- **Annual Fee:** ${annual_fee}
+
+</details>
+
+---
+"""
+        
+        # Add transaction details
+        output += f"""
 ### 📊 Transaction Details:
-- **Amount:** ${amount:.2f}
+
+- **Amount:** ${amount_float:.2f}
 - **Merchant:** {merchant}
 - **Category:** {category}
+- **MCC Code:** {transaction['mcc']}
 """
         
         chart = create_agent_recommendation_chart_enhanced(result)