api

app.py

@@ -529,16 +529,10 @@ async def get_customer_analysis(customer_id: str):
 async def get_financial_recommendations(customer_id: str):
     """
     Get hyper-personalized financial recommendations for a customer
-    
-    Parameters:
-    - customer_id: The ID of the customer
-    
-    Returns:
-    - JSON object containing personalized financial recommendations and insights
     """
     try:
         # Validate customer
-        if customer_id not in customer_profiles['Customer_Id'].unique():
+        if customer_id not in customer_profiles['Customer_Id'].values:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
                 detail="Customer not found"
@@ -553,21 +547,37 @@ async def get_financial_recommendations(customer_id: str):
         # Get social sentiment data
         customer_sentiment = customer_Media[customer_Media['Customer_Id'] == customer_id]
         
-        # Calculate financial metrics
-        total_spent = customer_purchases['Amount (In Dollars)'].sum()
-        avg_transaction = customer_purchases['Amount (In Dollars)'].mean()
-        purchase_frequency = len(customer_purchases) / (
-            (customer_purchases['Purchase_Date'].max() - customer_purchases['Purchase_Date'].min()).days + 1
-        )
-        
-        # Denormalize age and income
-        age = customer_profile['Age'] * scaler.scale_[0] + scaler.mean_[0]
-        income = customer_profile['Income per year (in dollars)'] * scaler.scale_[1] + scaler.mean_[1]
-        
-        # Calculate spending ratio
-        spending_ratio = (total_spent / income) * 100 if income > 0 else 0
+        # Calculate financial metrics with type conversion
+        try:
+            total_spent = customer_purchases['Amount (In Dollars)'].astype(float).sum()
+            avg_transaction = customer_purchases['Amount (In Dollars)'].astype(float).mean()
+            
+            # Convert purchase dates to datetime
+            customer_purchases['Purchase_Date'] = pd.to_datetime(customer_purchases['Purchase_Date'])
+            date_range = (customer_purchases['Purchase_Date'].max() - customer_purchases['Purchase_Date'].min()).days
+            purchase_frequency = len(customer_purchases) / (date_range + 1) if date_range > 0 else 0
+            
+        except (ValueError, TypeError) as e:
+            logger.error(f"Error processing numerical calculations: {str(e)}")
+            total_spent = 0
+            avg_transaction = 0
+            purchase_frequency = 0
+        
+        try:
+            # Convert age and income to float
+            age = float(customer_profile['Age'])
+            income = float(customer_profile['Income per year (in dollars)'])
+            
+            # Calculate spending ratio
+            spending_ratio = (total_spent / income) * 100 if income > 0 else 0
+            
+        except (ValueError, TypeError) as e:
+            logger.error(f"Error processing profile data: {str(e)}")
+            age = 0
+            income = 0
+            spending_ratio = 0
         
-        # Generate personalized recommendations
+        # Generate recommendations based on processed data
         recommendations = {
             "investment_recommendations": [],
             "savings_recommendations": [],
@@ -576,67 +586,77 @@ async def get_financial_recommendations(customer_id: str):
             "action_items": []
         }
         
-        # Investment recommendations based on age and income
+        # Investment recommendations based on age
         if age < 30:
-            recommendations["investment_recommendations"].extend([
+            recommendations["investment_recommendations"] = [
                 "Consider starting a retirement account with aggressive growth funds",
                 "Look into low-cost index funds for long-term growth",
                 "Build an emergency fund of 3-6 months expenses"
-            ])
+            ]
         elif age < 50:
-            recommendations["investment_recommendations"].extend([
+            recommendations["investment_recommendations"] = [
                 "Diversify investment portfolio with mix of stocks and bonds",
                 "Consider real estate investment opportunities",
                 "Maximize retirement contributions"
-            ])
+            ]
         else:
-            recommendations["investment_recommendations"].extend([
+            recommendations["investment_recommendations"] = [
                 "Focus on preservation of capital",
                 "Consider dividend-paying stocks",
                 "Review retirement withdrawal strategy"
-            ])
+            ]
         
         # Savings recommendations based on spending ratio
         if spending_ratio > 70:
-            recommendations["savings_recommendations"].extend([
+            recommendations["savings_recommendations"] = [
                 "Critical: Reduce monthly expenses",
                 "Implement 50/30/20 budgeting rule",
                 "Identify and cut non-essential spending"
-            ])
+            ]
         elif spending_ratio > 50:
-            recommendations["savings_recommendations"].extend([
+            recommendations["savings_recommendations"] = [
                 "Look for additional saving opportunities",
                 "Consider automated savings transfers",
                 "Review subscription services"
-            ])
+            ]
         else:
-            recommendations["savings_recommendations"].extend([
+            recommendations["savings_recommendations"] = [
                 "Maintain current saving habits",
                 "Consider increasing investment contributions",
                 "Look into tax-advantaged savings options"
-            ])
+            ]
         
         # Budget recommendations based on purchase patterns
-        category_spending = customer_purchases.groupby('Category')['Amount (In Dollars)'].sum()
-        top_spending_categories = category_spending.nlargest(3)
-        
-        recommendations["budget_recommendations"].extend([
-            f"Highest spending in {cat}: ${amount:.2f}" 
-            for cat, amount in top_spending_categories.items()
-        ])
-        
-        # Risk assessment based on sentiment and spending patterns
-        recent_sentiment = customer_sentiment.sort_values('Timestamp', ascending=False)['Sentiment_Score'].mean()
-        if recent_sentiment < -0.2:
-            risk_level = "Conservative"
-        elif recent_sentiment > 0.2:
-            risk_level = "Moderate"
-        else:
+        try:
+            category_spending = customer_purchases.groupby('Category')['Amount (In Dollars)'].astype(float).sum()
+            top_spending_categories = category_spending.nlargest(3)
+            
+            recommendations["budget_recommendations"] = [
+                f"Highest spending in {cat}: ${amount:.2f}" 
+                for cat, amount in top_spending_categories.items()
+            ]
+        except Exception as e:
+            logger.error(f"Error processing category spending: {str(e)}")
+            recommendations["budget_recommendations"] = ["Unable to process category spending"]
+        
+        # Risk assessment based on sentiment
+        try:
+            recent_sentiment = customer_sentiment['Sentiment_Score'].astype(float).mean()
+            if pd.isna(recent_sentiment):
+                risk_level = "Balanced"
+            elif recent_sentiment < -0.2:
+                risk_level = "Conservative"
+            elif recent_sentiment > 0.2:
+                risk_level = "Moderate"
+            else:
+                risk_level = "Balanced"
+        except Exception as e:
+            logger.error(f"Error processing sentiment: {str(e)}")
             risk_level = "Balanced"
             
         recommendations["risk_assessment"] = f"Based on your profile and behavior: {risk_level} risk tolerance"
         
-        # Generate action items
+        # Action items
         recommendations["action_items"] = [
             {
                 "priority": "High",
@@ -675,12 +695,9 @@ async def get_financial_recommendations(customer_id: str):
             "analysis_timestamp": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
         }
         
-    except HTTPException:
-        raise
     except Exception as e:
         logger.error(f"Error processing financial recommendations for customer {customer_id}: {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=f"Error processing request: {str(e)}"
-        )
-
+        )