api

app.py

@@ -3,12 +3,13 @@ from fastapi import FastAPI, File, HTTPException, status
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 import json
-from typing import List, Dict, Any
+from typing import List, Dict, Any, Optional
 import pandas as pd
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
 from scipy import sparse
-from datetime import datetime
+from datetime import datetime, timedelta
+from statistics import mean
 import warnings
 import os
 import logging
@@ -524,3 +525,256 @@ async def get_customer_analysis(customer_id: str):
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=f"Error processing request: {str(e)}"
         )
+class CardRecommendation(BaseModel):
+    name: str
+    annual_fee: float
+    rewards_rate: float
+    benefits: str
+
+class InvestmentRecommendation(BaseModel):
+    type: str
+    risk_level: str
+    expected_return: float
+    min_investment: float
+
+class RealEstateRecommendation(BaseModel):
+    type: str
+    location: str
+    price: float
+    monthly_payment: float
+    roi_potential: float
+
+class FinancialAnalysisResponse(BaseModel):
+    monthly_avg_spend: float
+    spend_trend: float
+    credit_score_range: str
+    credit_score_change: str
+    investment_potential: float
+    spending_categories: List[Dict[str, float]]
+    spending_insights: List[str]
+    recent_transactions: List[Dict[str, Any]]
+    card_recommendations: List[CardRecommendation]
+    investment_recommendations: List[InvestmentRecommendation]
+    real_estate_recommendations: List[RealEstateRecommendation]
+    financial_health_score: int
+    action_items: List[str]
+
+@app.get("/financial-analysis/{customer_id}", response_model=FinancialAnalysisResponse)
+async def get_financial_analysis(customer_id: str):
+    """
+    Get comprehensive financial analysis and recommendations for a customer
+    
+    Parameters:
+    - customer_id: The ID of the customer
+    
+    Returns:
+    - JSON object containing financial analysis and personalized recommendations
+    """
+    try:
+        # Validate customer
+        if customer_id not in purchase_history['Customer_Id'].unique():
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Customer not found"
+            )
+
+        # Get customer profile
+        customer_profile = customer_profiles[customer_profiles['Customer_Id'] == customer_id].iloc[0]
+        customer_transactions = purchase_history[purchase_history['Customer_Id'] == customer_id]
+
+        # Calculate basic financial metrics
+        current_month_spending = customer_transactions[
+            customer_transactions['Purchase_Date'] >= datetime.now() - timedelta(days=30)
+        ]['Amount (In Dollars)'].sum()
+
+        previous_month_spending = customer_transactions[
+            (customer_transactions['Purchase_Date'] >= datetime.now() - timedelta(days=60)) &
+            (customer_transactions['Purchase_Date'] < datetime.now() - timedelta(days=30))
+        ]['Amount (In Dollars)'].sum()
+
+        monthly_avg_spend = customer_transactions.groupby(
+            customer_transactions['Purchase_Date'].dt.to_period('M')
+        )['Amount (In Dollars)'].mean().mean()
+
+        # Calculate spend trend
+        spend_trend = ((current_month_spending - previous_month_spending) / previous_month_spending * 100) if previous_month_spending > 0 else 0
+
+        # Calculate spending categories
+        spending_categories = customer_transactions.groupby('Category')['Amount (In Dollars)'].sum().reset_index()
+        spending_categories_list = [
+            {"category": row['Category'], "amount": float(row['Amount (In Dollars)'])}
+            for _, row in spending_categories.iterrows()
+        ]
+
+        # Generate spending insights
+        spending_insights = []
+        
+        # Category-based insights
+        for category in spending_categories.itertuples():
+            category_avg = category.Amount / len(customer_transactions['Purchase_Date'].dt.to_period('M').unique())
+            if category_avg > monthly_avg_spend * 0.3:
+                spending_insights.append(f"High spending in {category.Category}: ${category_avg:.2f}/month")
+
+        # Age-based recommendations
+        age = customer_profile['Age']
+        income = customer_profile['Income per year (in dollars)']
+
+        # Determine credit score range (simulated based on age and income)
+        base_score = min(max((age * 10 + income / 1000) / 2, 300), 850)
+        credit_score_range = f"{int(base_score-25)}-{int(base_score+25)}"
+        credit_score_change = "↑ improving" if age > 25 and income > 50000 else "stable"
+
+        # Calculate investment potential (simplified)
+        monthly_income = income / 12
+        investment_potential = max(0, monthly_income - monthly_avg_spend * 1.2)
+
+        # Generate recommendations based on age and income
+        card_recommendations = []
+        investment_recommendations = []
+        real_estate_recommendations = []
+
+        # Credit Card Recommendations
+        if income < 50000:
+            card_recommendations.append(CardRecommendation(
+                name="Cash Back Starter Card",
+                annual_fee=0,
+                rewards_rate=1.5,
+                benefits="No annual fee, 1.5% cash back on all purchases"
+            ))
+        elif income < 100000:
+            card_recommendations.append(CardRecommendation(
+                name="Premium Rewards Card",
+                annual_fee=95,
+                rewards_rate=2.5,
+                benefits="Travel insurance, cash back on all purchases, airport lounge access"
+            ))
+        else:
+            card_recommendations.append(CardRecommendation(
+                name="Elite Travel Card",
+                annual_fee=495,
+                rewards_rate=3.0,
+                benefits="Comprehensive travel benefits, concierge service, premium insurance"
+            ))
+
+        # Investment Recommendations
+        if age < 30:
+            investment_recommendations.extend([
+                InvestmentRecommendation(
+                    type="Index Fund",
+                    risk_level="High",
+                    expected_return=10.0,
+                    min_investment=1000.0
+                ),
+                InvestmentRecommendation(
+                    type="Tech Growth ETF",
+                    risk_level="High",
+                    expected_return=12.0,
+                    min_investment=2000.0
+                )
+            ])
+        elif age < 50:
+            investment_recommendations.extend([
+                InvestmentRecommendation(
+                    type="Balanced Fund",
+                    risk_level="Medium",
+                    expected_return=8.0,
+                    min_investment=5000.0
+                ),
+                InvestmentRecommendation(
+                    type="Dividend Growth Stocks",
+                    risk_level="Medium",
+                    expected_return=7.0,
+                    min_investment=10000.0
+                )
+            ])
+        else:
+            investment_recommendations.extend([
+                InvestmentRecommendation(
+                    type="Bond Fund",
+                    risk_level="Low",
+                    expected_return=5.0,
+                    min_investment=10000.0
+                ),
+                InvestmentRecommendation(
+                    type="Income Fund",
+                    risk_level="Low",
+                    expected_return=4.0,
+                    min_investment=25000.0
+                )
+            ])
+
+        # Real Estate Recommendations
+        if income > 75000:
+            mortgage_capacity = (income * 4) * 0.8  # 80% of 4x annual income
+            monthly_payment = (mortgage_capacity * 0.05) / 12  # Simplified mortgage calculation
+
+            real_estate_recommendations.extend([
+                RealEstateRecommendation(
+                    type="Starter Home",
+                    location="Suburban Area",
+                    price=mortgage_capacity,
+                    monthly_payment=monthly_payment,
+                    roi_potential=5.0
+                )
+            ])
+
+            if income > 150000:
+                real_estate_recommendations.append(
+                    RealEstateRecommendation(
+                        type="Investment Property",
+                        location="Urban Center",
+                        price=mortgage_capacity * 0.7,
+                        monthly_payment=monthly_payment * 0.7,
+                        roi_potential=8.0
+                    )
+                )
+
+        # Calculate financial health score
+        savings_ratio = max(0, min(1, (income - monthly_avg_spend * 12) / income))
+        diversity_score = len(spending_categories) / 10
+        stability_score = min(1, len(customer_transactions) / 100)
+        
+        financial_health_score = int((savings_ratio * 0.4 + diversity_score * 0.3 + stability_score * 0.3) * 100)
+
+        # Generate action items
+        action_items = []
+        if savings_ratio < 0.2:
+            action_items.append("Increase monthly savings to at least 20% of income")
+        if monthly_avg_spend > monthly_income * 0.7:
+            action_items.append("Review monthly expenses to reduce spending")
+        if len(investment_recommendations) > 0:
+            action_items.append(f"Consider investing in {investment_recommendations[0].type}")
+
+        # Get recent transactions
+        recent_transactions = [
+            {
+                "date": row['Purchase_Date'].strftime('%Y-%m-%d'),
+                "amount": float(row['Amount (In Dollars)'])
+            }
+            for _, row in customer_transactions.sort_values('Purchase_Date', ascending=False).head(10).iterrows()
+        ]
+
+        return {
+            "monthly_avg_spend": float(monthly_avg_spend),
+            "spend_trend": float(spend_trend),
+            "credit_score_range": credit_score_range,
+            "credit_score_change": credit_score_change,
+            "investment_potential": float(investment_potential),
+            "spending_categories": spending_categories_list,
+            "spending_insights": spending_insights,
+            "recent_transactions": recent_transactions,
+            "card_recommendations": card_recommendations,
+            "investment_recommendations": investment_recommendations,
+            "real_estate_recommendations": real_estate_recommendations,
+            "financial_health_score": financial_health_score,
+            "action_items": action_items
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error processing financial analysis for customer {customer_id}: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Error processing request: {str(e)}"
+        )