Update app.py

app.py

@@ -34,12 +34,13 @@ COLORS = {
     'indigo': '#6366f1'
 }
 
-plt.style.use('seaborn-v0_8-whitegrid')
+plt.style.use('default')  # Changed from seaborn-v0_8-whitegrid for compatibility
 sns.set_palette("husl")
 
 class B2BCustomerAnalytics:
     def __init__(self):
         self.df = None
+        self.processed_df = None
         self.model = None
         self.feature_importance = None
         self.predictions = None
@@ -50,188 +51,244 @@ class B2BCustomerAnalytics:
             if file is None:
                 return "Please upload a CSV file", None, None, None
             
+            # Load raw data
             self.df = pd.read_csv(file.name)
             
+            # Check for required columns - be flexible with column names
             required_columns = ['customer_id', 'order_date', 'amount']
-            missing_cols = [col for col in required_columns if col not in self.df.columns]
-            if missing_cols:
-                return f"Missing required columns: {missing_cols}", None, None, None
+            df_columns_lower = [col.lower() for col in self.df.columns]
             
+            # Map common variations
+            column_mapping = {}
+            for req_col in required_columns:
+                found = False
+                for df_col in self.df.columns:
+                    if req_col in df_col.lower() or df_col.lower() in req_col:
+                        column_mapping[req_col] = df_col
+                        found = True
+                        break
+                if not found:
+                    return f"Missing required column: {req_col}. Available columns: {list(self.df.columns)}", None, None, None
+            
+            # Rename columns to standard names
+            self.df = self.df.rename(columns=column_mapping)
+            
+            # Convert order_date to datetime
             self.df['order_date'] = pd.to_datetime(self.df['order_date'])
             
-            if 'recency_days' not in self.df.columns or 'frequency' not in self.df.columns or 'monetary' not in self.df.columns:
-                self.df = self.calculate_rfm_metrics(self.df)
+            # Calculate RFM metrics
+            self.processed_df = self.calculate_rfm_metrics(self.df.copy())
             
-            self.df = self.perform_customer_segmentation(self.df)
+            # Perform customer segmentation
+            self.processed_df = self.perform_customer_segmentation(self.processed_df)
             
+            # Generate summary
             summary_html, kpi_cards = self.generate_summary_dashboard()
             
-            return "Data loaded successfully!", summary_html, self.df.head(20), kpi_cards
+            return "Data loaded successfully!", summary_html, self.processed_df.head(20), kpi_cards
             
         except Exception as e:
             return f"Error loading data: {str(e)}", None, None, None
     
     def calculate_rfm_metrics(self, df):
         """Calculate RFM metrics from transaction data"""
-        current_date = df['order_date'].max() + timedelta(days=1)
-        
-        customer_metrics = df.groupby('customer_id').agg({
-            'order_date': ['max', 'count'],
-            'amount': ['sum', 'mean']
-        }).round(2)
-        
-        customer_metrics.columns = ['last_order_date', 'frequency', 'monetary', 'avg_order_value']
-        customer_metrics['recency_days'] = (current_date - customer_metrics['last_order_date']).dt.days
-        
-        df_with_rfm = df.merge(customer_metrics[['recency_days', 'frequency', 'monetary']], 
-                               left_on='customer_id', right_index=True, how='left')
-        
-        return df_with_rfm
+        try:
+            current_date = df['order_date'].max() + timedelta(days=1)
+            
+            # Calculate customer-level metrics
+            customer_metrics = df.groupby('customer_id').agg({
+                'order_date': ['max', 'count'],
+                'amount': ['sum', 'mean']
+            }).round(2)
+            
+            # Flatten column names
+            customer_metrics.columns = ['last_order_date', 'frequency', 'monetary', 'avg_order_value']
+            customer_metrics['recency_days'] = (current_date - customer_metrics['last_order_date']).dt.days
+            
+            # Merge back with original data
+            df_with_rfm = df.merge(
+                customer_metrics[['recency_days', 'frequency', 'monetary']], 
+                left_on='customer_id', 
+                right_index=True, 
+                how='left'
+            )
+            
+            return df_with_rfm
+            
+        except Exception as e:
+            print(f"Error in calculate_rfm_metrics: {e}")
+            return df
     
     def perform_customer_segmentation(self, df):
         """Perform customer segmentation based on RFM analysis"""
-        customer_df = df.groupby('customer_id').agg({
-            'recency_days': 'first',
-            'frequency': 'first', 
-            'monetary': 'first'
-        }).reset_index()
-        
-        customer_df['R_Score'] = pd.qcut(customer_df['recency_days'].rank(method='first'), 5, labels=[5,4,3,2,1])
-        customer_df['F_Score'] = pd.qcut(customer_df['frequency'].rank(method='first'), 5, labels=[1,2,3,4,5])
-        customer_df['M_Score'] = pd.qcut(customer_df['monetary'].rank(method='first'), 5, labels=[1,2,3,4,5])
-        
-        customer_df['R_Score'] = customer_df['R_Score'].astype(int)
-        customer_df['F_Score'] = customer_df['F_Score'].astype(int)
-        customer_df['M_Score'] = customer_df['M_Score'].astype(int)
-        
-        def segment_customers(row):
-            if row['R_Score'] >= 4 and row['F_Score'] >= 4 and row['M_Score'] >= 4:
-                return 'Champions'
-            elif row['R_Score'] >= 3 and row['F_Score'] >= 3 and row['M_Score'] >= 3:
-                return 'Loyal Customers'
-            elif row['R_Score'] >= 3 and row['F_Score'] >= 2:
-                return 'Potential Loyalists'
-            elif row['R_Score'] >= 4 and row['F_Score'] <= 2:
-                return 'New Customers'
-            elif row['R_Score'] <= 2 and row['F_Score'] >= 3:
-                return 'At Risk'
-            elif row['R_Score'] <= 2 and row['F_Score'] <= 2 and row['M_Score'] >= 3:
-                return 'Cannot Lose Them'
-            elif row['R_Score'] <= 2 and row['F_Score'] <= 2 and row['M_Score'] <= 2:
-                return 'Lost Customers'
-            else:
-                return 'Others'
-        
-        customer_df['Segment'] = customer_df.apply(segment_customers, axis=1)
-        
-        customer_df['Churn_Risk'] = customer_df.apply(lambda x: 
-            'High' if x['Segment'] in ['Lost Customers', 'At Risk'] else
-            'Medium' if x['Segment'] in ['Others', 'Cannot Lose Them'] else 'Low', axis=1)
-        
-        segment_data = customer_df[['customer_id', 'Segment', 'Churn_Risk', 'R_Score', 'F_Score', 'M_Score']]
-        df_with_segments = df.merge(segment_data, on='customer_id', how='left')
-        
-        return df_with_segments
+        try:
+            # Get unique customer data
+            customer_df = df.groupby('customer_id').agg({
+                'recency_days': 'first',
+                'frequency': 'first', 
+                'monetary': 'first'
+            }).reset_index()
+            
+            # Calculate RFM scores using quantiles
+            try:
+                customer_df['R_Score'] = pd.qcut(customer_df['recency_days'].rank(method='first'), 5, labels=[5,4,3,2,1], duplicates='drop')
+                customer_df['F_Score'] = pd.qcut(customer_df['frequency'].rank(method='first'), 5, labels=[1,2,3,4,5], duplicates='drop')
+                customer_df['M_Score'] = pd.qcut(customer_df['monetary'].rank(method='first'), 5, labels=[1,2,3,4,5], duplicates='drop')
+            except ValueError:
+                # If qcut fails due to duplicate values, use simple binning
+                customer_df['R_Score'] = pd.cut(customer_df['recency_days'], 5, labels=[5,4,3,2,1])
+                customer_df['F_Score'] = pd.cut(customer_df['frequency'], 5, labels=[1,2,3,4,5])
+                customer_df['M_Score'] = pd.cut(customer_df['monetary'], 5, labels=[1,2,3,4,5])
+            
+            customer_df['R_Score'] = customer_df['R_Score'].astype(int)
+            customer_df['F_Score'] = customer_df['F_Score'].astype(int)
+            customer_df['M_Score'] = customer_df['M_Score'].astype(int)
+            
+            def segment_customers(row):
+                if row['R_Score'] >= 4 and row['F_Score'] >= 4 and row['M_Score'] >= 4:
+                    return 'Champions'
+                elif row['R_Score'] >= 3 and row['F_Score'] >= 3 and row['M_Score'] >= 3:
+                    return 'Loyal Customers'
+                elif row['R_Score'] >= 3 and row['F_Score'] >= 2:
+                    return 'Potential Loyalists'
+                elif row['R_Score'] >= 4 and row['F_Score'] <= 2:
+                    return 'New Customers'
+                elif row['R_Score'] <= 2 and row['F_Score'] >= 3:
+                    return 'At Risk'
+                elif row['R_Score'] <= 2 and row['F_Score'] <= 2 and row['M_Score'] >= 3:
+                    return 'Cannot Lose Them'
+                elif row['R_Score'] <= 2 and row['F_Score'] <= 2 and row['M_Score'] <= 2:
+                    return 'Lost Customers'
+                else:
+                    return 'Others'
+            
+            customer_df['Segment'] = customer_df.apply(segment_customers, axis=1)
+            
+            customer_df['Churn_Risk'] = customer_df.apply(lambda x: 
+                'High' if x['Segment'] in ['Lost Customers', 'At Risk'] else
+                'Medium' if x['Segment'] in ['Others', 'Cannot Lose Them'] else 'Low', axis=1)
+            
+            # Merge segmentation data back
+            segment_data = customer_df[['customer_id', 'Segment', 'Churn_Risk', 'R_Score', 'F_Score', 'M_Score']]
+            df_with_segments = df.merge(segment_data, on='customer_id', how='left')
+            
+            return df_with_segments
+            
+        except Exception as e:
+            print(f"Error in perform_customer_segmentation: {e}")
+            # Return original df with dummy segments if segmentation fails
+            df['Segment'] = 'Others'
+            df['Churn_Risk'] = 'Medium'
+            df['R_Score'] = 3
+            df['F_Score'] = 3 
+            df['M_Score'] = 3
+            return df
     
     def generate_summary_dashboard(self):
         """Generate modern dashboard summary with KPI cards"""
-        if self.df is None:
+        if self.processed_df is None:
             return "No data loaded", ""
         
-        total_customers = self.df['customer_id'].nunique()
-        total_orders = len(self.df)
-        total_revenue = self.df['amount'].sum()
-        avg_order_value = self.df['amount'].mean()
-        
-        segment_dist = self.df.groupby('customer_id')['Segment'].first().value_counts()
-        risk_dist = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts()
-        
-        # Create modern horizontal dashboard
-        summary_html = f"""
-        <div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 2rem; border-radius: 1rem; color: white; margin-bottom: 2rem; text-align: center;">
-            <h1 style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">
-                B2B Customer Analytics Platform
-            </h1>
-            <p style="font-size: 1.2rem; opacity: 0.9;">
-                Enterprise Customer Health Monitoring & Churn Prediction System
-            </p>
-        </div>
-        
-        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1.5rem; margin-bottom: 3rem;">
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #3b82f6;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #dbeafe; border-radius: 0.5rem; color: #1d4ed8;">📊</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #3b82f6;">{total_customers:,}</span>
-                </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Total Customers</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Active enterprise clients</p>
-            </div>
+        try:
+            total_customers = self.processed_df['customer_id'].nunique()
+            total_orders = len(self.processed_df)
+            total_revenue = self.processed_df['amount'].sum()
+            avg_order_value = self.processed_df['amount'].mean()
             
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #10b981;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #d1fae5; border-radius: 0.5rem; color: #047857;">💰</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #10b981;">${(total_revenue/1000000):.1f}M</span>
-                </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Total Revenue</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Contract value sum</p>
-            </div>
+            # Get segment and risk distributions
+            segment_dist = self.processed_df.groupby('customer_id')['Segment'].first().value_counts()
+            risk_dist = self.processed_df.groupby('customer_id')['Churn_Risk'].first().value_counts()
             
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #8b5cf6;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #ede9fe; border-radius: 0.5rem; color: #7c3aed;">📈</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #8b5cf6;">${avg_order_value:.0f}</span>
-                </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Avg Order Value</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Per order average</p>
+            # Create modern horizontal dashboard
+            summary_html = f"""
+            <div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 2rem; border-radius: 1rem; color: white; margin-bottom: 2rem; text-align: center;">
+                <h1 style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">
+                    B2B Customer Analytics Platform
+                </h1>
+                <p style="font-size: 1.2rem; opacity: 0.9;">
+                    Enterprise Customer Health Monitoring & Churn Prediction System
+                </p>
             </div>
             
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #ef4444;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #fee2e2; border-radius: 0.5rem; color: #dc2626;">🚨</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #ef4444;">{risk_dist.get('High', 0)}</span>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1.5rem; margin-bottom: 3rem;">
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #3b82f6;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #dbeafe; border-radius: 0.5rem; color: #1d4ed8;">📊</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #3b82f6;">{total_customers:,}</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Total Customers</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Active enterprise clients</p>
                 </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">High Risk Clients</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Need immediate attention</p>
-            </div>
-            
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #f59e0b;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #fef3c7; border-radius: 0.5rem; color: #d97706;">🏆</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #f59e0b;">{segment_dist.get('Champions', 0)}</span>
+                
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #10b981;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #d1fae5; border-radius: 0.5rem; color: #047857;">💰</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #10b981;">${(total_revenue/1000000):.1f}M</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Total Revenue</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Contract value sum</p>
                 </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Champion Customers</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Top tier clients</p>
-            </div>
-            
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #06b6d4;">
-                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #cffafe; border-radius: 0.5rem; color: #0891b2;">✅</div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #06b6d4;">{risk_dist.get('Low', 0)}</span>
+                
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #8b5cf6;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #ede9fe; border-radius: 0.5rem; color: #7c3aed;">📈</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #8b5cf6;">${avg_order_value:.0f}</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Avg Order Value</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Per order average</p>
+                </div>
+                
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #ef4444;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #fee2e2; border-radius: 0.5rem; color: #dc2626;">🚨</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #ef4444;">{risk_dist.get('High', 0)}</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">High Risk Clients</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Need immediate attention</p>
+                </div>
+                
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #f59e0b;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #fef3c7; border-radius: 0.5rem; color: #d97706;">🏆</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #f59e0b;">{segment_dist.get('Champions', 0)}</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Champion Customers</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Top tier clients</p>
+                </div>
+                
+                <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #06b6d4;">
+                    <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                        <div style="padding: 0.75rem; background: #cffafe; border-radius: 0.5rem; color: #0891b2;">✅</div>
+                        <span style="font-size: 2rem; font-weight: bold; color: #06b6d4;">{risk_dist.get('Low', 0)}</span>
+                    </div>
+                    <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Healthy Customers</h3>
+                    <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Low churn risk</p>
                 </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin: 0;">Healthy Customers</h3>
-                <p style="color: #6b7280; font-size: 0.875rem; margin: 0.25rem 0 0 0;">Low churn risk</p>
             </div>
-        </div>
-        """
-        
-        kpi_data = [
-            ["Total Customers", f"{total_customers:,}", "👥", "#3b82f6"],
-            ["Total Revenue", f"${total_revenue/1000000:.1f}M", "💰", "#10b981"], 
-            ["Avg Order Value", f"${avg_order_value:.0f}", "📈", "#8b5cf6"],
-            ["High Risk Customers", f"{risk_dist.get('High', 0)}", "🚨", "#ef4444"],
-            ["Champion Customers", f"{segment_dist.get('Champions', 0)}", "🏆", "#f59e0b"],
-            ["Healthy Customers", f"{risk_dist.get('Low', 0)}", "✅", "#06b6d4"]
-        ]
-        
-        return summary_html, kpi_data
+            """
+            
+            kpi_data = [
+                ["Total Customers", f"{total_customers:,}", "👥", "#3b82f6"],
+                ["Total Revenue", f"${total_revenue/1000000:.1f}M", "💰", "#10b981"], 
+                ["Avg Order Value", f"${avg_order_value:.0f}", "📈", "#8b5cf6"],
+                ["High Risk Customers", f"{risk_dist.get('High', 0)}", "🚨", "#ef4444"],
+                ["Champion Customers", f"{segment_dist.get('Champions', 0)}", "🏆", "#f59e0b"],
+                ["Healthy Customers", f"{risk_dist.get('Low', 0)}", "✅", "#06b6d4"]
+            ]
+            
+            return summary_html, kpi_data
+            
+        except Exception as e:
+            return f"Error generating dashboard: {str(e)}", []
     
     def train_churn_model(self):
         """Train churn prediction model"""
-        if self.df is None:
-            return "No data available. Please upload a CSV file first.", None
+        if self.processed_df is None:
+            return "No data available. Please upload and process a CSV file first.", None
         
         try:
-            customer_features = self.df.groupby('customer_id').agg({
+            # Prepare customer-level features
+            customer_features = self.processed_df.groupby('customer_id').agg({
                 'recency_days': 'first',
                 'frequency': 'first',
                 'monetary': 'first',
@@ -239,40 +296,46 @@ class B2BCustomerAnalytics:
                 'order_date': ['min', 'max']
             }).reset_index()
             
+            # Flatten column names
             customer_features.columns = ['customer_id', 'recency_days', 'frequency', 'monetary',
                                        'avg_amount', 'std_amount', 'min_amount', 'max_amount',
                                        'first_order', 'last_order']
             
+            # Handle missing values
             customer_features['std_amount'].fillna(0, inplace=True)
             
+            # Calculate additional features
             customer_features['customer_lifetime'] = (customer_features['last_order'] - customer_features['first_order']).dt.days
             customer_features['customer_lifetime'].fillna(0, inplace=True)
             
-            if 'churn_label' not in self.df.columns:
-                customer_features['churn_label'] = (customer_features['recency_days'] > 90).astype(int)
-            else:
-                churn_labels = self.df.groupby('customer_id')['churn_label'].first().reset_index()
-                customer_features = customer_features.merge(churn_labels, on='customer_id')
+            # Create churn labels based on recency
+            customer_features['churn_label'] = (customer_features['recency_days'] > 90).astype(int)
             
+            # Select features for modeling
             feature_cols = ['recency_days', 'frequency', 'monetary', 'avg_amount', 'std_amount', 
                            'min_amount', 'max_amount', 'customer_lifetime']
             
             X = customer_features[feature_cols]
             y = customer_features['churn_label']
             
+            # Train-test split
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
             
+            # Train model
             self.model = xgb.XGBClassifier(random_state=42, eval_metric='logloss')
             self.model.fit(X_train, y_train)
             
+            # Make predictions
             y_pred = self.model.predict(X_test)
             y_pred_proba = self.model.predict_proba(X_test)[:, 1]
             
+            # Feature importance
             self.feature_importance = pd.DataFrame({
                 'feature': feature_cols,
                 'importance': self.model.feature_importances_
             }).sort_values('importance', ascending=False)
             
+            # Predict for all customers
             all_predictions = self.model.predict_proba(X)[:, 1]
             customer_features['churn_probability'] = all_predictions
             self.predictions = customer_features
@@ -334,194 +397,209 @@ class B2BCustomerAnalytics:
         if self.feature_importance is None:
             return None
             
-        fig = px.bar(
-            self.feature_importance.head(8), 
-            x='importance', 
-            y='feature',
-            orientation='h',
-            title='Feature Importance Analysis',
-            labels={'importance': 'Importance Score', 'feature': 'Features'},
-            color='importance',
-            color_continuous_scale='viridis'
-        )
-        
-        fig.update_layout(
-            height=500,
-            showlegend=False,
-            plot_bgcolor='white',
-            paper_bgcolor='white',
-            title={
-                'text': '<b>Feature Importance Analysis</b>',
-                'x': 0.5,
-                'xanchor': 'center',
-                'font': {'size': 20, 'color': '#1f2937'}
-            },
-            font=dict(family="Inter, system-ui, sans-serif", size=12),
-            yaxis={'categoryorder': 'total ascending'},
-            xaxis=dict(gridcolor='#f1f5f9'),
-            yaxis_title=dict(font_size=14),
-            xaxis_title=dict(font_size=14)
-        )
-        
-        return fig
+        try:
+            fig = px.bar(
+                self.feature_importance.head(8), 
+                x='importance', 
+                y='feature',
+                orientation='h',
+                title='Feature Importance Analysis',
+                labels={'importance': 'Importance Score', 'feature': 'Features'},
+                color='importance',
+                color_continuous_scale='viridis'
+            )
+            
+            fig.update_layout(
+                height=500,
+                showlegend=False,
+                plot_bgcolor='white',
+                paper_bgcolor='white',
+                title={
+                    'text': '<b>Feature Importance Analysis</b>',
+                    'x': 0.5,
+                    'xanchor': 'center',
+                    'font': {'size': 20, 'color': '#1f2937'}
+                },
+                font=dict(family="Inter, system-ui, sans-serif", size=12),
+                yaxis={'categoryorder': 'total ascending'},
+                xaxis=dict(gridcolor='#f1f5f9'),
+                yaxis_title=dict(font_size=14),
+                xaxis_title=dict(font_size=14)
+            )
+            
+            return fig
+            
+        except Exception as e:
+            print(f"Error creating performance chart: {e}")
+            return None
     
     def create_visualizations(self):
         """Create comprehensive modern visualizations"""
-        if self.df is None:
+        if self.processed_df is None:
             return None, None, None, None
         
-        # 1. Customer Segment Distribution
-        segment_data = self.df.groupby('customer_id')['Segment'].first().value_counts().reset_index()
-        segment_data.columns = ['Segment', 'Count']
-        
-        fig1 = px.pie(
-            segment_data, 
-            values='Count', 
-            names='Segment',
-            title='<b>Customer Segment Distribution</b>',
-            hole=0.4,
-            color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899']
-        )
-        fig1.update_traces(textposition='inside', textinfo='percent+label', textfont_size=13)
-        fig1.update_layout(
-            height=450, 
-            showlegend=True,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
-            font=dict(family="Inter, system-ui, sans-serif", size=12),
-            paper_bgcolor='white',
-            plot_bgcolor='white'
-        )
-        
-        # 2. RFM Analysis
-        customer_rfm = self.df.groupby('customer_id').agg({
-            'recency_days': 'first',
-            'frequency': 'first', 
-            'monetary': 'first',
-            'Segment': 'first'
-        }).reset_index()
-        
-        fig2 = px.scatter(
-            customer_rfm, 
-            x='recency_days', 
-            y='frequency', 
-            size='monetary',
-            color='Segment', 
-            title='<b>RFM Customer Behavior Matrix</b>',
-            labels={
-                'recency_days': 'Days Since Last Purchase',
-                'frequency': 'Purchase Frequency',
-                'monetary': 'Total Revenue'
-            },
-            color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6'],
-            size_max=60
-        )
-        fig2.update_layout(
-            height=500,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
-            font=dict(family="Inter, system-ui, sans-serif", size=12),
-            paper_bgcolor='white',
-            plot_bgcolor='white'
-        )
-        
-        # 3. Churn Risk Analysis
-        if self.predictions is not None:
-            fig3 = px.histogram(
-                self.predictions, 
-                x='churn_probability', 
-                nbins=20,
-                title='<b>Churn Probability Distribution</b>',
-                labels={'churn_probability': 'Churn Probability', 'count': 'Number of Customers'},
-                color_discrete_sequence=[COLORS['primary']]
+        try:
+            # 1. Customer Segment Distribution
+            segment_data = self.processed_df.groupby('customer_id')['Segment'].first().value_counts().reset_index()
+            segment_data.columns = ['Segment', 'Count']
+            
+            fig1 = px.pie(
+                segment_data, 
+                values='Count', 
+                names='Segment',
+                title='<b>Customer Segment Distribution</b>',
+                hole=0.4,
+                color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899']
             )
-            fig3.add_vline(x=0.5, line_dash="dash", line_color="#ef4444", line_width=2,
-                          annotation_text="High Risk Threshold", annotation_position="top")
-        else:
-            risk_data = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts().reset_index()
-            risk_data.columns = ['Risk_Level', 'Count']
-            colors_map = {'High': '#ef4444', 'Medium': '#f59e0b', 'Low': '#10b981'}
-            fig3 = px.bar(
-                risk_data, 
-                x='Risk_Level', 
-                y='Count',
-                title='<b>Customer Churn Risk Distribution</b>',
-                color='Risk_Level', 
-                color_discrete_map=colors_map
+            fig1.update_traces(textposition='inside', textinfo='percent+label', textfont_size=13)
+            fig1.update_layout(
+                height=450, 
+                showlegend=True,
+                title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
+                font=dict(family="Inter, system-ui, sans-serif", size=12),
+                paper_bgcolor='white',
+                plot_bgcolor='white'
             )
-        
-        fig3.update_layout(
-            height=450, 
-            showlegend=False,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
-            font=dict(family="Inter, system-ui, sans-serif", size=12),
-            plot_bgcolor='white',
-            paper_bgcolor='white'
-        )
-        
-        # 4. Revenue Trends
-        self.df['order_month'] = self.df['order_date'].dt.to_period('M')
-        monthly_revenue = self.df.groupby('order_month')['amount'].sum().reset_index()
-        monthly_revenue['order_month'] = monthly_revenue['order_month'].astype(str)
-        
-        fig4 = px.line(
-            monthly_revenue, 
-            x='order_month', 
-            y='amount',
-            title='<b>Monthly Revenue Trends</b>',
-            labels={'amount': 'Revenue ($)', 'order_month': 'Month'},
-            line_shape='spline'
-        )
-        fig4.update_traces(line_color=COLORS['primary'], line_width=4, mode='lines+markers')
-        fig4.update_layout(
-            height=450, 
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
-            font=dict(family="Inter, system-ui, sans-serif", size=12),
-            plot_bgcolor='white',
-            paper_bgcolor='white',
-            xaxis_tickangle=-45,
-            xaxis=dict(gridcolor='#f1f5f9'),
-            yaxis=dict(gridcolor='#f1f5f9')
-        )
-        
-        return fig1, fig2, fig3, fig4
+            
+            # 2. RFM Analysis
+            customer_rfm = self.processed_df.groupby('customer_id').agg({
+                'recency_days': 'first',
+                'frequency': 'first', 
+                'monetary': 'first',
+                'Segment': 'first'
+            }).reset_index()
+            
+            fig2 = px.scatter(
+                customer_rfm, 
+                x='recency_days', 
+                y='frequency', 
+                size='monetary',
+                color='Segment', 
+                title='<b>RFM Customer Behavior Matrix</b>',
+                labels={
+                    'recency_days': 'Days Since Last Purchase',
+                    'frequency': 'Purchase Frequency',
+                    'monetary': 'Total Revenue'
+                },
+                color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6'],
+                size_max=60
+            )
+            fig2.update_layout(
+                height=500,
+                title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
+                font=dict(family="Inter, system-ui, sans-serif", size=12),
+                paper_bgcolor='white',
+                plot_bgcolor='white'
+            )
+            
+            # 3. Churn Risk Analysis
+            if self.predictions is not None:
+                fig3 = px.histogram(
+                    self.predictions, 
+                    x='churn_probability', 
+                    nbins=20,
+                    title='<b>Churn Probability Distribution</b>',
+                    labels={'churn_probability': 'Churn Probability', 'count': 'Number of Customers'},
+                    color_discrete_sequence=[COLORS['primary']]
+                )
+                fig3.add_vline(x=0.5, line_dash="dash", line_color="#ef4444", line_width=2,
+                              annotation_text="High Risk Threshold", annotation_position="top")
+            else:
+                risk_data = self.processed_df.groupby('customer_id')['Churn_Risk'].first().value_counts().reset_index()
+                risk_data.columns = ['Risk_Level', 'Count']
+                colors_map = {'High': '#ef4444', 'Medium': '#f59e0b', 'Low': '#10b981'}
+                fig3 = px.bar(
+                    risk_data, 
+                    x='Risk_Level', 
+                    y='Count',
+                    title='<b>Customer Churn Risk Distribution</b>',
+                    color='Risk_Level', 
+                    color_discrete_map=colors_map
+                )
+            
+            fig3.update_layout(
+                height=450, 
+                showlegend=False,
+                title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
+                font=dict(family="Inter, system-ui, sans-serif", size=12),
+                plot_bgcolor='white',
+                paper_bgcolor='white'
+            )
+            
+            # 4. Revenue Trends
+            self.processed_df['order_month'] = self.processed_df['order_date'].dt.to_period('M')
+            monthly_revenue = self.processed_df.groupby('order_month')['amount'].sum().reset_index()
+            monthly_revenue['order_month'] = monthly_revenue['order_month'].astype(str)
+            
+            fig4 = px.line(
+                monthly_revenue, 
+                x='order_month', 
+                y='amount',
+                title='<b>Monthly Revenue Trends</b>',
+                labels={'amount': 'Revenue ($)', 'order_month': 'Month'},
+                line_shape='spline'
+            )
+            fig4.update_traces(line_color=COLORS['primary'], line_width=4, mode='lines+markers')
+            fig4.update_layout(
+                height=450, 
+                title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 20, 'color': '#1f2937'}},
+                font=dict(family="Inter, system-ui, sans-serif", size=12),
+                plot_bgcolor='white',
+                paper_bgcolor='white',
+                xaxis_tickangle=-45,
+                xaxis=dict(gridcolor='#f1f5f9'),
+                yaxis=dict(gridcolor='#f1f5f9')
+            )
+            
+            return fig1, fig2, fig3, fig4
+            
+        except Exception as e:
+            print(f"Error creating visualizations: {e}")
+            return None, None, None, None
     
     def create_customer_table(self):
         """Create modern customer segmentation table"""
-        if self.df is None:
+        if self.processed_df is None:
             return None
             
-        customer_summary = self.df.groupby('customer_id').agg({
-            'Segment': 'first',
-            'Churn_Risk': 'first',
-            'recency_days': 'first',
-            'frequency': 'first',
-            'monetary': 'first',
-            'amount': 'mean'
-        }).reset_index()
-        
-        if self.predictions is not None:
-            customer_summary = customer_summary.merge(
-                self.predictions[['customer_id', 'churn_probability']], 
-                on='customer_id', 
-                how='left'
-            )
-            customer_summary['churn_probability'] = customer_summary['churn_probability'].fillna(0)
-        else:
-            customer_summary['churn_probability'] = 0.5
-        
-        customer_summary['monetary'] = customer_summary['monetary'].round(2)
-        customer_summary['amount'] = customer_summary['amount'].round(2)
-        customer_summary['churn_probability'] = (customer_summary['churn_probability'] * 100).round(1)
-        
-        customer_summary.columns = [
-            'Customer ID', 'Segment', 'Risk Level', 'Recency (Days)', 
-            'Frequency', 'Total Spent ($)', 'Avg Order ($)', 'Churn Probability (%)'
-        ]
-        
-        return customer_summary.head(50)
+        try:
+            customer_summary = self.processed_df.groupby('customer_id').agg({
+                'Segment': 'first',
+                'Churn_Risk': 'first',
+                'recency_days': 'first',
+                'frequency': 'first',
+                'monetary': 'first',
+                'amount': 'mean'
+            }).reset_index()
+            
+            if self.predictions is not None:
+                customer_summary = customer_summary.merge(
+                    self.predictions[['customer_id', 'churn_probability']], 
+                    on='customer_id', 
+                    how='left'
+                )
+                customer_summary['churn_probability'] = customer_summary['churn_probability'].fillna(0)
+            else:
+                customer_summary['churn_probability'] = 0.5
+            
+            customer_summary['monetary'] = customer_summary['monetary'].round(2)
+            customer_summary['amount'] = customer_summary['amount'].round(2)
+            customer_summary['churn_probability'] = (customer_summary['churn_probability'] * 100).round(1)
+            
+            customer_summary.columns = [
+                'Customer ID', 'Segment', 'Risk Level', 'Recency (Days)', 
+                'Frequency', 'Total Spent ($)', 'Avg Order ($)', 'Churn Probability (%)'
+            ]
+            
+            return customer_summary.head(50)
+            
+        except Exception as e:
+            print(f"Error creating customer table: {e}")
+            return None
     
     def generate_pdf_report(self):
         """Generate comprehensive PDF report"""
-        if self.df is None:
+        if self.processed_df is None:
             return None
         
         try:
@@ -546,10 +624,10 @@ class B2BCustomerAnalytics:
             
             story.append(Paragraph("Executive Summary", styles['Heading2']))
             
-            total_customers = self.df['customer_id'].nunique()
-            total_revenue = self.df['amount'].sum()
-            avg_order_value = self.df['amount'].mean()
-            high_risk_customers = len(self.df[self.df['Churn_Risk'] == 'High']['customer_id'].unique())
+            total_customers = self.processed_df['customer_id'].nunique()
+            total_revenue = self.processed_df['amount'].sum()
+            avg_order_value = self.processed_df['amount'].mean()
+            high_risk_customers = len(self.processed_df[self.processed_df['Churn_Risk'] == 'High']['customer_id'].unique())
             
             summary_text = f"""
             This comprehensive analysis examines {total_customers} B2B customers with total revenue of ${total_revenue:,.2f}. 
@@ -567,8 +645,8 @@ class B2BCustomerAnalytics:
             
             story.append(Paragraph("Key Performance Indicators", styles['Heading2']))
             
-            segment_dist = self.df.groupby('customer_id')['Segment'].first().value_counts()
-            risk_dist = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts()
+            segment_dist = self.processed_df.groupby('customer_id')['Segment'].first().value_counts()
+            risk_dist = self.processed_df.groupby('customer_id')['Churn_Risk'].first().value_counts()
             
             metrics_data = [
                 ['Metric', 'Value', 'Status'],
@@ -640,87 +718,91 @@ class B2BCustomerAnalytics:
 
     def get_customer_insights(self, customer_id):
         """Get detailed insights for a specific customer"""
-        if self.df is None:
+        if self.processed_df is None:
             return "No data available"
         
-        customer_data = self.df[self.df['customer_id'] == customer_id]
-        if customer_data.empty:
-            return f"Customer {customer_id} not found"
-        
-        total_orders = len(customer_data)
-        total_spent = customer_data['amount'].sum()
-        avg_order_value = customer_data['amount'].mean()
-        first_order = customer_data['order_date'].min()
-        last_order = customer_data['order_date'].max()
-        segment = customer_data['Segment'].iloc[0]
-        risk_level = customer_data['Churn_Risk'].iloc[0]
-        recency = customer_data['recency_days'].iloc[0]
-        
-        churn_prob = 0.5
-        if self.predictions is not None:
-            pred_data = self.predictions[self.predictions['customer_id'] == customer_id]
-            if not pred_data.empty:
-                churn_prob = pred_data['churn_probability'].iloc[0]
-        
-        insights_html = f"""
-        <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); margin-bottom: 2rem;">
-            <div style="text-align: center; margin-bottom: 2rem;">
-                <div style="display: inline-block; padding: 1.5rem; background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); border-radius: 50%; margin-bottom: 1rem;">
-                    <span style="font-size: 2rem; color: white;">📊</span>
-                </div>
-                <h3 style="color: #1f2937; font-size: 1.75rem; font-weight: bold; margin-bottom: 0.5rem;">
-                    Customer Profile: {customer_id}
-                </h3>
-                <p style="color: #6b7280; font-size: 1.1rem;">Comprehensive Customer Intelligence Report</p>
-            </div>
+        try:
+            customer_data = self.processed_df[self.processed_df['customer_id'] == customer_id]
+            if customer_data.empty:
+                return f"Customer {customer_id} not found"
             
-            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1.5rem; margin-bottom: 2rem;">
-                <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CUSTOMER SEGMENT</h4>
-                    <div style="font-size: 1.5rem; font-weight: bold;">{segment}</div>
-                </div>
-                <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CHURN RISK</h4>
-                    <div style="font-size: 1.5rem; font-weight: bold;">{risk_level}</div>
-                </div>
-                <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CHURN PROBABILITY</h4>
-                    <div style="font-size: 1.5rem; font-weight: bold;">{churn_prob:.1%}</div>
-                </div>
-            </div>
+            total_orders = len(customer_data)
+            total_spent = customer_data['amount'].sum()
+            avg_order_value = customer_data['amount'].mean()
+            first_order = customer_data['order_date'].min()
+            last_order = customer_data['order_date'].max()
+            segment = customer_data['Segment'].iloc[0]
+            risk_level = customer_data['Churn_Risk'].iloc[0]
+            recency = customer_data['recency_days'].iloc[0]
             
-            <div style="background: #f8fafc; padding: 2rem; border-radius: 1rem; margin-bottom: 2rem;">
-                <h4 style="color: #374151; font-weight: 700; margin-bottom: 1.5rem; font-size: 1.3rem;">Transaction Analytics</h4>
-                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 2rem;">
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Total Orders</div>
-                        <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">{total_orders}</div>
+            churn_prob = 0.5
+            if self.predictions is not None:
+                pred_data = self.predictions[self.predictions['customer_id'] == customer_id]
+                if not pred_data.empty:
+                    churn_prob = pred_data['churn_probability'].iloc[0]
+            
+            insights_html = f"""
+            <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); margin-bottom: 2rem;">
+                <div style="text-align: center; margin-bottom: 2rem;">
+                    <div style="display: inline-block; padding: 1.5rem; background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); border-radius: 50%; margin-bottom: 1rem;">
+                        <span style="font-size: 2rem; color: white;">📊</span>
                     </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Total Spent</div>
-                        <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">${total_spent:,.2f}</div>
+                    <h3 style="color: #1f2937; font-size: 1.75rem; font-weight: bold; margin-bottom: 0.5rem;">
+                        Customer Profile: {customer_id}
+                    </h3>
+                    <p style="color: #6b7280; font-size: 1.1rem;">Comprehensive Customer Intelligence Report</p>
+                </div>
+                
+                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1.5rem; margin-bottom: 2rem;">
+                    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
+                        <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CUSTOMER SEGMENT</h4>
+                        <div style="font-size: 1.5rem; font-weight: bold;">{segment}</div>
                     </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Avg Order Value</div>
-                        <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">${avg_order_value:.2f}</div>
+                    <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
+                        <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CHURN RISK</h4>
+                        <div style="font-size: 1.5rem; font-weight: bold;">{risk_level}</div>
                     </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Days Since Last Order</div>
-                        <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">{recency}</div>
+                    <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); padding: 1.5rem; border-radius: 1rem; color: white; text-align: center;">
+                        <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem; font-weight: 600;">CHURN PROBABILITY</h4>
+                        <div style="font-size: 1.5rem; font-weight: bold;">{churn_prob:.1%}</div>
                     </div>
                 </div>
+                
+                <div style="background: #f8fafc; padding: 2rem; border-radius: 1rem; margin-bottom: 2rem;">
+                    <h4 style="color: #374151; font-weight: 700; margin-bottom: 1.5rem; font-size: 1.3rem;">Transaction Analytics</h4>
+                    <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 2rem;">
+                        <div>
+                            <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Total Orders</div>
+                            <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">{total_orders}</div>
+                        </div>
+                        <div>
+                            <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Total Spent</div>
+                            <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">${total_spent:,.2f}</div>
+                        </div>
+                        <div>
+                            <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Avg Order Value</div>
+                            <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">${avg_order_value:.2f}</div>
+                        </div>
+                        <div>
+                            <div style="font-size: 0.875rem; color: #6b7280; font-weight: 600; margin-bottom: 0.5rem;">Days Since Last Order</div>
+                            <div style="font-size: 2rem; font-weight: bold; color: #1f2937;">{recency}</div>
+                        </div>
+                    </div>
+                </div>
+                
+                <div style="background: linear-gradient(135deg, #f0f9ff, #e0f2fe); border-left: 4px solid #3b82f6; padding: 1.5rem; border-radius: 0.5rem;">
+                    <h4 style="color: #1e40af; font-weight: 700; margin-bottom: 1rem; font-size: 1.2rem;">Strategic Recommendations</h4>
+                    <p style="color: #1f2937; margin: 0; font-size: 1rem; line-height: 1.6;">
+                        {self._get_customer_recommendations(segment, risk_level, churn_prob, recency)}
+                    </p>
+                </div>
             </div>
+            """
             
-            <div style="background: linear-gradient(135deg, #f0f9ff, #e0f2fe); border-left: 4px solid #3b82f6; padding: 1.5rem; border-radius: 0.5rem;">
-                <h4 style="color: #1e40af; font-weight: 700; margin-bottom: 1rem; font-size: 1.2rem;">Strategic Recommendations</h4>
-                <p style="color: #1f2937; margin: 0; font-size: 1rem; line-height: 1.6;">
-                    {self._get_customer_recommendations(segment, risk_level, churn_prob, recency)}
-                </p>
-            </div>
-        </div>
-        """
-        
-        return insights_html
+            return insights_html
+            
+        except Exception as e:
+            return f"Error getting customer insights: {str(e)}"
     
     def _get_customer_recommendations(self, segment, risk_level, churn_prob, recency):
         """Generate personalized recommendations based on customer profile"""