Update app.py

app.py

@@ -29,11 +29,14 @@ COLORS = {
     'warning': '#f59e0b',
     'danger': '#ef4444',
     'purple': '#8b5cf6',
-    'indigo': '#6366f1',
+    'pink': '#ec4899',
     'blue': '#3b82f6',
-    'gray': '#6b7280'
+    'indigo': '#6366f1'
 }
 
+plt.style.use('seaborn-v0_8-whitegrid')
+sns.set_palette("husl")
+
 class B2BCustomerAnalytics:
     def __init__(self):
         self.df = None
@@ -49,26 +52,21 @@ class B2BCustomerAnalytics:
             
             self.df = pd.read_csv(file.name)
             
-            # Basic validation
             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
             
-            # Convert order_date to datetime
             self.df['order_date'] = pd.to_datetime(self.df['order_date'])
             
-            # Calculate RFM metrics if not present
-            if 'recency_days' not in self.df.columns:
+            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)
             
-            # Customer segmentation
             self.df = self.perform_customer_segmentation(self.df)
             
-            # Generate modern dashboard
-            dashboard_html, metrics_cards = self.generate_modern_dashboard()
+            summary_html, kpi_cards = self.generate_summary_dashboard()
             
-            return "Data loaded successfully", dashboard_html, self.df.head(20), metrics_cards
+            return "Data loaded successfully!", summary_html, self.df.head(20), kpi_cards
             
         except Exception as e:
             return f"Error loading data: {str(e)}", None, None, None
@@ -98,7 +96,6 @@ class B2BCustomerAnalytics:
             'monetary': 'first'
         }).reset_index()
         
-        # Create RFM scores (1-5 scale)
         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])
@@ -126,6 +123,7 @@ class B2BCustomerAnalytics:
                 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)
@@ -135,29 +133,23 @@ class B2BCustomerAnalytics:
         
         return df_with_segments
     
-    def generate_modern_dashboard(self):
-        """Generate modern dashboard with clean design"""
+    def generate_summary_dashboard(self):
+        """Generate modern dashboard summary with KPI cards"""
         if self.df is None:
             return "No data loaded", ""
         
-        # Calculate KPIs
         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()
         
-        # Risk and segment distributions
         segment_dist = self.df.groupby('customer_id')['Segment'].first().value_counts()
         risk_dist = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts()
         
-        high_risk_customers = risk_dist.get('High', 0)
-        champion_customers = segment_dist.get('Champions', 0)
-        healthy_customers = risk_dist.get('Low', 0)
-        
-        # Modern dashboard HTML
-        dashboard_html = f"""
-        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 3rem; border-radius: 1rem; color: white; margin-bottom: 3rem; text-align: center;">
-            <h1 style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem; font-family: 'Inter', sans-serif;">
+        # 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;">
@@ -165,89 +157,76 @@ class B2BCustomerAnalytics:
             </p>
         </div>
         
-        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1.5rem; margin-bottom: 3rem;">
-            
-            <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #3b82f6; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #eff6ff; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #3b82f6; border-radius: 50%;"></div>
-                    </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-bottom: 0.25rem;">Total Customers</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Active enterprise clients</p>
+                <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>
             
-            <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #10b981; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #f0fdf4; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #10b981; border-radius: 50%;"></div>
-                    </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 #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-bottom: 0.25rem;">Total Revenue</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Contract value sum</p>
+                <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>
             
-            <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #8b5cf6; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #faf5ff; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #8b5cf6; border-radius: 50%;"></div>
-                    </div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #8b5cf6;">${(avg_order_value/1000):.0f}K</span>
+            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border-left: 4px solid #8b5cf6;">
+                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem;">
+                    <div style="padding: 0.75rem; background: #ede9fe; border-radius: 0.5rem; color: #7c3aed;">📈</div>
+                    <span style="font-size: 2rem; font-weight: bold; color: #8b5cf6;">${avg_order_value:.0f}</span>
                 </div>
-                <h3 style="color: #1f2937; font-weight: 600; margin-bottom: 0.25rem;">Avg Order Value</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Per customer average</p>
+                <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: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #ef4444; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #fef2f2; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #ef4444; border-radius: 50%;"></div>
-                    </div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #ef4444;">{high_risk_customers}</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 #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-bottom: 0.25rem;">High Risk Clients</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Require immediate attention</p>
+                <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: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #f59e0b; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #fffbeb; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #f59e0b; border-radius: 50%;"></div>
-                    </div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #f59e0b;">{champion_customers}</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 #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-bottom: 0.25rem;">Champion Clients</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Top tier customers</p>
+                <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: 2rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #06b6d4; transition: transform 0.2s;">
-                <div style="display: flex; items-center: justify-between; margin-bottom: 1rem;">
-                    <div style="padding: 0.75rem; background: #f0fdfa; border-radius: 0.5rem;">
-                        <div style="width: 1.5rem; height: 1.5rem; background: #06b6d4; border-radius: 50%;"></div>
-                    </div>
-                    <span style="font-size: 2rem; font-weight: bold; color: #06b6d4;">{healthy_customers}</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 #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-bottom: 0.25rem;">Healthy Clients</h3>
-                <p style="color: #6b7280; font-size: 0.875rem;">Low churn risk</p>
+                <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>
         """
         
-        metrics_cards = [
-            ["Total Customers", f"{total_customers:,}", "#3b82f6"],
-            ["Total Revenue", f"${total_revenue/1000000:.1f}M", "#10b981"], 
-            ["Avg Order Value", f"${avg_order_value/1000:.0f}K", "#8b5cf6"],
-            ["High Risk Customers", f"{high_risk_customers}", "#ef4444"],
-            ["Champion Customers", f"{champion_customers}", "#f59e0b"],
-            ["Healthy Customers", f"{healthy_customers}", "#06b6d4"]
+        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 dashboard_html, metrics_cards
+        return summary_html, kpi_data
     
     def train_churn_model(self):
-        """Train churn prediction model with modern UI feedback"""
+        """Train churn prediction model"""
         if self.df is None:
             return "No data available. Please upload a CSV file first.", None
         
@@ -265,10 +244,15 @@ class B2BCustomerAnalytics:
                                        'first_order', 'last_order']
             
             customer_features['std_amount'].fillna(0, inplace=True)
+            
             customer_features['customer_lifetime'] = (customer_features['last_order'] - customer_features['first_order']).dt.days
             customer_features['customer_lifetime'].fillna(0, inplace=True)
             
-            customer_features['churn_label'] = (customer_features['recency_days'] > 90).astype(int)
+            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')
             
             feature_cols = ['recency_days', 'frequency', 'monetary', 'avg_amount', 'std_amount', 
                            'min_amount', 'max_amount', 'customer_lifetime']
@@ -282,7 +266,7 @@ class B2BCustomerAnalytics:
             self.model.fit(X_train, y_train)
             
             y_pred = self.model.predict(X_test)
-            accuracy = accuracy_score(y_test, y_pred)
+            y_pred_proba = self.model.predict_proba(X_test)[:, 1]
             
             self.feature_importance = pd.DataFrame({
                 'feature': feature_cols,
@@ -293,12 +277,13 @@ class B2BCustomerAnalytics:
             customer_features['churn_probability'] = all_predictions
             self.predictions = customer_features
             
-            # Modern results display
+            accuracy = accuracy_score(y_test, y_pred)
+            
             results_html = f"""
-            <div style="background: white; padding: 2.5rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border: 1px solid #e5e7eb; margin-top: 2rem;">
+            <div style="background: white; padding: 2rem; border-radius: 1rem; box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1); border: 1px solid #e5e7eb; margin-bottom: 2rem;">
                 <div style="text-align: center; margin-bottom: 2rem;">
-                    <div style="display: inline-block; padding: 1rem; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 50%; margin-bottom: 1rem;">
-                        <div style="width: 2rem; height: 2rem; background: white; border-radius: 50%; opacity: 0.3;"></div>
+                    <div style="display: inline-block; padding: 1rem; background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); border-radius: 50%; margin-bottom: 1rem;">
+                        <span style="font-size: 2rem;">🤖</span>
                     </div>
                     <h3 style="font-size: 1.75rem; font-weight: bold; color: #1f2937; margin-bottom: 0.5rem;">
                         Model Training Completed
@@ -308,36 +293,31 @@ class B2BCustomerAnalytics:
                 
                 <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1.5rem; margin-bottom: 2rem;">
                     <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 1.5rem; border-radius: 1rem; text-align: center; color: white;">
-                        <div style="font-size: 2rem; font-weight: bold; margin-bottom: 0.5rem;">{accuracy:.1%}</div>
+                        <div style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">{accuracy:.1%}</div>
                         <div style="font-size: 1rem; opacity: 0.9;">Model Accuracy</div>
                     </div>
                     <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 1.5rem; border-radius: 1rem; text-align: center; color: white;">
-                        <div style="font-size: 2rem; font-weight: bold; margin-bottom: 0.5rem;">{len(feature_cols)}</div>
+                        <div style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">{len(feature_cols)}</div>
                         <div style="font-size: 1rem; opacity: 0.9;">Features Used</div>
                     </div>
                     <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); padding: 1.5rem; border-radius: 1rem; text-align: center; color: white;">
-                        <div style="font-size: 2rem; font-weight: bold; margin-bottom: 0.5rem;">{len(X_train)}</div>
+                        <div style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">{len(X_train)}</div>
                         <div style="font-size: 1rem; opacity: 0.9;">Training Samples</div>
                     </div>
                     <div style="background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%); padding: 1.5rem; border-radius: 1rem; text-align: center; color: white;">
-                        <div style="font-size: 2rem; font-weight: bold; margin-bottom: 0.5rem;">{len(X_test)}</div>
+                        <div style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem;">{len(X_test)}</div>
                         <div style="font-size: 1rem; opacity: 0.9;">Test Samples</div>
                     </div>
                 </div>
                 
-                <div style="background: #f8fafc; padding: 2rem; border-radius: 1rem; border: 1px solid #e2e8f0;">
-                    <h4 style="font-weight: 600; color: #374151; margin-bottom: 1.5rem; font-size: 1.25rem;">Top Feature Importance</h4>
-                    <div style="space-y: 1rem;">
+                <div style="background: #f8fafc; padding: 1.5rem; border-radius: 1rem;">
+                    <h4 style="font-weight: 700; color: #374151; margin-bottom: 1rem; font-size: 1.2rem;">Top Feature Importance</h4>
+                    <div>
                         {''.join([f'''<div style="display: flex; justify-content: space-between; align-items: center; padding: 1rem 0; border-bottom: 1px solid #e5e7eb;">
-                            <span style="font-weight: 500; color: #374151; font-size: 1rem;">{row['feature'].replace('_', ' ').title()}</span>
-                            <div style="display: flex; align-items: center;">
-                                <div style="width: 100px; height: 8px; background: #e5e7eb; border-radius: 4px; margin-right: 1rem;">
-                                    <div style="height: 100%; background: #3b82f6; border-radius: 4px; width: {row['importance']*100:.1f}%;"></div>
-                                </div>
-                                <span style="background: #3b82f6; color: white; padding: 0.25rem 0.75rem; border-radius: 9999px; font-size: 0.875rem; font-weight: 500;">
-                                    {row['importance']:.3f}
-                                </span>
-                            </div>
+                            <span style="font-weight: 600; color: #374151; font-size: 1rem;">{row['feature'].replace('_', ' ').title()}</span>
+                            <span style="background: linear-gradient(135deg, #3b82f6, #1d4ed8); color: white; padding: 0.5rem 1rem; border-radius: 2rem; font-size: 0.9rem; font-weight: 600;">
+                                {row['importance']:.3f}
+                            </span>
                         </div>''' for _, row in self.feature_importance.head(5).iterrows()])}
                     </div>
                 </div>
@@ -350,7 +330,7 @@ class B2BCustomerAnalytics:
             return f"Error training model: {str(e)}", None
     
     def create_model_performance_chart(self):
-        """Create clean model performance visualization"""
+        """Create model performance visualization"""
         if self.feature_importance is None:
             return None
             
@@ -362,29 +342,31 @@ class B2BCustomerAnalytics:
             title='Feature Importance Analysis',
             labels={'importance': 'Importance Score', 'feature': 'Features'},
             color='importance',
-            color_continuous_scale=['#e0e7ff', '#6366f1']
+            color_continuous_scale='viridis'
         )
         
         fig.update_layout(
-            height=400,
+            height=500,
             showlegend=False,
             plot_bgcolor='white',
             paper_bgcolor='white',
             title={
-                'text': 'Feature Importance Analysis',
+                'text': '<b>Feature Importance Analysis</b>',
                 'x': 0.5,
                 'xanchor': 'center',
-                'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter, sans-serif'}
+                'font': {'size': 20, 'color': '#1f2937'}
             },
-            font=dict(family="Inter, sans-serif", color='#374151'),
+            font=dict(family="Inter, system-ui, sans-serif", size=12),
             yaxis={'categoryorder': 'total ascending'},
-            margin=dict(l=20, r=20, t=60, b=20)
+            xaxis=dict(gridcolor='#f1f5f9'),
+            yaxis_title=dict(font_size=14),
+            xaxis_title=dict(font_size=14)
         )
         
         return fig
     
     def create_visualizations(self):
-        """Create modern, clean visualizations"""
+        """Create comprehensive modern visualizations"""
         if self.df is None:
             return None, None, None, None
         
@@ -396,21 +378,16 @@ class B2BCustomerAnalytics:
             segment_data, 
             values='Count', 
             names='Segment',
-            title='Customer Segment Distribution',
+            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=12,
-            textfont_family='Inter'
-        )
+        fig1.update_traces(textposition='inside', textinfo='percent+label', textfont_size=13)
         fig1.update_layout(
-            height=400, 
+            height=450, 
             showlegend=True,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter'}},
-            font=dict(family="Inter, sans-serif", color='#374151'),
+            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'
         )
@@ -428,19 +405,20 @@ class B2BCustomerAnalytics:
             x='recency_days', 
             y='frequency', 
             size='monetary',
-            color='Segment',
-            title='RFM Analysis - Customer Behavior Matrix',
+            color='Segment', 
+            title='<b>RFM Customer Behavior Matrix</b>',
             labels={
-                'recency_days': 'Recency (Days)',
-                'frequency': 'Frequency (Orders)',
-                'monetary': 'Monetary (Revenue)'
+                'recency_days': 'Days Since Last Purchase',
+                'frequency': 'Purchase Frequency',
+                'monetary': 'Total Revenue'
             },
-            color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6']
+            color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6'],
+            size_max=60
         )
         fig2.update_layout(
-            height=400,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter'}},
-            font=dict(family="Inter, sans-serif", color='#374151'),
+            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'
         )
@@ -451,11 +429,12 @@ class B2BCustomerAnalytics:
                 self.predictions, 
                 x='churn_probability', 
                 nbins=20,
-                title='Churn Probability Distribution',
+                title='<b>Churn Probability Distribution</b>',
                 labels={'churn_probability': 'Churn Probability', 'count': 'Number of Customers'},
-                color_discrete_sequence=['#6366f1']
+                color_discrete_sequence=[COLORS['primary']]
             )
-            fig3.add_vline(x=0.5, line_dash="dash", line_color="#ef4444", annotation_text="High Risk Threshold")
+            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']
@@ -464,18 +443,18 @@ class B2BCustomerAnalytics:
                 risk_data, 
                 x='Risk_Level', 
                 y='Count',
-                title='Customer Churn Risk Distribution',
+                title='<b>Customer Churn Risk Distribution</b>',
                 color='Risk_Level', 
                 color_discrete_map=colors_map
             )
         
         fig3.update_layout(
-            height=400, 
+            height=450, 
             showlegend=False,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter'}},
-            font=dict(family="Inter, sans-serif", color='#374151'),
-            paper_bgcolor='white',
-            plot_bgcolor='white'
+            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
@@ -487,18 +466,522 @@ class B2BCustomerAnalytics:
             monthly_revenue, 
             x='order_month', 
             y='amount',
-            title='Monthly Revenue Trends',
+            title='<b>Monthly Revenue Trends</b>',
             labels={'amount': 'Revenue ($)', 'order_month': 'Month'},
             line_shape='spline'
         )
-        fig4.update_traces(line_color='#6366f1', line_width=3)
+        fig4.update_traces(line_color=COLORS['primary'], line_width=4, mode='lines+markers')
         fig4.update_layout(
-            height=400, 
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter'}},
-            font=dict(family="Inter, sans-serif", color='#374151'),
-            paper_bgcolor='white',
+            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',
-            xaxis_tickangle=-45
+            paper_bgcolor='white',
+            xaxis_tickangle=-45,
+            xaxis=dict(gridcolor='#f1f5f9'),
+            yaxis=dict(gridcolor='#f1f5f9')
         )
         
-        return
+        return fig1, fig2, fig3, fig4
+    
+    def create_customer_table(self):
+        """Create modern customer segmentation table"""
+        if self.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)
+    
+    def generate_pdf_report(self):
+        """Generate comprehensive PDF report"""
+        if self.df is None:
+            return None
+        
+        try:
+            buffer = io.BytesIO()
+            doc = SimpleDocTemplate(buffer, pagesize=A4, rightMargin=72, leftMargin=72,
+                                  topMargin=72, bottomMargin=18)
+            
+            styles = getSampleStyleSheet()
+            title_style = ParagraphStyle(
+                'CustomTitle',
+                parent=styles['Heading1'],
+                fontSize=24,
+                spaceAfter=30,
+                textColor=colors.HexColor('#6366f1'),
+                alignment=1
+            )
+            
+            story = []
+            
+            story.append(Paragraph("B2B Customer Analytics Report", title_style))
+            story.append(Spacer(1, 20))
+            
+            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())
+            
+            summary_text = f"""
+            This comprehensive analysis examines {total_customers} B2B customers with total revenue of ${total_revenue:,.2f}. 
+            The average order value stands at ${avg_order_value:.2f}, indicating healthy transaction volumes.
+            
+            Critical findings reveal {high_risk_customers} customers at high risk of churning, representing significant revenue exposure. 
+            Our machine learning model achieved high accuracy in predicting customer churn, enabling proactive retention strategies.
+            
+            The customer segmentation analysis identifies distinct behavioral patterns, with Champions showing the highest lifetime value 
+            and lowest churn risk, while At Risk customers require immediate intervention to prevent revenue loss.
+            """
+            
+            story.append(Paragraph(summary_text, styles['Normal']))
+            story.append(Spacer(1, 20))
+            
+            story.append(Paragraph("Key Performance Indicators", styles['Heading2']))
+            
+            segment_dist = self.df.groupby('customer_id')['Segment'].first().value_counts()
+            risk_dist = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts()
+            
+            metrics_data = [
+                ['Metric', 'Value', 'Status'],
+                ['Total Customers', f"{total_customers:,}", 'Baseline'],
+                ['Total Revenue', f"${total_revenue:,.2f}", 'Strong'],
+                ['Average Order Value', f"${avg_order_value:.2f}", 'Healthy'],
+                ['Champions', f"{segment_dist.get('Champions', 0)}", 'Retain'],
+                ['At Risk Customers', f"{segment_dist.get('At Risk', 0)}", 'Action Required'],
+                ['High Risk Churn', f"{risk_dist.get('High', 0)}", 'Critical'],
+                ['Low Risk Churn', f"{risk_dist.get('Low', 0)}", 'Stable']
+            ]
+            
+            metrics_table = Table(metrics_data, colWidths=[2*inch, 1.5*inch, 1.5*inch])
+            metrics_table.setStyle(TableStyle([
+                ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#6366f1')),
+                ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
+                ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+                ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+                ('FONTSIZE', (0, 0), (-1, 0), 12),
+                ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
+                ('BACKGROUND', (0, 1), (-1, -1), colors.beige),
+                ('GRID', (0, 0), (-1, -1), 1, colors.black),
+                ('FONTSIZE', (0, 1), (-1, -1), 10),
+                ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')
+            ]))
+            
+            story.append(metrics_table)
+            story.append(Spacer(1, 20))
+            
+            story.append(Paragraph("Strategic Recommendations", styles['Heading2']))
+            
+            recommendations_text = """
+            Based on our comprehensive analysis, we recommend the following strategic actions:
+            
+            1. IMMEDIATE ACTIONS (0-30 days):
+               • Contact all high-risk customers personally
+               • Offer retention incentives to at-risk segments
+               • Implement automated early warning system
+            
+            2. SHORT-TERM INITIATIVES (1-3 months):
+               • Develop targeted marketing campaigns by segment
+               • Launch loyalty program for Champions
+               • Create win-back campaigns for lost customers
+            
+            3. LONG-TERM STRATEGY (3-12 months):
+               • Invest in customer success programs
+               • Develop predictive analytics capabilities
+               • Build comprehensive customer health scoring
+               • Implement continuous model monitoring and improvement
+            """
+            
+            story.append(Paragraph(recommendations_text, styles['Normal']))
+            story.append(Spacer(1, 20))
+            
+            story.append(Paragraph(f"Report generated on {datetime.now().strftime('%B %d, %Y at %I:%M %p')}", 
+                                 styles['Normal']))
+            story.append(Paragraph("B2B Customer Analytics Platform - Enterprise Edition", 
+                                 styles['Normal']))
+            
+            doc.build(story)
+            pdf_bytes = buffer.getvalue()
+            buffer.close()
+            
+            return pdf_bytes
+            
+        except Exception as e:
+            print(f"Error generating PDF report: {str(e)}")
+            return None
+
+    def get_customer_insights(self, customer_id):
+        """Get detailed insights for a specific customer"""
+        if self.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>
+            
+            <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>
+            
+            <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>
+        """
+        
+        return insights_html
+    
+    def _get_customer_recommendations(self, segment, risk_level, churn_prob, recency):
+        """Generate personalized recommendations based on customer profile"""
+        recommendations = []
+        
+        if risk_level == 'High' or churn_prob > 0.7:
+            recommendations.append("🚨 URGENT: Personal outreach required within 24 hours")
+            recommendations.append("💰 Offer retention incentive (discount/upgrade)")
+            recommendations.append("📞 Schedule executive-level call")
+        elif risk_level == 'Medium':
+            recommendations.append("📧 Send personalized re-engagement campaign")
+            recommendations.append("🎯 Offer targeted product recommendations")
+        
+        if segment == 'Champions':
+            recommendations.append("🏆 Invite to VIP program or advisory board")
+            recommendations.append("🔄 Cross-sell premium services")
+        elif segment == 'At Risk':
+            recommendations.append("⚠️ Proactive customer success intervention")
+            recommendations.append("📊 Conduct health check survey")
+        elif segment == 'New Customers':
+            recommendations.append("🎉 Deploy onboarding campaign")
+            recommendations.append("📚 Provide educational resources")
+        
+        if recency > 60:
+            recommendations.append("🔄 Win-back campaign with special offer")
+        
+        return " • ".join(recommendations) if recommendations else "Continue monitoring customer engagement patterns."
+
+
+def create_gradio_interface():
+    """Create the modern Gradio interface for the B2B Customer Analytics platform"""
+    
+    analytics = B2BCustomerAnalytics()
+    
+    def load_data(file):
+        if file is None:
+            return "Please upload a CSV file", None, None, None
+        result = analytics.load_and_process_data(file)
+        return result
+    
+    def train_model():
+        result = analytics.train_churn_model()
+        return result
+    
+    def create_charts():
+        return analytics.create_visualizations()
+    
+    def get_customer_table():
+        return analytics.create_customer_table()
+    
+    def generate_report():
+        pdf_bytes = analytics.generate_pdf_report()
+        if pdf_bytes:
+            return pdf_bytes
+        return None
+    
+    def get_insights(customer_id):
+        if not customer_id:
+            return "Please enter a customer ID"
+        return analytics.get_customer_insights(customer_id)
+    
+    # Custom CSS for modern styling
+    custom_css = """
+    .gradio-container {
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
+        max-width: 1400px !important;
+        margin: 0 auto !important;
+    }
+    .main-header {
+        background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%) !important;
+        padding: 2rem !important;
+        border-radius: 1rem !important;
+        color: white !important;
+        text-align: center !important;
+        margin-bottom: 2rem !important;
+    }
+    .tab-nav {
+        border-radius: 1rem !important;
+        background: white !important;
+        box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1) !important;
+    }
+    .block {
+        border-radius: 1rem !important;
+        box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1) !important;
+        border: 1px solid #e5e7eb !important;
+    }
+    """
+    
+    with gr.Blocks(
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="purple", 
+            neutral_hue="slate"
+        ),
+        title="B2B Customer Analytics Platform",
+        css=custom_css
+    ) as demo:
+        
+        gr.HTML("""
+        <div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 3rem 2rem; border-radius: 1rem; color: white; text-align: center; margin-bottom: 2rem;">
+            <div style="display: inline-block; padding: 1rem; background: rgba(255, 255, 255, 0.2); border-radius: 50%; margin-bottom: 1rem;">
+                <span style="font-size: 3rem;">🏢</span>
+            </div>
+            <h1 style="font-size: 3rem; font-weight: 800; margin-bottom: 0.5rem; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                B2B Customer Analytics Platform
+            </h1>
+            <p style="font-size: 1.3rem; opacity: 0.95; font-weight: 500;">
+                Enterprise Customer Health Monitoring & Churn Prediction System
+            </p>
+            <div style="margin-top: 1.5rem; font-size: 0.95rem; opacity: 0.8;">
+                Powered by Advanced Machine Learning & Predictive Analytics
+            </div>
+        </div>
+        """)
+        
+        with gr.Tabs(elem_classes="tab-nav"):
+            
+            with gr.Tab("📊 Data Upload & Dashboard", elem_id="data-tab"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        file_input = gr.File(
+                            label="Upload Customer Data CSV", 
+                            file_types=[".csv"],
+                            elem_classes="block"
+                        )
+                        load_btn = gr.Button(
+                            "Load & Process Data", 
+                            variant="primary", 
+                            size="lg",
+                            elem_classes="block"
+                        )
+                        load_status = gr.HTML()
+                
+                summary_display = gr.HTML()
+                data_preview = gr.DataFrame(
+                    label="Data Preview", 
+                    max_rows=10,
+                    elem_classes="block"
+                )
+            
+            with gr.Tab("🎯 Customer Segmentation Analysis", elem_id="segmentation-tab"):
+                with gr.Row():
+                    with gr.Column():
+                        segment_chart = gr.Plot(label="Customer Segments Distribution")
+                    with gr.Column():
+                        rfm_chart = gr.Plot(label="RFM Behavior Matrix")
+                
+                customer_table = gr.DataFrame(
+                    label="Customer Segmentation Details",
+                    elem_classes="block"
+                )
+            
+            with gr.Tab("🤖 AI-Powered Churn Prediction", elem_id="churn-tab"):
+                with gr.Column():
+                    train_btn = gr.Button(
+                        "Train Churn Prediction Model", 
+                        variant="primary", 
+                        size="lg",
+                        elem_classes="block"
+                    )
+                    
+                    model_results = gr.HTML()
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            performance_chart = gr.Plot(label="Feature Importance Analysis")
+                        with gr.Column():
+                            churn_chart = gr.Plot(label="Churn Risk Distribution")
+            
+            with gr.Tab("💰 Revenue Analytics", elem_id="revenue-tab"):
+                revenue_chart = gr.Plot(
+                    label="Monthly Revenue Trends",
+                    elem_classes="block"
+                )
+            
+            with gr.Tab("🔍 Customer Intelligence", elem_id="insights-tab"):
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        customer_id_input = gr.Textbox(
+                            label="Customer ID Lookup", 
+                            placeholder="Enter customer ID (e.g., CUST001)",
+                            elem_classes="block"
+                        )
+                    with gr.Column(scale=1):
+                        insights_btn = gr.Button(
+                            "Get Customer Profile", 
+                            variant="primary",
+                            elem_classes="block"
+                        )
+                
+                customer_insights = gr.HTML()
+            
+            with gr.Tab("📄 Executive Reports", elem_id="reports-tab"):
+                with gr.Column():
+                    gr.HTML("""
+                    <div style="background: linear-gradient(135deg, #f0f9ff, #e0f2fe); padding: 2rem; border-radius: 1rem; margin-bottom: 2rem; border-left: 4px solid #3b82f6;">
+                        <h3 style="color: #1e40af; margin-bottom: 1rem; font-size: 1.5rem; font-weight: 700;">📋 Comprehensive Analytics Report</h3>
+                        <p style="color: #374151; margin-bottom: 1.5rem; font-size: 1.1rem;">Generate a complete executive summary with insights, recommendations, and strategic action items.</p>
+                        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1rem;">
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Executive Summary</div>
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Customer Segmentation</div>
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Churn Risk Assessment</div>
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Revenue Analytics</div>
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Strategic Recommendations</div>
+                            <div style="font-size: 0.9rem; color: #6b7280;">✓ Model Performance Metrics</div>
+                        </div>
+                    </div>
+                    """)
+                    
+                    report_btn = gr.Button(
+                        "Generate Executive Report", 
+                        variant="primary", 
+                        size="lg",
+                        elem_classes="block"
+                    )
+                    
+                    report_download = gr.File(
+                        label="Download PDF Report",
+                        elem_classes="block"
+                    )
+        
+        # Event handlers
+        load_btn.click(
+            fn=load_data,
+            inputs=[file_input],
+            outputs=[load_status, summary_display, data_preview, gr.HTML()]
+        )
+        
+        train_btn.click(
+            fn=train_model,
+            outputs=[model_results, performance_chart]
+        )
+        
+        load_btn.click(
+            fn=create_charts,
+            outputs=[segment_chart, rfm_chart, churn_chart, revenue_chart]
+        )
+        
+        load_btn.click(
+            fn=get_customer_table,
+            outputs=[customer_table]
+        )
+        
+        insights_btn.click(
+            fn=get_insights,
+            inputs=[customer_id_input],
+            outputs=[customer_insights]
+        )
+        
+        report_btn.click(
+            fn=generate_report,
+            outputs=[report_download]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )