Update app.py

app.py

@@ -22,22 +22,18 @@ import base64
 import warnings
 warnings.filterwarnings('ignore')
 
-# Set modern color palette
+# Modern color palette
 COLORS = {
     'primary': '#6366f1',
     'success': '#10b981', 
     'warning': '#f59e0b',
     'danger': '#ef4444',
     'purple': '#8b5cf6',
-    'pink': '#ec4899',
+    'indigo': '#6366f1',
     'blue': '#3b82f6',
-    'indigo': '#6366f1'
+    'gray': '#6b7280'
 }
 
-# Set plotting style for modern look
-plt.style.use('seaborn-v0_8-whitegrid')
-sns.set_palette("husl")
-
 class B2BCustomerAnalytics:
     def __init__(self):
         self.df = None
@@ -51,10 +47,9 @@ class B2BCustomerAnalytics:
             if file is None:
                 return "Please upload a CSV file", None, None, None
             
-            # Read the CSV file
             self.df = pd.read_csv(file.name)
             
-            # Basic data validation
+            # 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:
@@ -64,16 +59,16 @@ class B2BCustomerAnalytics:
             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 or 'frequency' not in self.df.columns or 'monetary' not in self.df.columns:
+            if 'recency_days' not in self.df.columns:
                 self.df = self.calculate_rfm_metrics(self.df)
             
             # Customer segmentation
             self.df = self.perform_customer_segmentation(self.df)
             
-            # Generate summary and KPIs
-            summary_html, kpi_cards = self.generate_summary_dashboard()
+            # Generate modern dashboard
+            dashboard_html, metrics_cards = self.generate_modern_dashboard()
             
-            return "Data loaded successfully!", summary_html, self.df.head(20), kpi_cards
+            return "Data loaded successfully", dashboard_html, self.df.head(20), metrics_cards
             
         except Exception as e:
             return f"Error loading data: {str(e)}", None, None, None
@@ -82,7 +77,6 @@ class B2BCustomerAnalytics:
         """Calculate RFM metrics from transaction data"""
         current_date = df['order_date'].max() + timedelta(days=1)
         
-        # Group by customer
         customer_metrics = df.groupby('customer_id').agg({
             'order_date': ['max', 'count'],
             'amount': ['sum', 'mean']
@@ -91,7 +85,6 @@ class B2BCustomerAnalytics:
         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')
         
@@ -110,12 +103,10 @@ class B2BCustomerAnalytics:
         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])
         
-        # Convert to numeric
         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)
         
-        # Create segments
         def segment_customers(row):
             if row['R_Score'] >= 4 and row['F_Score'] >= 4 and row['M_Score'] >= 4:
                 return 'Champions'
@@ -135,83 +126,132 @@ class B2BCustomerAnalytics:
                 return 'Others'
         
         customer_df['Segment'] = customer_df.apply(segment_customers, axis=1)
-        
-        # Calculate churn risk
         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 segments back to original data
         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
     
-    def generate_summary_dashboard(self):
-        """Generate modern dashboard summary with KPI cards"""
+    def generate_modern_dashboard(self):
+        """Generate modern dashboard with clean design"""
         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()
         
-        # Segment and risk distributions
+        # 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()
         
-        # Create modern HTML summary
-        summary_html = f"""
-        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 2rem; border-radius: 1rem; color: white; margin-bottom: 2rem;">
-            <h2 style="font-size: 2rem; font-weight: bold; margin-bottom: 0.5rem; text-align: center;">
-                🏢 B2B Customer Analytics Dashboard
-            </h2>
-            <p style="text-align: center; font-size: 1.1rem; opacity: 0.9;">
+        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;">
+                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(280px, 1fr)); gap: 1.5rem; margin-bottom: 2rem;">
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #3b82f6;">
-                <h3 style="color: #1f2937; font-weight: 600; margin-bottom: 1rem;">📊 Data Overview</h3>
-                <p><strong>Total Customers:</strong> {total_customers:,}</p>
-                <p><strong>Total Orders:</strong> {total_orders:,}</p>
-                <p><strong>Total Revenue:</strong> ${total_revenue:,.2f}</p>
-                <p><strong>Avg Order Value:</strong> ${avg_order_value:.2f}</p>
+        <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>
+                    <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>
             </div>
             
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #10b981;">
-                <h3 style="color: #1f2937; font-weight: 600; margin-bottom: 1rem;">🎯 Customer Segments</h3>
-                {''.join([f'<p><strong>{segment}:</strong> {count}</p>' for segment, count in segment_dist.items()])}
+            <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>
+                    <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>
             </div>
             
-            <div style="background: white; padding: 1.5rem; border-radius: 1rem; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); border-left: 4px solid #ef4444;">
-                <h3 style="color: #1f2937; font-weight: 600; margin-bottom: 1rem;">⚠️ Churn Risk Analysis</h3>
-                {''.join([f'<p><strong>{risk} Risk:</strong> {count} customers</p>' for risk, count in risk_dist.items()])}
+            <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>
+                <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>
+            </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>
+                <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>
+            </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>
+                <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>
+            </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>
+                <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>
             </div>
         </div>
         """
         
-        # Create KPI cards 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"]
+        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"]
         ]
         
-        return summary_html, kpi_data
+        return dashboard_html, metrics_cards
     
     def train_churn_model(self):
-        """Train churn prediction model"""
+        """Train churn prediction model with modern UI feedback"""
         if self.df is None:
             return "No data available. Please upload a CSV file first.", None
         
         try:
-            # Prepare data for modeling
             customer_features = self.df.groupby('customer_id').agg({
                 'recency_days': 'first',
                 'frequency': 'first',
@@ -220,94 +260,84 @@ 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']
             
-            # Fill NaN 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)
             
-            # Create churn labels (if not present)
-            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')
+            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']
             
-            # Split data
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
             
-            # Train XGBoost 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]
+            accuracy = accuracy_score(y_test, y_pred)
             
-            # Calculate feature importance
             self.feature_importance = pd.DataFrame({
                 'feature': feature_cols,
                 'importance': self.model.feature_importances_
             }).sort_values('importance', ascending=False)
             
-            # Generate predictions for all customers
             all_predictions = self.model.predict_proba(X)[:, 1]
             customer_features['churn_probability'] = all_predictions
             self.predictions = customer_features
             
-            # Model performance
-            accuracy = accuracy_score(y_test, y_pred)
-            
-            # Create modern results display
+            # Modern results display
             results_html = f"""
-            <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;">
+            <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="text-align: center; margin-bottom: 2rem;">
-                    <h3 style="font-size: 1.5rem; font-weight: bold; color: #1f2937; margin-bottom: 0.5rem;">
-                        🤖 Model Training Completed
+                    <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>
+                    <h3 style="font-size: 1.75rem; font-weight: bold; color: #1f2937; margin-bottom: 0.5rem;">
+                        Model Training Completed
                     </h3>
-                    <p style="color: #6b7280;">XGBoost Classifier with Advanced Feature Engineering</p>
+                    <p style="color: #6b7280; font-size: 1.1rem;">XGBoost Classifier with Advanced Feature Engineering</p>
                 </div>
                 
-                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1rem; margin-bottom: 2rem;">
-                    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 1rem; border-radius: 0.5rem; text-align: center; color: white;">
-                        <div style="font-size: 1.5rem; font-weight: bold;">{accuracy:.1%}</div>
-                        <div style="font-size: 0.9rem; opacity: 0.9;">Model Accuracy</div>
+                <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: 1rem; opacity: 0.9;">Model Accuracy</div>
                     </div>
-                    <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 1rem; border-radius: 0.5rem; text-align: center; color: white;">
-                        <div style="font-size: 1.5rem; font-weight: bold;">{len(feature_cols)}</div>
-                        <div style="font-size: 0.9rem; opacity: 0.9;">Features Used</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: 1rem; opacity: 0.9;">Features Used</div>
                     </div>
-                    <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); padding: 1rem; border-radius: 0.5rem; text-align: center; color: white;">
-                        <div style="font-size: 1.5rem; font-weight: bold;">{len(X_train)}</div>
-                        <div style="font-size: 0.9rem; opacity: 0.9;">Training Samples</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: 1rem; opacity: 0.9;">Training Samples</div>
                     </div>
-                    <div style="background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%); padding: 1rem; border-radius: 0.5rem; text-align: center; color: white;">
-                        <div style="font-size: 1.5rem; font-weight: bold;">{len(X_test)}</div>
-                        <div style="font-size: 0.9rem; opacity: 0.9;">Test Samples</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: 1rem; opacity: 0.9;">Test Samples</div>
                     </div>
                 </div>
                 
-                <div style="background: #f8fafc; padding: 1.5rem; border-radius: 0.5rem;">
-                    <h4 style="font-weight: 600; color: #374151; margin-bottom: 1rem;">🔍 Top Feature Importance</h4>
-                    <div style="space-y: 0.5rem;">
-                        {''.join([f'''<div style="display: flex; justify-content: space-between; align-items: center; padding: 0.5rem 0; border-bottom: 1px solid #e5e7eb;">
-                            <span style="font-weight: 500; color: #374151;">{row['feature'].replace('_', ' ').title()}</span>
-                            <span style="background: #3b82f6; color: white; padding: 0.25rem 0.75rem; border-radius: 9999px; font-size: 0.875rem;">
-                                {row['importance']:.3f}
-                            </span>
+                <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;">
+                        {''.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>
                         </div>''' for _, row in self.feature_importance.head(5).iterrows()])}
                     </div>
                 </div>
@@ -320,7 +350,7 @@ class B2BCustomerAnalytics:
             return f"Error training model: {str(e)}", None
     
     def create_model_performance_chart(self):
-        """Create model performance visualization"""
+        """Create clean model performance visualization"""
         if self.feature_importance is None:
             return None
             
@@ -329,34 +359,36 @@ class B2BCustomerAnalytics:
             x='importance', 
             y='feature',
             orientation='h',
-            title='Feature Importance - XGBoost Model',
+            title='Feature Importance Analysis',
             labels={'importance': 'Importance Score', 'feature': 'Features'},
             color='importance',
-            color_continuous_scale='viridis'
+            color_continuous_scale=['#e0e7ff', '#6366f1']
         )
         
         fig.update_layout(
             height=400,
             showlegend=False,
             plot_bgcolor='white',
+            paper_bgcolor='white',
             title={
-                'text': 'Feature Importance - XGBoost Model',
+                'text': 'Feature Importance Analysis',
                 'x': 0.5,
                 'xanchor': 'center',
-                'font': {'size': 18, 'color': '#1f2937'}
+                'font': {'size': 18, 'color': '#1f2937', 'family': 'Inter, sans-serif'}
             },
-            font=dict(family="Inter, sans-serif"),
-            yaxis={'categoryorder': 'total ascending'}
+            font=dict(family="Inter, sans-serif", color='#374151'),
+            yaxis={'categoryorder': 'total ascending'},
+            margin=dict(l=20, r=20, t=60, b=20)
         )
         
         return fig
     
     def create_visualizations(self):
-        """Create comprehensive modern visualizations"""
+        """Create modern, clean visualizations"""
         if self.df is None:
             return None, None, None, None
         
-        # 1. Customer Segment Distribution (Donut Chart)
+        # 1. Customer Segment Distribution
         segment_data = self.df.groupby('customer_id')['Segment'].first().value_counts().reset_index()
         segment_data.columns = ['Segment', 'Count']
         
@@ -368,15 +400,22 @@ class B2BCustomerAnalytics:
             hole=0.4,
             color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899']
         )
-        fig1.update_traces(textposition='inside', textinfo='percent+label', textfont_size=12)
+        fig1.update_traces(
+            textposition='inside', 
+            textinfo='percent+label', 
+            textfont_size=12,
+            textfont_family='Inter'
+        )
         fig1.update_layout(
             height=400, 
             showlegend=True,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937'}},
-            font=dict(family="Inter, sans-serif")
+            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'
         )
         
-        # 2. RFM Analysis (3D Scatter)
+        # 2. RFM Analysis
         customer_rfm = self.df.groupby('customer_id').agg({
             'recency_days': 'first',
             'frequency': 'first', 
@@ -384,12 +423,12 @@ class B2BCustomerAnalytics:
             'Segment': 'first'
         }).reset_index()
         
-        fig2 = px.scatter_3d(
+        fig2 = px.scatter(
             customer_rfm, 
             x='recency_days', 
             y='frequency', 
-            z='monetary',
-            color='Segment', 
+            size='monetary',
+            color='Segment',
             title='RFM Analysis - Customer Behavior Matrix',
             labels={
                 'recency_days': 'Recency (Days)',
@@ -399,9 +438,11 @@ class B2BCustomerAnalytics:
             color_discrete_sequence=['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6']
         )
         fig2.update_layout(
-            height=500,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937'}},
-            font=dict(family="Inter, sans-serif")
+            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',
+            plot_bgcolor='white'
         )
         
         # 3. Churn Risk Analysis
@@ -412,9 +453,9 @@ class B2BCustomerAnalytics:
                 nbins=20,
                 title='Churn Probability Distribution',
                 labels={'churn_probability': 'Churn Probability', 'count': 'Number of Customers'},
-                color_discrete_sequence=[COLORS['primary']]
+                color_discrete_sequence=['#6366f1']
             )
-            fig3.add_vline(x=0.5, line_dash="dash", line_color="red", annotation_text="High Risk Threshold")
+            fig3.add_vline(x=0.5, line_dash="dash", line_color="#ef4444", annotation_text="High Risk Threshold")
         else:
             risk_data = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts().reset_index()
             risk_data.columns = ['Risk_Level', 'Count']
@@ -431,8 +472,9 @@ class B2BCustomerAnalytics:
         fig3.update_layout(
             height=400, 
             showlegend=False,
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937'}},
-            font=dict(family="Inter, sans-serif"),
+            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'
         )
         
@@ -449,566 +491,14 @@ class B2BCustomerAnalytics:
             labels={'amount': 'Revenue ($)', 'order_month': 'Month'},
             line_shape='spline'
         )
-        fig4.update_traces(line_color=COLORS['primary'], line_width=3)
+        fig4.update_traces(line_color='#6366f1', line_width=3)
         fig4.update_layout(
             height=400, 
-            title={'x': 0.5, 'xanchor': 'center', 'font': {'size': 18, 'color': '#1f2937'}},
-            font=dict(family="Inter, sans-serif"),
+            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',
             xaxis_tickangle=-45
         )
         
-        return fig1, fig2, fig3, fig4
-    
-    def create_customer_table(self):
-        """Create modern customer segmentation table"""
-        if self.df is None:
-            return None
-            
-        # Aggregate customer data for table
-        customer_summary = self.df.groupby('customer_id').agg({
-            'Segment': 'first',
-            'Churn_Risk': 'first',
-            'recency_days': 'first',
-            'frequency': 'first',
-            'monetary': 'first',
-            'amount': 'mean'
-        }).reset_index()
-        
-        # Add churn probability if available
-        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  # Default value
-        
-        # Format for display
-        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)
-        
-        # Rename columns for better display
-        customer_summary.columns = [
-            'Customer ID', 'Segment', 'Risk Level', 'Recency (Days)', 
-            'Frequency', 'Total Spent ($)', 'Avg Order ($)', 'Churn Probability (%)'
-        ]
-        
-        return customer_summary.head(50)  # Show top 50 customers
-    
-    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 = []
-            
-            # Title
-            story.append(Paragraph("B2B Customer Analytics Report", title_style))
-            story.append(Spacer(1, 20))
-            
-            # Executive Summary
-            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 ${avg_order_value:.2f}, indicating healthy transaction volumes.
-            
-            Critical findings reveal {high_risk_customers} customers at high risk of churning, representing significant revenue exposure. 
-            Our machine learning model achieved 78% accuracy in predicting customer churn, enabling proactive retention strategies.
-            
-            The customer segmentation analysis identifies distinct behavioral patterns, with Champions showing the highest lifetime value 
-            and lowest churn risk, while At Risk customers require immediate intervention to prevent revenue loss.
-            """
-            
-            story.append(Paragraph(summary_text, styles['Normal']))
-            story.append(Spacer(1, 20))
-            
-            # Key Metrics
-            story.append(Paragraph("Key Performance Indicators", styles['Heading2']))
-            
-            segment_dist = self.df.groupby('customer_id')['Segment'].first().value_counts()
-            risk_dist = self.df.groupby('customer_id')['Churn_Risk'].first().value_counts()
-            
-            metrics_data = [
-                ['Metric', 'Value', 'Status'],
-                ['Total Customers', f"{total_customers:,}", 'Baseline'],
-                ['Total Revenue', f"${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']
-            ]
-            
-            
-# Continue from where the code was cut off in generate_pdf_report method
-            
-            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))
-            
-            # Customer Segmentation Analysis
-            story.append(Paragraph("Customer Segmentation Analysis", styles['Heading2']))
-            
-            segmentation_text = """
-            Our RFM (Recency, Frequency, Monetary) analysis reveals distinct customer segments:
-            
-            • Champions: High-value, recent, and frequent customers - our most valuable segment
-            • Loyal Customers: Consistent purchasers with good transaction history
-            • Potential Loyalists: Recent customers with growth potential
-            • At Risk: Previously good customers showing declining engagement
-            • Cannot Lose Them: High-value customers with concerning recency patterns
-            """
-            
-            story.append(Paragraph(segmentation_text, styles['Normal']))
-            story.append(Spacer(1, 15))
-            
-            # Segment breakdown table
-            segment_data = [['Segment', 'Count', 'Percentage', 'Avg Revenue', 'Strategy']]
-            total_unique_customers = len(segment_dist)
-            
-            for segment, count in segment_dist.items():
-                avg_revenue = self.df[self.df['Segment'] == segment]['amount'].mean()
-                percentage = (count / total_unique_customers) * 100
-                
-                if segment == 'Champions':
-                    strategy = 'Reward & Retain'
-                elif segment == 'Loyal Customers':
-                    strategy = 'Upsell & Cross-sell'
-                elif segment == 'At Risk':
-                    strategy = 'Immediate Intervention'
-                elif segment == 'Potential Loyalists':
-                    strategy = 'Nurture & Develop'
-                else:
-                    strategy = 'Monitor & Engage'
-                
-                segment_data.append([
-                    segment, 
-                    str(count), 
-                    f"{percentage:.1f}%", 
-                    f"${avg_revenue:.2f}",
-                    strategy
-                ])
-            
-            segment_table = Table(segment_data, colWidths=[1.8*inch, 0.8*inch, 1*inch, 1*inch, 1.4*inch])
-            segment_table.setStyle(TableStyle([
-                ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#10b981')),
-                ('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), 10),
-                ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
-                ('BACKGROUND', (0, 1), (-1, -1), colors.lightblue),
-                ('GRID', (0, 0), (-1, -1), 1, colors.black),
-                ('FONTSIZE', (0, 1), (-1, -1), 9),
-                ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')
-            ]))
-            
-            story.append(segment_table)
-            story.append(PageBreak())
-            
-            # Churn Risk Analysis
-            story.append(Paragraph("Churn Risk Assessment", styles['Heading2']))
-            
-            churn_text = f"""
-            Machine Learning Model Performance:
-            Our XGBoost classifier achieved high accuracy in predicting customer churn probability.
-            
-            Risk Distribution:
-            • High Risk: {risk_dist.get('High', 0)} customers ({(risk_dist.get('High', 0)/total_unique_customers)*100:.1f}%)
-            • Medium Risk: {risk_dist.get('Medium', 0)} customers ({(risk_dist.get('Medium', 0)/total_unique_customers)*100:.1f}%)
-            • Low Risk: {risk_dist.get('Low', 0)} customers ({(risk_dist.get('Low', 0)/total_unique_customers)*100:.1f}%)
-            
-            Key Risk Factors:
-            """
-            
-            story.append(Paragraph(churn_text, styles['Normal']))
-            
-            if self.feature_importance is not None:
-                feature_text = "Top predictive features for churn:\n"
-                for _, row in self.feature_importance.head(5).iterrows():
-                    feature_text += f"• {row['feature'].replace('_', ' ').title()}: {row['importance']:.3f}\n"
-                story.append(Paragraph(feature_text, styles['Normal']))
-            
-            story.append(Spacer(1, 20))
-            
-            # Recommendations
-            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
-            
-            4. TECHNOLOGY INVESTMENTS:
-               • CRM integration for real-time scoring
-               • Marketing automation platform
-               • Customer success management tools
-               • Advanced analytics infrastructure
-            """
-            
-            story.append(Paragraph(recommendations_text, styles['Normal']))
-            story.append(Spacer(1, 20))
-            
-            # Footer
-            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']))
-            
-            # Build PDF
-            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"
-        
-        # Get customer metrics
-        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]
-        
-        # Get churn probability if available
-        churn_prob = 0.5  # default
-        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);">
-            <h3 style="color: #1f2937; font-size: 1.5rem; font-weight: bold; margin-bottom: 1.5rem; text-align: center;">
-                📊 Customer Profile: {customer_id}
-            </h3>
-            
-            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1rem; margin-bottom: 2rem;">
-                <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 1rem; border-radius: 0.5rem; color: white;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem;">SEGMENT</h4>
-                    <div style="font-size: 1.2rem; font-weight: bold;">{segment}</div>
-                </div>
-                <div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 1rem; border-radius: 0.5rem; color: white;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem;">CHURN RISK</h4>
-                    <div style="font-size: 1.2rem; font-weight: bold;">{risk_level}</div>
-                </div>
-                <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); padding: 1rem; border-radius: 0.5rem; color: white;">
-                    <h4 style="font-size: 0.9rem; opacity: 0.9; margin-bottom: 0.5rem;">CHURN PROBABILITY</h4>
-                    <div style="font-size: 1.2rem; font-weight: bold;">{churn_prob:.1%}</div>
-                </div>
-            </div>
-            
-            <div style="background: #f8fafc; padding: 1.5rem; border-radius: 0.5rem;">
-                <h4 style="color: #374151; font-weight: 600; margin-bottom: 1rem;">📈 Transaction Metrics</h4>
-                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1rem;">
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280;">Total Orders</div>
-                        <div style="font-size: 1.25rem; font-weight: bold; color: #1f2937;">{total_orders}</div>
-                    </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280;">Total Spent</div>
-                        <div style="font-size: 1.25rem; font-weight: bold; color: #1f2937;">${total_spent:,.2f}</div>
-                    </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280;">Avg Order Value</div>
-                        <div style="font-size: 1.25rem; font-weight: bold; color: #1f2937;">${avg_order_value:.2f}</div>
-                    </div>
-                    <div>
-                        <div style="font-size: 0.875rem; color: #6b7280;">Days Since Last Order</div>
-                        <div style="font-size: 1.25rem; font-weight: bold; color: #1f2937;">{recency}</div>
-                    </div>
-                </div>
-            </div>
-            
-            <div style="background: #f0f9ff; border-left: 4px solid #3b82f6; padding: 1rem; margin-top: 1rem;">
-                <h4 style="color: #1e40af; font-weight: 600; margin-bottom: 0.5rem;">💡 Recommendations</h4>
-                <p style="color: #1f2937; margin: 0;">
-                    {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."
-
-
-# Gradio Interface
-def create_gradio_interface():
-    """Create the 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)
-    
-    # Create Gradio interface
-    with gr.Blocks(
-        theme=gr.themes.Soft(primary_hue="blue"),
-        title="B2B Customer Analytics Platform",
-        css="""
-        .gradio-container {
-            font-family: 'Inter', sans-serif;
-        }
-        .main-header {
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            padding: 2rem;
-            border-radius: 1rem;
-            color: white;
-            text-align: center;
-            margin-bottom: 2rem;
-        }
-        .metric-card {
-            background: white;
-            padding: 1.5rem;
-            border-radius: 1rem;
-            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-            border-left: 4px solid #3b82f6;
-        }
-        """
-    ) as demo:
-        
-        gr.HTML("""
-        <div class="main-header">
-            <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;">
-                Advanced Customer Segmentation & Churn Prediction System
-            </p>
-        </div>
-        """)
-        
-        with gr.Tabs():
-            # Data Upload Tab
-            with gr.Tab("📊 Data Upload & Overview"):
-                with gr.Row():
-                    file_input = gr.File(label="Upload Customer Data CSV", file_types=[".csv"])
-                
-                with gr.Row():
-                    load_btn = gr.Button("Load & Process Data", variant="primary", size="lg")
-                
-                load_status = gr.HTML()
-                summary_display = gr.HTML()
-                data_preview = gr.DataFrame(label="Data Preview")
-                kpi_display = gr.HTML()
-            
-            # Analytics & Segmentation Tab
-            with gr.Tab("🎯 Customer Segmentation"):
-                with gr.Row():
-                    segment_chart = gr.Plot(label="Customer Segments")
-                    rfm_chart = gr.Plot(label="RFM Analysis")
-                
-                with gr.Row():
-                    customer_table = gr.DataFrame(label="Customer Segmentation Table")
-            
-            # Churn Prediction Tab
-            with gr.Tab("🤖 Churn Prediction"):
-                with gr.Row():
-                    train_btn = gr.Button("Train Churn Prediction Model", variant="primary", size="lg")
-                
-                model_results = gr.HTML()
-                
-                with gr.Row():
-                    performance_chart = gr.Plot(label="Model Performance")
-                    churn_chart = gr.Plot(label="Churn Risk Analysis")
-            
-            # Revenue Analytics Tab
-            with gr.Tab("💰 Revenue Analytics"):
-                with gr.Row():
-                    revenue_chart = gr.Plot(label="Revenue Trends")
-            
-            # Customer Insights Tab
-            with gr.Tab("🔍 Customer Insights"):
-                with gr.Row():
-                    customer_id_input = gr.Textbox(
-                        label="Enter Customer ID", 
-                        placeholder="e.g., CUST001"
-                    )
-                    insights_btn = gr.Button("Get Customer Insights", variant="primary")
-                
-                customer_insights = gr.HTML()
-            
-            # Report Generation Tab
-            with gr.Tab("📄 Reports"):
-                with gr.Row():
-                    report_btn = gr.Button("Generate PDF Report", variant="primary", size="lg")
-                
-                with gr.Row():
-                    report_download = gr.File(label="Download Report")
-                
-                gr.HTML("""
-                <div style="background: #f0f9ff; padding: 1.5rem; border-radius: 0.5rem; margin-top: 1rem;">
-                    <h3 style="color: #1e40af; margin-bottom: 1rem;">📋 Report Contents</h3>
-                    <ul style="color: #374151;">
-                        <li>Executive Summary with Key Metrics</li>
-                        <li>Customer Segmentation Analysis</li>
-                        <li>Churn Risk Assessment</li>
-                        <li>Revenue Trends and Patterns</li>
-                        <li>Strategic Recommendations</li>
-                        <li>Model Performance Metrics</li>
-                    </ul>
-                </div>
-                """)
-        
-        # Event handlers
-        load_btn.click(
-            fn=load_data,
-            inputs=[file_input],
-            outputs=[load_status, summary_display, data_preview, kpi_display]
-        )
-        
-        train_btn.click(
-            fn=train_model,
-            outputs=[model_results, performance_chart]
-        )
-        
-        # Auto-update visualizations when data is loaded
-        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__":
-    # Launch the application
-    demo = create_gradio_interface()
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+        return