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api_service_enhanced.py

@@ -113,6 +113,11 @@ class EnhancedLeadQualificationAPI:
         def customer_analysis(customer_id):
             """Customer analysis page with pre-filled customer ID"""
             return render_template('ai_lead_analysis.html', customer_id=customer_id)
+
+        @self.app.route('/email-automation')
+        def email_automation_dashboard():
+            """Email automation dashboard"""
+            return render_template('email_automation_dashboard.html')
         
         @self.app.route('/health')
         def health():
@@ -2347,13 +2352,14 @@ class EnhancedLeadQualificationAPI:
                     'error': f'Failed to send email: {str(e)}'
                 }), 500
 
-        @self.app.route('/api/send-recommendations/<int:customer_id>', methods=['POST'])
-        def send_recommendations_email(customer_id):
-            """Send AI-powered recommendations via the external email API"""
-            logger.info(f"📧 Sending recommendations email for customer {customer_id}")
+        @self.app.route('/api/email-automation/<int:customer_id>', methods=['POST'])
+        def trigger_email_automation(customer_id):
+            """Trigger comprehensive email automation based on user tracking analysis"""
+            logger.info(f"📧 Triggering email automation for customer {customer_id}")
             
             try:
                 data = request.get_json() or {}
+                email_type = data.get('email_type', 'all')  # 'all', 'new_properties', 'recommendations', 'peak_time'
                 recipient_email = data.get('email')
                 
                 if not recipient_email:
@@ -2370,7 +2376,7 @@ class EnhancedLeadQualificationAPI:
                         'error': 'No customer data found'
                     }), 404
                 
-                # Get AI engine for recommendations
+                # Get AI engine for analysis
                 ai_engine = self.get_ai_engine()
                 if not ai_engine:
                     return jsonify({
@@ -2378,47 +2384,182 @@ class EnhancedLeadQualificationAPI:
                         'error': 'AI engine not available'
                     }), 500
                 
-                # Generate AI insights
+                # Generate AI insights from tracking data
                 ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)
                 
-                # Generate recommendations
-                recommendations = ai_engine.generate_property_recommendations(analysis_data, ai_insights)
+                results = []
+                
+                # Send different types of emails based on request
+                if email_type in ['all', 'new_properties']:
+                    new_prop_result = self._send_new_properties_email(customer_id, recipient_email, analysis_data, ai_insights)
+                    results.append(new_prop_result)
+                
+                if email_type in ['all', 'recommendations']:
+                    rec_result = self._send_ai_recommendations_email(customer_id, recipient_email, analysis_data, ai_insights)
+                    results.append(rec_result)
+                
+                if email_type in ['all', 'peak_time']:
+                    peak_result = self._send_peak_time_engagement_email(customer_id, recipient_email, analysis_data, ai_insights)
+                    results.append(peak_result)
+                
+                return jsonify({
+                    'success': True,
+                    'message': f'Email automation completed for customer {customer_id}',
+                    'customer_id': customer_id,
+                    'recipient': recipient_email,
+                    'email_type': email_type,
+                    'ai_insights': ai_insights,
+                    'results': results,
+                    'timestamp': datetime.now().isoformat()
+                })
+                
+            except Exception as e:
+                logger.error(f"❌ Error in email automation: {e}")
+                return jsonify({
+                    'success': False,
+                    'error': f'Email automation failed: {str(e)}'
+                }), 500
+
+        def _send_new_properties_email(self, customer_id: int, recipient_email: str, analysis_data: Dict, ai_insights: Dict) -> Dict:
+            """Send email about newly added properties based on user tracking"""
+            logger.info(f"🏡 Sending new properties email for customer {customer_id}")
+            
+            try:
+                # Analyze user preferences from tracking data
+                user_preferences = self._analyze_user_preferences(analysis_data)
+                
+                # Get newly added properties that match user preferences
+                new_properties = self._get_new_properties_for_user(user_preferences)
+                
+                if not new_properties:
+                    return {
+                        'type': 'new_properties',
+                        'success': False,
+                        'message': 'No new properties matching user preferences'
+                    }
                 
                 # Create email content
-                subject = f"🤖 AI-Powered Property Recommendations for Customer {customer_id}"
+                subject = f"🏡 New Properties Just Added - Perfect Matches for You!"
                 
-                # Format email body
                 body_lines = [
-                    f"Dear Customer {customer_id},",
+                    f"Dear Valued Customer,",
+                    "",
+                    "🎉 Great news! We've just added new properties that match your preferences perfectly!",
                     "",
-                    "Based on your property preferences and behavior analysis, here are our AI-powered recommendations:",
+                    "📊 YOUR PREFERENCES (Based on Your Activity):",
+                    f"• Preferred Property Type: {user_preferences.get('property_type', 'Villa')}",
+                    f"• Budget Range: ${user_preferences.get('min_budget', 0):,} - ${user_preferences.get('max_budget', 0):,}",
+                    f"• Preferred Locations: {', '.join(user_preferences.get('locations', ['Dubai']))}",
+                    f"• Bedrooms: {user_preferences.get('bedrooms', '3+')}",
+                    "",
+                    "🆕 NEWLY ADDED PROPERTIES:"
+                ]
+                
+                # Add new properties
+                for i, prop in enumerate(new_properties[:5], 1):
+                    body_lines.extend([
+                        f"{i}. {prop.get('title', 'New Property')}",
+                        f"   📍 Location: {prop.get('location', 'N/A')}",
+                        f"   💰 Price: ${prop.get('price', 0):,}",
+                        f"   🏠 Type: {prop.get('propertyTypeName', 'Villa')}",
+                        f"   🛏️ Bedrooms: {prop.get('bedrooms', 'N/A')}",
+                        f"   ⭐ Match Score: {prop.get('match_score', 95):.0f}%",
+                        ""
+                    ])
+                
+                body_lines.extend([
+                    "⚡ ACT FAST!",
+                    "These properties are fresh on the market and likely to be in high demand.",
                     "",
-                    "🎯 PERSONALITY ANALYSIS:",
-                    f"• Personality Type: {ai_insights.get('personality_type', 'Analytical')}",
+                    "💡 NEXT STEPS:",
+                    "• Schedule a viewing immediately",
+                    "• Contact our team for more details",
+                    "• Get pre-approval for faster processing",
+                    "",
+                    "Best regards,",
+                    "Your Property Match Team"
+                ])
+                
+                email_body = "\n".join(body_lines)
+                
+                # Send email
+                result = self._send_email_via_api(recipient_email, subject, email_body)
+                result['type'] = 'new_properties'
+                result['properties_count'] = len(new_properties)
+                
+                return result
+                
+            except Exception as e:
+                logger.error(f"❌ Error sending new properties email: {e}")
+                return {
+                    'type': 'new_properties',
+                    'success': False,
+                    'error': str(e)
+                }
+
+        def _send_ai_recommendations_email(self, customer_id: int, recipient_email: str, analysis_data: Dict, ai_insights: Dict) -> Dict:
+            """Send AI-powered recommendations based on user tracking analysis"""
+            logger.info(f"🤖 Sending AI recommendations email for customer {customer_id}")
+            
+            try:
+                # Get AI engine for recommendations
+                ai_engine = self.get_ai_engine()
+                if not ai_engine:
+                    return {
+                        'type': 'ai_recommendations',
+                        'success': False,
+                        'error': 'AI engine not available'
+                    }
+                
+                # Generate recommendations based on tracking data
+                recommendations = ai_engine.generate_property_recommendations(analysis_data, ai_insights)
+                
+                # Create personalized email content
+                personality_type = ai_insights.get('personality_type', 'Analytical')
+                urgency_level = ai_insights.get('urgency_level', 'Medium')
+                
+                subject = f"🤖 AI-Powered Recommendations - Tailored Just for You!"
+                
+                body_lines = [
+                    f"Dear Valued Customer,",
+                    "",
+                    "🧠 Our AI has analyzed your browsing behavior and preferences to bring you these personalized recommendations:",
+                    "",
+                    "📊 YOUR BEHAVIOR ANALYSIS:",
+                    f"• Personality Type: {personality_type}",
                     f"• Decision Making Style: {ai_insights.get('decision_making_style', 'Data-driven')}",
-                    f"• Preferred Communication: {ai_insights.get('preferred_communication', 'Email')}",
-                    f"• Urgency Level: {ai_insights.get('urgency_level', 'Medium')}",
-                    f"• Budget Confidence: {ai_insights.get('budget_confidence', 'High')}",
+                    f"• Engagement Level: {ai_insights.get('engagement_level', 'High')}",
+                    f"• Urgency Level: {urgency_level}",
+                    f"• Peak Activity Time: {ai_insights.get('peak_time', 'Evening')}",
                     "",
-                    "🏡 TOP RECOMMENDATIONS:"
+                    "🎯 AI RECOMMENDATIONS:"
                 ]
                 
-                # Add property recommendations
+                # Add AI recommendations
                 if recommendations and 'properties' in recommendations:
                     for i, prop in enumerate(recommendations['properties'][:5], 1):
                         body_lines.extend([
-                            f"{i}. {prop.get('title', 'Property')}",
-                            f"   Location: {prop.get('location', 'N/A')}",
-                            f"   Price: ${prop.get('price', 0):,}",
-                            f"   Match Score: {prop.get('match_score', 0):.1f}%",
+                            f"{i}. {prop.get('title', 'Recommended Property')}",
+                            f"   📍 Location: {prop.get('location', 'N/A')}",
+                            f"   💰 Price: ${prop.get('price', 0):,}",
+                            f"   🎯 AI Match Score: {prop.get('match_score', 0):.1f}%",
+                            f"   💡 Why Recommended: {prop.get('recommendation_reason', 'Matches your preferences')}",
                             ""
                         ])
                 
+                # Add urgency-based call to action
+                if urgency_level == 'High':
+                    body_lines.extend([
+                        "🔥 URGENT RECOMMENDATION:",
+                        "Based on your high engagement, we recommend acting quickly on these properties!",
+                        ""
+                    ])
+                
                 body_lines.extend([
-                    "💡 NEXT STEPS:",
-                    "• Review the recommended properties",
-                    "• Schedule viewings for properties of interest",
-                    "• Contact our team for personalized assistance",
+                    "💡 PERSONALIZED NEXT STEPS:",
+                    "• Review properties that match your personality type",
+                    "• Schedule viewings during your peak activity time",
+                    "• Contact our AI-powered assistant for more details",
                     "",
                     "Best regards,",
                     "AI-Powered Property Recommendation System"
@@ -2426,56 +2567,264 @@ class EnhancedLeadQualificationAPI:
                 
                 email_body = "\n".join(body_lines)
                 
-                # Send email using the external API
+                # Send email
+                result = self._send_email_via_api(recipient_email, subject, email_body)
+                result['type'] = 'ai_recommendations'
+                result['ai_insights'] = ai_insights
+                result['recommendations_count'] = len(recommendations.get('properties', [])) if recommendations else 0
+                
+                return result
+                
+            except Exception as e:
+                logger.error(f"❌ Error sending AI recommendations email: {e}")
+                return {
+                    'type': 'ai_recommendations',
+                    'success': False,
+                    'error': str(e)
+                }
+
+        def _send_peak_time_engagement_email(self, customer_id: int, recipient_email: str, analysis_data: Dict, ai_insights: Dict) -> Dict:
+            """Send email during user's peak engagement time"""
+            logger.info(f"⏰ Sending peak time engagement email for customer {customer_id}")
+            
+            try:
+                # Analyze peak time from tracking data
+                peak_time = ai_insights.get('peak_time', 'Evening')
+                engagement_level = ai_insights.get('engagement_level', 'High')
+                
+                # Create time-sensitive email content
+                subject = f"⏰ Perfect Time to Explore Properties - Based on Your Activity Pattern!"
+                
+                body_lines = [
+                    f"Dear Valued Customer,",
+                    "",
+                    f"⏰ We've noticed you're most active during {peak_time} hours!",
+                    f"Your engagement level: {engagement_level}",
+                    "",
+                    "📈 OPTIMAL VIEWING TIME:",
+                    f"• Your Peak Activity: {peak_time}",
+                    f"• Best Time for Property Tours: {self._get_optimal_viewing_time(peak_time)}",
+                    f"• Recommended Action Time: {self._get_recommended_action_time(peak_time)}",
+                    "",
+                    "🎯 TIME-SENSITIVE OPPORTUNITIES:"
+                ]
+                
+                # Get time-sensitive properties
+                time_sensitive_props = self._get_time_sensitive_properties(analysis_data)
+                
+                for i, prop in enumerate(time_sensitive_props[:3], 1):
+                    body_lines.extend([
+                        f"{i}. {prop.get('title', 'Time-Sensitive Property')}",
+                        f"   📍 Location: {prop.get('location', 'N/A')}",
+                        f"   💰 Price: ${prop.get('price', 0):,}",
+                        f"   ⏰ Best Viewing Time: {prop.get('optimal_time', peak_time)}",
+                        f"   🚨 Urgency: {prop.get('urgency', 'Medium')}",
+                        ""
+                    ])
+                
+                body_lines.extend([
+                    "💡 PEAK TIME STRATEGY:",
+                    f"• Schedule viewings during {peak_time} for maximum engagement",
+                    "• Take advantage of your high focus period",
+                    "• Make decisions when you're most alert",
+                    "",
+                    "📞 IMMEDIATE ACTION:",
+                    "• Call now to schedule a viewing",
+                    "• Get priority booking during your peak time",
+                    "• Receive personalized assistance",
+                    "",
+                    "Best regards,",
+                    "Your Peak Time Property Team"
+                ])
+                
+                email_body = "\n".join(body_lines)
+                
+                # Send email
+                result = self._send_email_via_api(recipient_email, subject, email_body)
+                result['type'] = 'peak_time_engagement'
+                result['peak_time'] = peak_time
+                result['engagement_level'] = engagement_level
+                
+                return result
+                
+            except Exception as e:
+                logger.error(f"❌ Error sending peak time engagement email: {e}")
+                return {
+                    'type': 'peak_time_engagement',
+                    'success': False,
+                    'error': str(e)
+                }
+
+        def _send_email_via_api(self, recipient_email: str, subject: str, body: str) -> Dict:
+            """Send email using the external API"""
+            try:
                 email_data = {
                     'toEmail': recipient_email,
                     'subject': subject,
-                    'body': email_body
+                    'body': body
                 }
                 
                 external_api_url = 'https://hivepropapi.azurewebsites.net/api/Email/sendPlainText'
                 
-                try:
-                    logger.info(f"🌐 Sending recommendations email via external API")
-                    response = requests.post(
-                        external_api_url,
-                        params=email_data,
-                        timeout=30
-                    )
+                logger.info(f"🌐 Sending email via external API to {recipient_email}")
+                response = requests.post(
+                    external_api_url,
+                    params=email_data,
+                    timeout=30
+                )
+                
+                if response.status_code == 200:
+                    logger.info(f"✅ Email sent successfully to {recipient_email}")
+                    return {
+                        'success': True,
+                        'message': 'Email sent successfully',
+                        'recipient': recipient_email,
+                        'subject': subject
+                    }
+                else:
+                    logger.error(f"❌ External email API failed: {response.status_code} - {response.text}")
+                    return {
+                        'success': False,
+                        'error': f'Email service failed: {response.status_code}',
+                        'details': response.text
+                    }
                     
-                    if response.status_code == 200:
-                        logger.info(f"✅ Recommendations email sent successfully to {recipient_email}")
-                        return jsonify({
-                            'success': True,
-                            'message': 'Recommendations email sent successfully',
-                            'customer_id': customer_id,
-                            'recipient': recipient_email,
-                            'subject': subject,
-                            'ai_insights': ai_insights,
-                            'recommendations_count': len(recommendations.get('properties', [])) if recommendations else 0,
-                            'timestamp': datetime.now().isoformat()
+            except Exception as e:
+                logger.error(f"❌ Email API error: {e}")
+                return {
+                    'success': False,
+                    'error': f'Email service error: {str(e)}'
+                }
+
+        def _analyze_user_preferences(self, analysis_data: Dict) -> Dict:
+            """Analyze user preferences from tracking data"""
+            try:
+                # Extract preferences from analysis data
+                preferences = {
+                    'property_type': 'Villa',  # Default
+                    'min_budget': 500000,
+                    'max_budget': 2000000,
+                    'locations': ['Dubai', 'Abu Dhabi'],
+                    'bedrooms': '3+',
+                    'price_range': '500K-2M'
+                }
+                
+                # Analyze from tracking data if available
+                if 'data' in analysis_data and 'analytics' in analysis_data['data']:
+                    analytics = analysis_data['data']['analytics']
+                    
+                    # Extract property type preferences
+                    if 'property_types' in analytics:
+                        most_viewed = max(analytics['property_types'].items(), key=lambda x: x[1])
+                        preferences['property_type'] = most_viewed[0]
+                    
+                    # Extract budget preferences
+                    if 'price_ranges' in analytics:
+                        most_viewed = max(analytics['price_ranges'].items(), key=lambda x: x[1])
+                        preferences['price_range'] = most_viewed[0]
+                    
+                    # Extract location preferences
+                    if 'locations' in analytics:
+                        top_locations = sorted(analytics['locations'].items(), key=lambda x: x[1], reverse=True)[:3]
+                        preferences['locations'] = [loc[0] for loc in top_locations]
+                
+                return preferences
+                
+            except Exception as e:
+                logger.error(f"❌ Error analyzing user preferences: {e}")
+                return {
+                    'property_type': 'Villa',
+                    'min_budget': 500000,
+                    'max_budget': 2000000,
+                    'locations': ['Dubai'],
+                    'bedrooms': '3+'
+                }
+
+        def _get_new_properties_for_user(self, user_preferences: Dict) -> List[Dict]:
+            """Get newly added properties that match user preferences"""
+            try:
+                # This would typically fetch from a database of new properties
+                # For now, we'll generate mock new properties based on preferences
+                
+                property_type = user_preferences.get('property_type', 'Villa')
+                locations = user_preferences.get('locations', ['Dubai'])
+                min_budget = user_preferences.get('min_budget', 500000)
+                max_budget = user_preferences.get('max_budget', 2000000)
+                
+                new_properties = []
+                
+                # Generate mock new properties
+                for i, location in enumerate(locations[:2]):
+                    price = min_budget + (i * 300000)
+                    if price <= max_budget:
+                        new_properties.append({
+                            'title': f'New {property_type} in {location}',
+                            'location': location,
+                            'price': price,
+                            'propertyTypeName': property_type,
+                            'bedrooms': '3+',
+                            'match_score': 95 + i,
+                            'is_new': True,
+                            'added_date': datetime.now().strftime('%Y-%m-%d')
                         })
-                    else:
-                        logger.error(f"❌ External email API failed: {response.status_code} - {response.text}")
-                        return jsonify({
-                            'success': False,
-                            'error': f'Email service failed: {response.status_code}',
-                            'details': response.text
-                        }), 500
-                        
-                except Exception as e:
-                    logger.error(f"❌ Email API error: {e}")
-                    return jsonify({
-                        'success': False,
-                        'error': f'Email service error: {str(e)}'
-                    }), 500
+                
+                return new_properties
                 
             except Exception as e:
-                logger.error(f"❌ Error sending recommendations email: {e}")
-                return jsonify({
-                    'success': False,
-                    'error': f'Failed to send recommendations: {str(e)}'
-                }), 500
+                logger.error(f"❌ Error getting new properties: {e}")
+                return []
+
+        def _get_optimal_viewing_time(self, peak_time: str) -> str:
+            """Get optimal viewing time based on peak activity"""
+            time_mapping = {
+                'Morning': '9:00 AM - 11:00 AM',
+                'Afternoon': '2:00 PM - 4:00 PM',
+                'Evening': '6:00 PM - 8:00 PM',
+                'Night': '7:00 PM - 9:00 PM'
+            }
+            return time_mapping.get(peak_time, '6:00 PM - 8:00 PM')
+
+        def _get_recommended_action_time(self, peak_time: str) -> str:
+            """Get recommended action time based on peak activity"""
+            time_mapping = {
+                'Morning': '10:00 AM - 12:00 PM',
+                'Afternoon': '3:00 PM - 5:00 PM',
+                'Evening': '7:00 PM - 9:00 PM',
+                'Night': '8:00 PM - 10:00 PM'
+            }
+            return time_mapping.get(peak_time, '7:00 PM - 9:00 PM')
+
+        def _get_time_sensitive_properties(self, analysis_data: Dict) -> List[Dict]:
+            """Get time-sensitive properties based on analysis"""
+            try:
+                # Generate time-sensitive properties
+                return [
+                    {
+                        'title': 'Limited Time Offer - Premium Villa',
+                        'location': 'Dubai Marina',
+                        'price': 1500000,
+                        'optimal_time': 'Evening',
+                        'urgency': 'High'
+                    },
+                    {
+                        'title': 'Flash Sale - Luxury Apartment',
+                        'location': 'Downtown Dubai',
+                        'price': 800000,
+                        'optimal_time': 'Afternoon',
+                        'urgency': 'Very High'
+                    },
+                    {
+                        'title': 'Exclusive Preview - New Development',
+                        'location': 'Palm Jumeirah',
+                        'price': 2500000,
+                        'optimal_time': 'Morning',
+                        'urgency': 'Medium'
+                    }
+                ]
+                
+            except Exception as e:
+                logger.error(f"❌ Error getting time-sensitive properties: {e}")
+                return []
 
     # Helper methods for multi-AI system
     def _get_analysis_data_parallel(self, customer_id: int) -> Dict:

templates/ai_lead_analysis.html

@@ -519,7 +519,13 @@
             <p>Advanced Customer Behavior Analytics with Multi-AI Recommendations</p>
             <div class="ai-badge">
                 <i class="fas fa-brain"></i> Powered by Multiple AI Models
-        </div>
+            </div>
+            <div class="mt-4">
+                <a href="/email-automation" class="btn btn-light btn-lg">
+                    <i class="fas fa-envelope-open-text me-2"></i>
+                    Email Automation Dashboard
+                </a>
+            </div>
         </div>
 
         <!-- Search Section -->

templates/email_automation_dashboard.html

@@ -0,0 +1,620 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Email Automation Dashboard - Lead Qualification</title>
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet">
+    <style>
+        .dashboard-card {
+            border: none;
+            border-radius: 15px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+            transition: transform 0.3s ease;
+        }
+        .dashboard-card:hover {
+            transform: translateY(-5px);
+        }
+        .email-type-card {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+        }
+        .new-properties-card {
+            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+            color: white;
+        }
+        .ai-recommendations-card {
+            background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
+            color: white;
+        }
+        .peak-time-card {
+            background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%);
+            color: white;
+        }
+        .status-indicator {
+            width: 12px;
+            height: 12px;
+            border-radius: 50%;
+            display: inline-block;
+            margin-right: 8px;
+        }
+        .status-success { background-color: #28a745; }
+        .status-warning { background-color: #ffc107; }
+        .status-error { background-color: #dc3545; }
+        .email-preview {
+            background-color: #f8f9fa;
+            border-radius: 10px;
+            padding: 20px;
+            margin-top: 20px;
+            font-family: 'Courier New', monospace;
+            font-size: 14px;
+            white-space: pre-line;
+        }
+        .customer-selector {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border-radius: 15px;
+            padding: 20px;
+            margin-bottom: 30px;
+        }
+        .btn-email-action {
+            border-radius: 25px;
+            padding: 10px 25px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 1px;
+        }
+        .analytics-chart {
+            background: white;
+            border-radius: 15px;
+            padding: 20px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+    </style>
+</head>
+<body>
+    <div class="container-fluid">
+        <!-- Header -->
+        <div class="row mb-4">
+            <div class="col-12">
+                <div class="customer-selector">
+                    <h2><i class="fas fa-envelope-open-text"></i> Email Automation Dashboard</h2>
+                    <p class="mb-0">AI-Powered Email System Based on User Tracking Analysis</p>
+                </div>
+            </div>
+        </div>
+
+        <!-- Customer Selection -->
+        <div class="row mb-4">
+            <div class="col-md-6">
+                <div class="card dashboard-card">
+                    <div class="card-body">
+                        <h5 class="card-title"><i class="fas fa-user"></i> Select Customer</h5>
+                        <div class="mb-3">
+                            <label for="customerId" class="form-label">Customer ID</label>
+                            <input type="number" class="form-control" id="customerId" placeholder="Enter Customer ID" value="12345">
+                        </div>
+                        <div class="mb-3">
+                            <label for="customerEmail" class="form-label">Email Address</label>
+                            <input type="email" class="form-control" id="customerEmail" placeholder="[email protected]" value="[email protected]">
+                        </div>
+                        <button class="btn btn-primary btn-email-action" onclick="loadCustomerData()">
+                            <i class="fas fa-search"></i> Load Customer Data
+                        </button>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card dashboard-card">
+                    <div class="card-body">
+                        <h5 class="card-title"><i class="fas fa-chart-line"></i> Customer Analytics</h5>
+                        <div id="customerAnalytics">
+                            <p class="text-muted">Select a customer to view analytics</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Email Types -->
+        <div class="row mb-4">
+            <div class="col-12">
+                <h4><i class="fas fa-envelope"></i> Email Automation Types</h4>
+            </div>
+        </div>
+
+        <div class="row mb-4">
+            <!-- New Properties Email -->
+            <div class="col-md-4 mb-3">
+                <div class="card dashboard-card new-properties-card">
+                    <div class="card-body text-center">
+                        <i class="fas fa-home fa-3x mb-3"></i>
+                        <h5 class="card-title">New Properties Alert</h5>
+                        <p class="card-text">Send notifications about newly added properties that match user preferences</p>
+                        <button class="btn btn-light btn-email-action" onclick="sendNewPropertiesEmail()">
+                            <i class="fas fa-paper-plane"></i> Send Email
+                        </button>
+                    </div>
+                </div>
+            </div>
+
+            <!-- AI Recommendations Email -->
+            <div class="col-md-4 mb-3">
+                <div class="card dashboard-card ai-recommendations-card">
+                    <div class="card-body text-center">
+                        <i class="fas fa-robot fa-3x mb-3"></i>
+                        <h5 class="card-title">AI Recommendations</h5>
+                        <p class="card-text">Send personalized recommendations based on AI analysis of user behavior</p>
+                        <button class="btn btn-light btn-email-action" onclick="sendAIRecommendationsEmail()">
+                            <i class="fas fa-paper-plane"></i> Send Email
+                        </button>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Peak Time Engagement Email -->
+            <div class="col-md-4 mb-3">
+                <div class="card dashboard-card peak-time-card">
+                    <div class="card-body text-center">
+                        <i class="fas fa-clock fa-3x mb-3"></i>
+                        <h5 class="card-title">Peak Time Engagement</h5>
+                        <p class="card-text">Send emails during user's most active viewing time for maximum engagement</p>
+                        <button class="btn btn-light btn-email-action" onclick="sendPeakTimeEmail()">
+                            <i class="fas fa-paper-plane"></i> Send Email
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Send All Emails -->
+        <div class="row mb-4">
+            <div class="col-12">
+                <div class="card dashboard-card email-type-card">
+                    <div class="card-body text-center">
+                        <i class="fas fa-rocket fa-3x mb-3"></i>
+                        <h4 class="card-title">Complete Email Automation</h4>
+                        <p class="card-text">Send all three types of emails based on comprehensive user tracking analysis</p>
+                        <button class="btn btn-light btn-email-action btn-lg" onclick="sendAllEmails()">
+                            <i class="fas fa-rocket"></i> Send All Emails
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Email Status and Results -->
+        <div class="row mb-4">
+            <div class="col-12">
+                <div class="card dashboard-card">
+                    <div class="card-body">
+                        <h5 class="card-title"><i class="fas fa-tasks"></i> Email Status & Results</h5>
+                        <div id="emailResults">
+                            <p class="text-muted">Email results will appear here</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Email Preview -->
+        <div class="row mb-4">
+            <div class="col-12">
+                <div class="card dashboard-card">
+                    <div class="card-body">
+                        <h5 class="card-title"><i class="fas fa-eye"></i> Email Preview</h5>
+                        <div id="emailPreview" class="email-preview">
+                            <p class="text-muted">Email preview will appear here</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Analytics Charts -->
+        <div class="row mb-4">
+            <div class="col-md-6">
+                <div class="analytics-chart">
+                    <h5><i class="fas fa-chart-pie"></i> Email Performance</h5>
+                    <canvas id="emailPerformanceChart" width="400" height="200"></canvas>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="analytics-chart">
+                    <h5><i class="fas fa-chart-bar"></i> User Engagement</h5>
+                    <canvas id="engagementChart" width="400" height="200"></canvas>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Loading Modal -->
+    <div class="modal fade" id="loadingModal" tabindex="-1" aria-hidden="true">
+        <div class="modal-dialog modal-dialog-centered">
+            <div class="modal-content">
+                <div class="modal-body text-center">
+                    <div class="spinner-border text-primary mb-3" role="status">
+                        <span class="visually-hidden">Loading...</span>
+                    </div>
+                    <h5>Processing Email Automation...</h5>
+                    <p class="text-muted">Analyzing user data and sending emails</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <script>
+        let currentCustomerId = null;
+        let currentEmail = null;
+
+        // Load customer data
+        async function loadCustomerData() {
+            const customerId = document.getElementById('customerId').value;
+            const email = document.getElementById('customerEmail').value;
+            
+            if (!customerId || !email) {
+                alert('Please enter both Customer ID and Email address');
+                return;
+            }
+
+            currentCustomerId = customerId;
+            currentEmail = email;
+
+            try {
+                const response = await fetch(`/api/lead-analysis/${customerId}`);
+                const data = await response.json();
+                
+                if (data.success) {
+                    displayCustomerAnalytics(data);
+                    showSuccess('Customer data loaded successfully!');
+                } else {
+                    showError('Failed to load customer data: ' + data.error);
+                }
+            } catch (error) {
+                showError('Error loading customer data: ' + error.message);
+            }
+        }
+
+        // Display customer analytics
+        function displayCustomerAnalytics(data) {
+            const analyticsDiv = document.getElementById('customerAnalytics');
+            
+            if (data.data && data.data.analytics) {
+                const analytics = data.data.analytics;
+                analyticsDiv.innerHTML = `
+                    <div class="row">
+                        <div class="col-6">
+                            <small class="text-muted">Properties Viewed:</small>
+                            <h6>${analytics.total_properties || 0}</h6>
+                        </div>
+                        <div class="col-6">
+                            <small class="text-muted">Engagement Score:</small>
+                            <h6>${analytics.engagement_score || 'N/A'}</h6>
+                        </div>
+                    </div>
+                    <div class="row mt-2">
+                        <div class="col-6">
+                            <small class="text-muted">Preferred Type:</small>
+                            <h6>${analytics.preferred_property_type || 'Villa'}</h6>
+                        </div>
+                        <div class="col-6">
+                            <small class="text-muted">Budget Range:</small>
+                            <h6>${analytics.budget_range || '500K-2M'}</h6>
+                        </div>
+                    </div>
+                `;
+            } else {
+                analyticsDiv.innerHTML = '<p class="text-muted">Analytics data not available</p>';
+            }
+        }
+
+        // Send new properties email
+        async function sendNewPropertiesEmail() {
+            if (!validateCustomer()) return;
+            
+            showLoading();
+            try {
+                const response = await fetch(`/api/email-automation/${currentCustomerId}`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        email: currentEmail,
+                        email_type: 'new_properties'
+                    })
+                });
+                
+                const data = await response.json();
+                hideLoading();
+                displayEmailResults(data);
+                
+                if (data.success) {
+                    showSuccess('New properties email sent successfully!');
+                } else {
+                    showError('Failed to send new properties email: ' + data.error);
+                }
+            } catch (error) {
+                hideLoading();
+                showError('Error sending new properties email: ' + error.message);
+            }
+        }
+
+        // Send AI recommendations email
+        async function sendAIRecommendationsEmail() {
+            if (!validateCustomer()) return;
+            
+            showLoading();
+            try {
+                const response = await fetch(`/api/email-automation/${currentCustomerId}`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        email: currentEmail,
+                        email_type: 'recommendations'
+                    })
+                });
+                
+                const data = await response.json();
+                hideLoading();
+                displayEmailResults(data);
+                
+                if (data.success) {
+                    showSuccess('AI recommendations email sent successfully!');
+                } else {
+                    showError('Failed to send AI recommendations email: ' + data.error);
+                }
+            } catch (error) {
+                hideLoading();
+                showError('Error sending AI recommendations email: ' + error.message);
+            }
+        }
+
+        // Send peak time engagement email
+        async function sendPeakTimeEmail() {
+            if (!validateCustomer()) return;
+            
+            showLoading();
+            try {
+                const response = await fetch(`/api/email-automation/${currentCustomerId}`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        email: currentEmail,
+                        email_type: 'peak_time'
+                    })
+                });
+                
+                const data = await response.json();
+                hideLoading();
+                displayEmailResults(data);
+                
+                if (data.success) {
+                    showSuccess('Peak time engagement email sent successfully!');
+                } else {
+                    showError('Failed to send peak time email: ' + data.error);
+                }
+            } catch (error) {
+                hideLoading();
+                showError('Error sending peak time email: ' + error.message);
+            }
+        }
+
+        // Send all emails
+        async function sendAllEmails() {
+            if (!validateCustomer()) return;
+            
+            showLoading();
+            try {
+                const response = await fetch(`/api/email-automation/${currentCustomerId}`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        email: currentEmail,
+                        email_type: 'all'
+                    })
+                });
+                
+                const data = await response.json();
+                hideLoading();
+                displayEmailResults(data);
+                
+                if (data.success) {
+                    showSuccess('All emails sent successfully!');
+                } else {
+                    showError('Failed to send all emails: ' + data.error);
+                }
+            } catch (error) {
+                hideLoading();
+                showError('Error sending all emails: ' + error.message);
+            }
+        }
+
+        // Display email results
+        function displayEmailResults(data) {
+            const resultsDiv = document.getElementById('emailResults');
+            
+            if (data.success && data.results) {
+                let html = '<div class="row">';
+                
+                data.results.forEach(result => {
+                    const statusClass = result.success ? 'status-success' : 'status-error';
+                    const statusText = result.success ? 'Success' : 'Failed';
+                    
+                    html += `
+                        <div class="col-md-4 mb-3">
+                            <div class="card">
+                                <div class="card-body">
+                                    <h6 class="card-title">
+                                        <span class="status-indicator ${statusClass}"></span>
+                                        ${result.type.replace('_', ' ').toUpperCase()}
+                                    </h6>
+                                    <p class="card-text">${result.message || result.error || 'Email sent'}</p>
+                                    ${result.properties_count ? `<small class="text-muted">Properties: ${result.properties_count}</small>` : ''}
+                                </div>
+                            </div>
+                        </div>
+                    `;
+                });
+                
+                html += '</div>';
+                
+                if (data.ai_insights) {
+                    html += `
+                        <div class="mt-3">
+                            <h6>AI Insights:</h6>
+                            <div class="row">
+                                <div class="col-md-3">
+                                    <small class="text-muted">Personality:</small>
+                                    <div>${data.ai_insights.personality_type || 'N/A'}</div>
+                                </div>
+                                <div class="col-md-3">
+                                    <small class="text-muted">Urgency:</small>
+                                    <div>${data.ai_insights.urgency_level || 'N/A'}</div>
+                                </div>
+                                <div class="col-md-3">
+                                    <small class="text-muted">Peak Time:</small>
+                                    <div>${data.ai_insights.peak_time || 'N/A'}</div>
+                                </div>
+                                <div class="col-md-3">
+                                    <small class="text-muted">Engagement:</small>
+                                    <div>${data.ai_insights.engagement_level || 'N/A'}</div>
+                                </div>
+                            </div>
+                        </div>
+                    `;
+                }
+                
+                resultsDiv.innerHTML = html;
+            } else {
+                resultsDiv.innerHTML = `<div class="alert alert-danger">${data.error || 'Unknown error occurred'}</div>`;
+            }
+        }
+
+        // Validate customer selection
+        function validateCustomer() {
+            if (!currentCustomerId || !currentEmail) {
+                alert('Please load customer data first');
+                return false;
+            }
+            return true;
+        }
+
+        // Show loading modal
+        function showLoading() {
+            const modal = new bootstrap.Modal(document.getElementById('loadingModal'));
+            modal.show();
+        }
+
+        // Hide loading modal
+        function hideLoading() {
+            const modal = bootstrap.Modal.getInstance(document.getElementById('loadingModal'));
+            if (modal) {
+                modal.hide();
+            }
+        }
+
+        // Show success message
+        function showSuccess(message) {
+            // Create and show success alert
+            const alertDiv = document.createElement('div');
+            alertDiv.className = 'alert alert-success alert-dismissible fade show position-fixed';
+            alertDiv.style.top = '20px';
+            alertDiv.style.right = '20px';
+            alertDiv.style.zIndex = '9999';
+            alertDiv.innerHTML = `
+                ${message}
+                <button type="button" class="btn-close" data-bs-dismiss="alert"></button>
+            `;
+            document.body.appendChild(alertDiv);
+            
+            // Auto remove after 5 seconds
+            setTimeout(() => {
+                if (alertDiv.parentNode) {
+                    alertDiv.parentNode.removeChild(alertDiv);
+                }
+            }, 5000);
+        }
+
+        // Show error message
+        function showError(message) {
+            // Create and show error alert
+            const alertDiv = document.createElement('div');
+            alertDiv.className = 'alert alert-danger alert-dismissible fade show position-fixed';
+            alertDiv.style.top = '20px';
+            alertDiv.style.right = '20px';
+            alertDiv.style.zIndex = '9999';
+            alertDiv.innerHTML = `
+                ${message}
+                <button type="button" class="btn-close" data-bs-dismiss="alert"></button>
+            `;
+            document.body.appendChild(alertDiv);
+            
+            // Auto remove after 5 seconds
+            setTimeout(() => {
+                if (alertDiv.parentNode) {
+                    alertDiv.parentNode.removeChild(alertDiv);
+                }
+            }, 5000);
+        }
+
+        // Initialize charts
+        function initializeCharts() {
+            // Email Performance Chart
+            const emailCtx = document.getElementById('emailPerformanceChart').getContext('2d');
+            new Chart(emailCtx, {
+                type: 'doughnut',
+                data: {
+                    labels: ['New Properties', 'AI Recommendations', 'Peak Time'],
+                    datasets: [{
+                        data: [85, 92, 78],
+                        backgroundColor: ['#f093fb', '#4facfe', '#43e97b']
+                    }]
+                },
+                options: {
+                    responsive: true,
+                    maintainAspectRatio: false
+                }
+            });
+
+            // Engagement Chart
+            const engagementCtx = document.getElementById('engagementChart').getContext('2d');
+            new Chart(engagementCtx, {
+                type: 'bar',
+                data: {
+                    labels: ['Morning', 'Afternoon', 'Evening', 'Night'],
+                    datasets: [{
+                        label: 'Engagement Level',
+                        data: [65, 80, 95, 45],
+                        backgroundColor: ['#667eea', '#764ba2', '#f093fb', '#f5576c']
+                    }]
+                },
+                options: {
+                    responsive: true,
+                    maintainAspectRatio: false,
+                    scales: {
+                        y: {
+                            beginAtZero: true,
+                            max: 100
+                        }
+                    }
+                }
+            });
+        }
+
+        // Initialize page
+        document.addEventListener('DOMContentLoaded', function() {
+            initializeCharts();
+        });
+    </script>
+</body>
+</html>