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lead-qualification / api_service_enhanced.py

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	import os
	import json
	import requests
	import time
	from datetime import datetime, timedelta
	from flask import Flask, request, jsonify, render_template
	from flask_cors import CORS
	import logging
	from typing import Dict, List, Optional, Any
	import threading
	import math

	# Import centralized configuration
	from config import get_backend_url, get_ngrok_headers, get_api_endpoint, get_app_config

	# Import the AI recommendation engine
	try:
	from ai_recommendation_engine import AIRecommendationEngine
	ai_engine = AIRecommendationEngine()
	AI_ENGINE_AVAILABLE = True
	except Exception as e:
	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)
	logger.warning(f"AI engine not available: {e}")
	ai_engine = None
	AI_ENGINE_AVAILABLE = False

	# Import mock data service
	try:
	from mock_data_service import mock_data_service
	MOCK_DATA_AVAILABLE = True
	except ImportError:
	MOCK_DATA_AVAILABLE = False

	# Configure logging
	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)

	class CustomJSONEncoder(json.JSONEncoder):
	def default(self, obj):
	if isinstance(obj, float) and math.isinf(obj):
	return 999999999
	if isinstance(obj, float) and math.isnan(obj):
	return 0
	return super().default(obj)

	def formatPrice(price):
	"""Format price in Indian currency"""
	return f"₹{price:,.0f}"

	def formatDuration(seconds):
	"""Format duration in hours and minutes"""
	hours = seconds // 3600
	minutes = (seconds % 3600) // 60
	return f"{hours}h {minutes}m"

	class EnhancedLeadQualificationAPI:
	def __init__(self):
	self.app = Flask(__name__)
	self.app.json_encoder = CustomJSONEncoder
	CORS(self.app)

	# Configuration - Centralized from config.py
	self.config = {
	'backend_url': get_backend_url(),
	'api_timeout': get_app_config()['api_timeout'],
	'cache_duration': int(os.getenv('CACHE_DURATION', get_app_config()['cache_duration'])),
	'max_retries': int(os.getenv('MAX_RETRIES', get_app_config()['max_retries'])),
	'retry_delay': int(os.getenv('RETRY_DELAY', get_app_config()['retry_delay'])),
	'ngrok_headers': get_ngrok_headers()
	}

	# Cache for API responses
	self.cache = {}
	self.cache_timestamps = {}

	# AI processing status tracking
	self.ai_processing_status = {}

	# Setup routes
	self.setup_routes()

	# Add no-cache headers to all responses
	@self.app.after_request
	def add_no_cache_headers(response):
	"""Add headers to prevent any caching"""
	response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
	response.headers['Pragma'] = 'no-cache'
	response.headers['Expires'] = '0'
	return response

	# Start cache cleanup thread
	self.start_cache_cleanup()

	def get_ai_engine(self):
	"""Get the AI recommendation engine instance"""
	try:
	if AI_ENGINE_AVAILABLE and ai_engine:
	return ai_engine
	else:
	logger.warning("⚠️ AI engine not available")
	return None
	except Exception as e:
	logger.error(f"❌ Error getting AI engine: {e}")
	return None

	def setup_routes(self):
	"""Setup all API routes including AI-enhanced endpoints and frontend"""

	# Frontend routes
	@self.app.route('/')
	def index():
	"""Perfect AI Customer Analysis & Email Automation Dashboard"""
	return render_template('perfect_ai_dashboard.html', customer_id=None)

	@self.app.route('/old')
	def old_dashboard():
	"""Old dashboard for reference"""
	return render_template('ai_lead_analysis.html', customer_id=None)

	@self.app.route('/customer/<int:customer_id>')
	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():
	"""Health check endpoint"""
	return jsonify({
	'success': True,
	'service': 'AI Lead Analysis System',
	'status': 'running',
	'timestamp': datetime.now().isoformat()
	})

	@self.app.route('/api/frontend/status')
	def frontend_status():
	"""Get frontend service status"""
	return jsonify({
	'success': True,
	'service': 'AI Lead Analysis System',
	'status': 'running',
	'templates_available': [
	'ai_lead_analysis.html'
	],
	'features': [
	'AI Lead Analysis Dashboard',
	'Email Automation Testing',
	'Behavioral Analysis',
	'Real-time Analytics'
	],
	'timestamp': datetime.now().isoformat()
	})

	# API routes
	@self.app.route('/api/health', methods=['GET'])
	def health_check():
	"""Health check endpoint"""
	return jsonify({
	'status': 'healthy',
	'timestamp': datetime.now().isoformat(),
	'backend_url': self.config['backend_url'],
	'cache_size': len(self.cache),
	'ai_status': 'enabled',
	'ai_models': 'loaded'
	})

	@self.app.route('/api/lead-analysis/<int:customer_id>', methods=['GET'])
	def get_lead_analysis(customer_id):
	"""Get complete lead analysis for a customer (original functionality)"""
	logger.info(f"🔍 Lead analysis request for customer {customer_id}")

	try:
	cache_key = f'lead_analysis_{customer_id}'
	cached_data = self.get_cached_data(cache_key)

	if cached_data:
	logger.info(f"📋 Returning cached data for customer {customer_id}")
	return jsonify(cached_data)

	# Try to fetch data from backend with improved fallback mechanism
	logger.info(f"🌐 Fetching data for customer {customer_id} with fallback support")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')
	logger.info(f"📥 Data received for customer {customer_id}: {len(backend_response) if isinstance(backend_response, list) else 'No data'}")

	if not backend_response:
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': 'No data found for this customer',
	'data': None
	}), 404

	# Process lead data
	logger.info(f"⚙️ Processing lead data for customer {customer_id}")
	processed_data = self.process_lead_data(backend_response, customer_id)

	# Generate analytics
	logger.info(f"🧠 Generating analytics for customer {customer_id}")
	analytics = self.generate_analytics(backend_response, customer_id)

	# Create combined response
	combined_response = {
	'success': True,
	'customer_id': customer_id,
	'timestamp': datetime.now().isoformat(),
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': {
	'engagement_level': analytics.get('engagement_level'),
	'preferred_property_types': analytics.get('preferred_property_types', []),
	'price_preferences': analytics.get('price_preferences', {}),
	'viewing_patterns': analytics.get('viewing_patterns', {}),
	'property_analysis': analytics.get('property_analysis', {}),
	'recommendations': analytics.get('recommendations', []),
	'risk_assessment': analytics.get('risk_assessment', {}),
	'opportunity_score': analytics.get('opportunity_score'),
	'lead_timeline': analytics.get('lead_timeline', []),
	'conversion_probability': analytics.get('conversion_probability', {})
	},
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	},
	'api_info': {
	'backend_url': self.config['backend_url'],
	'endpoint_called': f'/api/PropertyLeadQualification/customer/{customer_id}',
	'response_time': datetime.now().isoformat(),
	'data_points': len(backend_response) if isinstance(backend_response, list) else 0
	}
	}

	# Clean the response for JSON serialization
	cleaned_response = self.clean_for_json(combined_response)
	logger.info(f"✅ Combined response created successfully for customer {customer_id}")

	# Cache the result
	self.cache_data(cache_key, cleaned_response)
	logger.info(f"💾 Combined data cached for customer {customer_id}")

	return jsonify(cleaned_response)

	except Exception as e:
	logger.error(f"❌ Error in lead analysis for customer {customer_id}: {e}")
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': f'Failed to generate lead analysis: {str(e)}',
	'data': None
	}), 500

	@self.app.route('/api/ai-analysis/<int:customer_id>', methods=['GET'])
	def get_ai_analysis(customer_id):
	"""Get AI-enhanced lead analysis for a customer"""
	logger.info(f"🤖 AI analysis request for customer {customer_id}")

	try:
	# First get the regular analysis
	cache_key = f'lead_analysis_{customer_id}'
	analysis_data = self.get_cached_data(cache_key)

	if not analysis_data:
	# Generate regular analysis first with improved fallback
	logger.info(f"🌐 Fetching data for AI analysis - customer {customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')

	if not backend_response:
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': 'No data found for this customer'
	}), 404

	processed_data = self.process_lead_data(backend_response, customer_id)
	analytics = self.generate_analytics(backend_response, customer_id)

	analysis_data = {
	'success': True,
	'customer_id': customer_id,
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': analytics,
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Now enhance with AI analysis
	if AI_ENGINE_AVAILABLE and ai_engine:
	logger.info(f"🧠 Generating AI insights for customer {customer_id}")
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)
	# Find AI-recommended properties
	logger.info(f"🔍 Finding AI recommendations for customer {customer_id}")
	ai_recommendations = ai_engine.find_similar_properties_ai(analysis_data, ai_insights)
	else:
	logger.warning("⚠️ AI engine not available, using mock AI insights")
	ai_insights = {
	'personality_type': 'Analytical',
	'decision_making_style': 'Data-driven',
	'preferred_communication': 'Email',
	'urgency_level': 'Medium',
	'budget_confidence': 'High',
	'location_preference': 'Premium areas',
	'property_type_preference': 'Luxury properties'
	}
	ai_recommendations = []

	# Combine everything
	enhanced_response = analysis_data.copy()
	enhanced_response['data']['ai_insights'] = ai_insights
	enhanced_response['data']['ai_recommendations'] = ai_recommendations
	enhanced_response['ai_enhanced'] = True
	enhanced_response['ai_timestamp'] = datetime.now().isoformat()

	# Cache AI-enhanced result
	ai_cache_key = f'ai_analysis_{customer_id}'
	self.cache_data(ai_cache_key, enhanced_response)

	logger.info(f"✅ AI analysis completed for customer {customer_id}")
	return jsonify(enhanced_response)

	except Exception as e:
	logger.error(f"❌ Error in AI analysis for customer {customer_id}: {e}")
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': f'Failed to generate AI analysis: {str(e)}',
	'ai_error': True
	}), 500

	@self.app.route('/api/ai-recommendations/<int:customer_id>', methods=['POST'])
	def send_ai_recommendations(customer_id):
	"""Send AI-powered recommendations via email"""
	logger.info(f"📧 AI recommendation email request for customer {customer_id}")

	try:
	# Get email from request
	data = request.get_json()
	recipient_email = data.get('email')

	if not recipient_email:
	return jsonify({
	'success': False,
	'error': 'Email address is required'
	}), 400

	# Mark as processing
	self.ai_processing_status[customer_id] = {
	'status': 'processing',
	'start_time': datetime.now().isoformat(),
	'email': recipient_email
	}

	# Get or generate analysis data
	cache_key = f'lead_analysis_{customer_id}'
	analysis_data = self.get_cached_data(cache_key)

	if not analysis_data:
	# Generate analysis if not cached with improved fallback
	logger.info(f"🌐 Fetching data for recommendations - customer {customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')

	if not backend_response:
	self.ai_processing_status[customer_id]['status'] = 'failed'
	self.ai_processing_status[customer_id]['error'] = 'No data found'
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': 'No data found for this customer'
	}), 404

	processed_data = self.process_lead_data(backend_response, customer_id)
	analytics = self.generate_analytics(backend_response, customer_id)

	analysis_data = {
	'success': True,
	'customer_id': customer_id,
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': analytics,
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Process with AI in background
	def process_ai_recommendations():
	try:
	if AI_ENGINE_AVAILABLE and ai_engine:
	result = ai_engine.process_customer_with_ai(customer_id, recipient_email, analysis_data)
	else:
	# Mock AI processing result
	result = {
	'success': True,
	'customer_id': customer_id,
	'email_sent': True,
	'recipient': recipient_email,
	'subject': f"🤖 AI-Powered Property Recommendations for Customer {customer_id}",
	'ai_insights': {
	'personality_type': 'Analytical',
	'decision_making_style': 'Data-driven',
	'preferred_communication': 'Email',
	'urgency_level': 'Medium',
	'budget_confidence': 'High'
	},
	'recommendations_count': 5,
	'mock_data': True,
	'message': 'Mock AI processing completed (AI engine not available)'
	}
	self.ai_processing_status[customer_id] = {
	'status': 'completed',
	'result': result,
	'completion_time': datetime.now().isoformat()
	}
	logger.info(f"✅ AI processing completed for customer {customer_id}")
	except Exception as e:
	self.ai_processing_status[customer_id] = {
	'status': 'failed',
	'error': str(e),
	'completion_time': datetime.now().isoformat()
	}
	logger.error(f"❌ AI processing failed for customer {customer_id}: {e}")

	# Start background processing
	thread = threading.Thread(target=process_ai_recommendations)
	thread.daemon = True
	thread.start()

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'message': 'AI recommendation processing started',
	'status': 'processing',
	'email': recipient_email,
	'estimated_completion': '2-3 minutes'
	})

	except Exception as e:
	logger.error(f"❌ Error starting AI recommendations for customer {customer_id}: {e}")
	return jsonify({
	'success': False,
	'customer_id': customer_id,
	'error': f'Failed to start AI processing: {str(e)}'
	}), 500

	@self.app.route('/api/ai-status/<int:customer_id>', methods=['GET'])
	def get_ai_status(customer_id):
	"""Get AI processing status for a customer"""
	status = self.ai_processing_status.get(customer_id, {
	'status': 'not_started',
	'message': 'No AI processing initiated for this customer'
	})

	response_data = {
	'customer_id': customer_id,
	'ai_processing': status,
	'timestamp': datetime.now().isoformat()
	}

	# Clean for JSON serialization
	cleaned_response = self.clean_for_json(response_data)
	return jsonify(cleaned_response)

	@self.app.route('/api/batch-ai-analysis', methods=['POST'])
	def batch_ai_analysis():
	"""Get AI analysis for multiple customers"""
	try:
	data = request.get_json()
	customer_ids = data.get('customer_ids', [])

	if not customer_ids:
	return jsonify({
	'success': False,
	'error': 'No customer IDs provided'
	}), 400

	results = {}
	for customer_id in customer_ids:
	try:
	# Get regular analysis with improved fallback
	logger.info(f"🌐 Fetching data for batch analysis - customer {customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')

	if not backend_response:
	results[customer_id] = {
	'success': False,
	'error': 'No data found for this customer'
	}
	continue

	processed_data = self.process_lead_data(backend_response, customer_id)
	analytics = self.generate_analytics(backend_response, customer_id)

	analysis_data = {
	'success': True,
	'customer_id': customer_id,
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': analytics,
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Add AI insights
	if AI_ENGINE_AVAILABLE and ai_engine:
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)
	ai_recommendations = ai_engine.find_similar_properties_ai(analysis_data, ai_insights)
	else:
	ai_insights = {
	'personality_type': 'Analytical',
	'decision_making_style': 'Data-driven',
	'preferred_communication': 'Email',
	'urgency_level': 'Medium',
	'budget_confidence': 'High',
	'location_preference': 'Premium areas',
	'property_type_preference': 'Luxury properties'
	}
	ai_recommendations = []

	analysis_data['data']['ai_insights'] = ai_insights
	analysis_data['data']['ai_recommendations'] = ai_recommendations
	analysis_data['ai_enhanced'] = True

	cleaned_response = self.clean_for_json(analysis_data)
	results[customer_id] = cleaned_response

	except Exception as e:
	results[customer_id] = {
	'success': False,
	'error': str(e)
	}

	return jsonify({
	'success': True,
	'results': results,
	'total_processed': len(customer_ids),
	'ai_enhanced': True
	})

	except Exception as e:
	logger.error(f"❌ Error in batch AI analysis: {e}")
	return jsonify({
	'success': False,
	'error': f'Batch AI analysis failed: {str(e)}'
	}), 500

	@self.app.route('/api/clear-cache', methods=['POST'])
	def clear_cache():
	"""Clear all cached data including AI processing status"""
	try:
	self.cache.clear()
	self.cache_timestamps.clear()
	self.ai_processing_status.clear()
	logger.info("🗑️ Cache and AI status cleared")
	return jsonify({
	'success': True,
	'message': 'Cache and AI processing status cleared successfully'
	})
	except Exception as e:
	logger.error(f"❌ Error clearing cache: {e}")
	return jsonify({
	'success': False,
	'error': f'Failed to clear cache: {str(e)}'
	}), 500

	@self.app.route('/api/test-email', methods=['POST'])
	def test_email():
	"""Test SendGrid email functionality"""
	logger.info("🧪 Testing SendGrid email functionality")

	try:
	# Debug request information
	logger.info(f"Request Content-Type: {request.content_type}")
	logger.info(f"Request Headers: {dict(request.headers)}")
	logger.info(f"Request Method: {request.method}")
	logger.info(f"Request Data: {request.get_data()}")

	# Handle both JSON and form data with better error handling
	data = {}
	if request.is_json:
	try:
	data = request.get_json() or {}
	logger.info(f"Parsed JSON data: {data}")
	except Exception as json_error:
	logger.error(f"JSON parsing error: {json_error}")
	return jsonify({
	'success': False,
	'error': f'Invalid JSON: {str(json_error)}'
	}), 400
	else:
	try:
	data = request.form.to_dict() or {}
	logger.info(f"Parsed form data: {data}")
	except Exception as form_error:
	logger.error(f"Form parsing error: {form_error}")
	return jsonify({
	'success': False,
	'error': f'Invalid form data: {str(form_error)}'
	}), 400

	recipient_email = data.get('email', '[email protected]')
	logger.info(f"📧 Testing email to: {recipient_email}")

	# Validate email format
	if not recipient_email or '@' not in recipient_email:
	return jsonify({
	'success': False,
	'error': 'Invalid email address'
	}), 400

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Test SendGrid email
	test_result = ai_engine.test_sendgrid_email(recipient_email)

	return jsonify(test_result)

	except Exception as e:
	logger.error(f"Error testing email: {e}")
	import traceback
	logger.error(f"Full traceback: {traceback.format_exc()}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/test-email-simple', methods=['GET'])
	def test_email_simple():
	"""Simple test SendGrid email functionality (GET method)"""
	logger.info("🧪 Testing SendGrid email functionality (simple GET)")

	try:
	# Use default email for testing
	recipient_email = '[email protected]'
	logger.info(f"📧 Testing email to: {recipient_email}")

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Test SendGrid email
	test_result = ai_engine.test_sendgrid_email(recipient_email)

	return jsonify(test_result)

	except Exception as e:
	logger.error(f"Error testing email (simple): {e}")
	import traceback
	logger.error(f"Full traceback: {traceback.format_exc()}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/automated-email', methods=['POST'])
	def send_automated_email():
	"""Send automated email"""
	try:
	data = request.json
	customer_id = data.get('customer_id')
	email_type = data.get('email_type', 'behavioral_trigger')
	recipient_email = data.get('recipient_email', '[email protected]')

	if not customer_id:
	return jsonify({
	'success': False,
	'error': 'Customer ID is required'
	}), 400

	# Generate mock email response (email service integrated)
	if MOCK_DATA_AVAILABLE:
	customer_data = mock_data_service.get_customer_data(customer_id)
	customer_profile = mock_data_service.get_customer_profile(customer_id)

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'recipient_email': recipient_email,
	'subject': f"🤖 AI-Powered Recommendations for {customer_profile.get('customerName', f'Customer {customer_id}')}",
	'email_type': email_type,
	'timestamp': datetime.now().isoformat(),
	'mock_data': True,
	'message': 'Mock email generated successfully (integrated email service)'
	})
	else:
	return jsonify({
	'success': False,
	'error': 'Email service not available and no mock data'
	}), 500

	except Exception as e:
	logger.error(f"Error sending automated email: {e}")
	return jsonify({
	'success': False,
	'error': f'Email service not available: {str(e)}'
	}), 500

	@self.app.route('/api/email-status', methods=['GET'])
	def get_email_status():
	"""Get email automation status"""
	return jsonify({
	'success': True,
	'service_status': 'integrated',
	'email_config': {
	'sender': '[email protected]',
	'recipient': '[email protected]',
	'smtp_server': 'Integrated Service'
	},
	'ai_engine_loaded': True,
	'timestamp': datetime.now().isoformat(),
	'message': 'Email service integrated into main API service'
	})

	@self.app.route('/api/email-preview/<int:customer_id>', methods=['GET'])
	def email_preview(customer_id):
	"""Generate email preview for a customer"""
	logger.info(f"📧 Generating email preview for customer {customer_id}")

	try:
	# Get customer data
	customer_data = self.make_api_request(f'/api/lead-analysis/{customer_id}')

	if not customer_data or 'data' not in customer_data:
	return jsonify({
	'success': False,
	'error': 'Customer data not found'
	}), 404

	# Process with AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate email content
	recipient_email = request.args.get('email', '[email protected]')
	email_result = ai_engine.process_customer_with_ai(
	customer_id, recipient_email, customer_data
	)

	if not email_result.get('success'):
	return jsonify({
	'success': False,
	'error': 'Failed to generate email content'
	}), 500

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'email_preview': email_result,
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"Error generating email preview: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/email-preview/<int:customer_id>')
	def email_preview_page(customer_id):
	"""Email preview page"""
	try:
	# Get email content
	email_response = self.get_email_preview(customer_id)
	email_data = email_response.json

	if not email_data.get('success'):
	return f"Error: {email_data.get('error', 'Unknown error')}"

	email_content = email_data['email_content']

	# Create HTML page with email content
	html_content = f"""
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Email Preview - Customer {customer_id}</title>
	<style>
	body {{
	font-family: Arial, sans-serif;
	margin: 20px;
	background-color: #f5f5f5;
	}}
	.container {{
	max-width: 800px;
	margin: 0 auto;
	background: white;
	padding: 20px;
	border-radius: 8px;
	box-shadow: 0 2px 10px rgba(0,0,0,0.1);
	}}
	.header {{
	background: #2c3e50;
	color: white;
	padding: 20px;
	border-radius: 8px;
	margin-bottom: 20px;
	}}
	.email-info {{
	background: #ecf0f1;
	padding: 15px;
	border-radius: 5px;
	margin-bottom: 20px;
	}}
	.email-content {{
	border: 1px solid #ddd;
	border-radius: 5px;
	overflow: hidden;
	}}
	.email-header {{
	background: #34495e;
	color: white;
	padding: 10px 15px;
	font-weight: bold;
	}}
	.email-body {{
	padding: 0;
	}}
	.back-button {{
	background: #3498db;
	color: white;
	padding: 10px 20px;
	text-decoration: none;
	border-radius: 5px;
	display: inline-block;
	margin-bottom: 20px;
	}}
	.back-button:hover {{
	background: #2980b9;
	}}
	</style>
	</head>
	<body>
	<div class="container">
	<div class="header">
	<h1>📧 Email Preview</h1>
	<p>Customer ID: {customer_id}</p>
	</div>

	<a href="/customer/{customer_id}" class="back-button">← Back to Customer Analysis</a>

	<div class="email-info">
	<h3>Email Details:</h3>
	<p><strong>To:</strong> {email_content['recipient_email']}</p>
	<p><strong>Subject:</strong> {email_content['subject']}</p>
	<p><strong>Generated:</strong> {email_content['timestamp']}</p>
	<p><strong>Recommendations:</strong> {email_data['recommendations_count']} properties</p>
	</div>

	<div class="email-content">
	<div class="email-header">
	Email Content Preview
	</div>
	<div class="email-body">
	{email_content['html_content']}
	</div>
	</div>
	</div>
	</body>
	</html>
	"""

	return html_content

	except Exception as e:
	logger.error(f"Error creating email preview page: {e}")
	return f"Error: {str(e)}"

	@self.app.route('/api/download-email/<filename>')
	def download_email(filename):
	"""Download saved email file"""
	try:
	import os
	emails_dir = "/tmp/emails"
	file_path = os.path.join(emails_dir, filename)

	if not os.path.exists(file_path):
	return jsonify({
	'success': False,
	'error': 'Email file not found'
	}), 404

	from flask import send_file
	return send_file(file_path, as_attachment=True)

	except Exception as e:
	logger.error(f"Error downloading email file: {e}")
	return jsonify({
	'success': False,
	'error': f'Failed to download email: {str(e)}'
	}), 500

	@self.app.route('/api/list-emails')
	def list_emails():
	"""List all saved email files"""
	try:
	import os
	emails_dir = "/tmp/emails"

	if not os.path.exists(emails_dir):
	return jsonify({
	'success': True,
	'emails': []
	})

	emails = []
	for filename in os.listdir(emails_dir):
	if filename.endswith('.html'):
	file_path = os.path.join(emails_dir, filename)
	file_stat = os.stat(file_path)
	emails.append({
	'filename': filename,
	'size': file_stat.st_size,
	'created': datetime.fromtimestamp(file_stat.st_ctime).isoformat(),
	'download_url': f'/api/download-email/{filename}'
	})

	# Sort by creation time (newest first)
	emails.sort(key=lambda x: x['created'], reverse=True)

	return jsonify({
	'success': True,
	'emails': emails
	})

	except Exception as e:
	logger.error(f"Error listing email files: {e}")
	return jsonify({
	'success': False,
	'error': f'Failed to list emails: {str(e)}'
	}), 500

	@self.app.route('/saved-emails')
	def saved_emails_page():
	"""Page to view and download saved emails"""
	try:
	import os
	emails_dir = "/tmp/emails"

	if not os.path.exists(emails_dir):
	emails = []
	else:
	emails = []
	for filename in os.listdir(emails_dir):
	if filename.endswith('.html'):
	file_path = os.path.join(emails_dir, filename)
	file_stat = os.stat(file_path)
	emails.append({
	'filename': filename,
	'size': file_stat.st_size,
	'created': datetime.fromtimestamp(file_stat.st_ctime).strftime('%Y-%m-%d %H:%M:%S'),
	'download_url': f'/api/download-email/{filename}'
	})

	# Sort by creation time (newest first)
	emails.sort(key=lambda x: x['created'], reverse=True)

	# Generate email cards HTML
	email_cards_html = ""
	if emails:
	for email in emails:
	email_cards_html += f"""
	<div class="email-card">
	<div class="row align-items-center">
	<div class="col-md-8">
	<h5><i class="fas fa-file-alt"></i> {email.get('filename', 'Unknown')}</h5>
	<p class="text-muted mb-1">
	<i class="fas fa-clock"></i> Created: {email.get('created', 'Unknown')}
	</p>
	<p class="text-muted mb-0">
	<i class="fas fa-file"></i> Size: {email.get('size', 0):,} bytes
	</p>
	</div>
	<div class="col-md-4 text-end">
	<a href="{email.get('download_url', '#')}" class="download-btn">
	<i class="fas fa-download"></i> Download HTML
	</a>
	</div>
	</div>
	</div>
	"""

	# Generate content section
	if emails:
	content_section = f"""
	<div class="row">
	<div class="col-12">
	<h3>📧 Generated Emails ({len(emails)} total)</h3>
	{email_cards_html}
	</div>
	</div>
	"""
	else:
	content_section = """
	<div class="row">
	<div class="col-12">
	<div class="text-center py-5">
	<i class="fas fa-inbox fa-3x text-muted mb-3"></i>
	<h3>No emails generated yet</h3>
	<p class="text-muted">Generate some AI recommendations to see saved emails here.</p>
	</div>
	</div>
	</div>
	"""

	html_content = f"""
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Saved Emails</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>
	body {{
	background-color: #f8f9fa;
	padding-top: 2rem;
	}}
	.header {{
	background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
	color: white;
	padding: 2rem 0;
	margin-bottom: 2rem;
	}}
	.email-card {{
	background: white;
	border-radius: 10px;
	box-shadow: 0 2px 10px rgba(0,0,0,0.1);
	margin-bottom: 1rem;
	padding: 1.5rem;
	}}
	.download-btn {{
	background: #28a745;
	color: white;
	border: none;
	padding: 0.5rem 1rem;
	border-radius: 5px;
	text-decoration: none;
	display: inline-block;
	}}
	.download-btn:hover {{
	background: #218838;
	color: white;
	text-decoration: none;
	}}
	</style>
	</head>
	<body>
	<div class="header">
	<div class="container">
	<h1><i class="fas fa-envelope"></i> Saved Emails</h1>
	<p class="mb-0">Download and view generated email content</p>
	</div>
	</div>

	<div class="container">
	<div class="row mb-4">
	<div class="col-md-6">
	<a href="/" class="btn btn-outline-primary">
	<i class="fas fa-arrow-left"></i> Back to Home
	</a>
	</div>
	<div class="col-md-6 text-end">
	<button class="btn btn-outline-secondary" onclick="location.reload()">
	<i class="fas fa-sync"></i> Refresh
	</button>
	</div>
	</div>

	{content_section}
	</div>
	</body>
	</html>
	"""

	return html_content

	except Exception as e:
	logger.error(f"Error creating saved emails page: {e}")
	return f"Error: {str(e)}"

	@self.app.route('/api/properties/parallel', methods=['GET'])
	def get_properties_parallel():
	"""Get properties using parallel processing for better performance"""
	logger.info("🚀 Fetching properties using parallel processing...")

	try:
	from concurrent.futures import ThreadPoolExecutor, as_completed
	import requests

	# Configuration
	max_workers = int(request.args.get('workers', 8))
	page_size = int(request.args.get('page_size', 100))
	max_pages = int(request.args.get('max_pages', 50))

	logger.info(f"🔧 Using {max_workers} workers, {page_size} properties per page, max {max_pages} pages")

	def fetch_properties_page(page_num):
	"""Fetch a single page of properties"""
	try:
	url = f"{self.config['backend_url']}/api/Property/allPropertieswithfulldetails"
	params = {
	'pageNumber': page_num,
	'pageSize': page_size
	}

	response = requests.get(url, params=params, timeout=None, verify=False)
	response.raise_for_status()

	data = response.json()

	# Handle different response structures
	if isinstance(data, list):
	properties = data
	elif isinstance(data, dict):
	properties = data.get('data', data.get('properties', data.get('results', [])))
	else:
	properties = []

	logger.info(f"✅ Page {page_num}: {len(properties)} properties")
	return properties

	except Exception as e:
	logger.error(f"❌ Error fetching page {page_num}: {e}")
	return []

	# Use ThreadPoolExecutor for parallel fetching
	all_properties = []
	completed_pages = 0
	empty_pages = 0

	with ThreadPoolExecutor(max_workers=max_workers) as executor:
	# Submit tasks for all pages
	future_to_page = {
	executor.submit(fetch_properties_page, page_num): page_num
	for page_num in range(1, max_pages + 1)
	}

	for future in as_completed(future_to_page):
	page_num = future_to_page[future]
	try:
	properties = future.result() # No timeout - let it take as long as needed
	if properties:
	all_properties.extend(properties)
	completed_pages += 1
	logger.info(f"✅ Parallel fetch - Page {page_num}: {len(properties)} properties (Total: {len(all_properties)})")
	else:
	empty_pages += 1
	logger.info(f"📄 Page {page_num} is empty (Empty pages: {empty_pages})")

	# Stop if we've hit too many empty pages in a row
	if empty_pages >= 3:
	logger.info("🛑 Stopping fetch - too many consecutive empty pages")
	break

	except Exception as e:
	logger.error(f"❌ Failed to fetch page {page_num}: {e}")
	# Continue with other pages

	logger.info(f"🎉 Parallel fetch completed: {len(all_properties)} total properties from {completed_pages} pages!")

	return jsonify({
	'success': True,
	'properties': all_properties,
	'total': len(all_properties),
	'pages_completed': completed_pages,
	'empty_pages': empty_pages,
	'workers_used': max_workers,
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"Error in parallel property fetch: {e}")
	return jsonify({
	'success': False,
	'error': str(e),
	'properties': []
	}), 500

	@self.app.route('/api/automated-email-analysis/<int:customer_id>', methods=['GET'])
	def analyze_automated_emails(customer_id):
	"""Analyze what automated emails would be sent for a customer"""
	logger.info(f"🤖 Analyzing automated email triggers for customer {customer_id}")

	try:
	# Get customer analysis using our AI model (not from backend)
	logger.info(f"🔍 Generating lead analysis for customer {customer_id} using AI model")

	# Try to fetch data from backend first for property data
	backend_response = None
	try:
	logger.info(f"🌐 Making backend API request to: {self.config['backend_url']}/api/PropertyLeadQualification/customer/{customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')
	logger.info(f"📥 Backend response received for customer {customer_id}: {len(backend_response) if isinstance(backend_response, list) else 'No data'}")
	except Exception as e:
	logger.warning(f"⚠️ Backend API failed: {e}")

	# If backend fails, use mock data
	if not backend_response and MOCK_DATA_AVAILABLE:
	logger.info(f"🔄 Using mock data for customer {customer_id}")
	backend_response = mock_data_service.get_customer_data(customer_id)
	logger.info(f"📥 Mock data generated for customer {customer_id}: {len(backend_response)} properties")

	if not backend_response:
	return jsonify({
	'success': False,
	'error': 'No property data found for this customer'
	}), 404

	# Process lead data using our AI model
	logger.info(f"⚙️ Processing lead data for customer {customer_id}")
	processed_data = self.process_lead_data(backend_response, customer_id)

	# Generate analytics using our AI model
	logger.info(f"🧠 Generating analytics for customer {customer_id}")
	analytics = self.generate_analytics(backend_response, customer_id)

	# Create customer analysis data structure
	customer_data = {
	'success': True,
	'customer_id': customer_id,
	'timestamp': datetime.now().isoformat(),
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': {
	'engagement_level': analytics.get('engagement_level'),
	'preferred_property_types': analytics.get('preferred_property_types', []),
	'price_preferences': analytics.get('price_preferences', {}),
	'viewing_patterns': analytics.get('viewing_patterns', {}),
	'property_analysis': analytics.get('property_analysis', {}),
	'recommendations': analytics.get('recommendations', []),
	'risk_assessment': analytics.get('risk_assessment', {}),
	'opportunity_score': analytics.get('opportunity_score'),
	'lead_timeline': analytics.get('lead_timeline', []),
	'conversion_probability': analytics.get('conversion_probability', {})
	},
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Analyze automated email triggers using our AI model
	trigger_analysis = ai_engine.analyze_automated_email_triggers(customer_data)

	if not trigger_analysis.get('success'):
	return jsonify({
	'success': False,
	'error': 'Failed to analyze email triggers'
	}), 500

	return jsonify(trigger_analysis)

	except Exception as e:
	logger.error(f"Error analyzing automated emails: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/test-automated-emails/<int:customer_id>', methods=['POST'])
	def test_automated_emails(customer_id):
	"""Test sending all automated emails for a customer"""
	logger.info(f"🧪 Testing automated emails for customer {customer_id}")

	try:
	data = request.get_json() or {}
	recipient_email = data.get('email', '[email protected]')

	# Get customer analysis using our AI model (not from backend)
	logger.info(f"🔍 Generating lead analysis for customer {customer_id} using AI model")

	# Try to fetch data from backend first for property data
	backend_response = None
	try:
	logger.info(f"🌐 Making backend API request to: {self.config['backend_url']}/api/PropertyLeadQualification/customer/{customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')
	logger.info(f"📥 Backend response received for customer {customer_id}: {len(backend_response) if isinstance(backend_response, list) else 'No data'}")
	except Exception as e:
	logger.warning(f"⚠️ Backend API failed: {e}")

	# If backend fails, use mock data
	if not backend_response and MOCK_DATA_AVAILABLE:
	logger.info(f"🔄 Using mock data for customer {customer_id}")
	backend_response = mock_data_service.get_customer_data(customer_id)
	logger.info(f"📥 Mock data generated for customer {customer_id}: {len(backend_response)} properties")

	if not backend_response:
	return jsonify({
	'success': False,
	'error': 'No property data found for this customer'
	}), 404

	# Process lead data using our AI model
	logger.info(f"⚙️ Processing lead data for customer {customer_id}")
	processed_data = self.process_lead_data(backend_response, customer_id)

	# Generate analytics using our AI model
	logger.info(f"🧠 Generating analytics for customer {customer_id}")
	analytics = self.generate_analytics(backend_response, customer_id)

	# Create customer analysis data structure
	customer_data = {
	'success': True,
	'customer_id': customer_id,
	'timestamp': datetime.now().isoformat(),
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': {
	'engagement_level': analytics.get('engagement_level'),
	'preferred_property_types': analytics.get('preferred_property_types', []),
	'price_preferences': analytics.get('price_preferences', {}),
	'viewing_patterns': analytics.get('viewing_patterns', {}),
	'property_analysis': analytics.get('property_analysis', {}),
	'recommendations': analytics.get('recommendations', []),
	'risk_assessment': analytics.get('risk_assessment', {}),
	'opportunity_score': analytics.get('opportunity_score'),
	'lead_timeline': analytics.get('lead_timeline', []),
	'conversion_probability': analytics.get('conversion_probability', {})
	},
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Analyze triggers using our AI model
	logger.info(f"🤖 Analyzing automated email triggers for customer {customer_id}")
	trigger_analysis = ai_engine.analyze_automated_email_triggers(customer_data)

	if not trigger_analysis.get('success'):
	return jsonify({
	'success': False,
	'error': 'Failed to analyze email triggers'
	}), 500

	# Send emails for each trigger
	sent_emails = []
	failed_emails = []

	for trigger in trigger_analysis.get('triggers', []):
	try:
	# Generate email content using our AI model
	email_content = ai_engine.generate_automated_email_content(
	trigger, customer_data, trigger_analysis.get('ai_insights', {})
	)

	if email_content.get('success'):
	# Send email
	success = ai_engine.send_email(recipient_email, email_content)

	if success:
	sent_emails.append({
	'trigger_type': trigger['trigger_type'],
	'subject': email_content['subject'],
	'priority': trigger['priority'],
	'reason': trigger['reason']
	})
	else:
	failed_emails.append({
	'trigger_type': trigger['trigger_type'],
	'error': 'Failed to send email'
	})
	else:
	failed_emails.append({
	'trigger_type': trigger['trigger_type'],
	'error': email_content.get('error', 'Failed to generate content')
	})

	except Exception as e:
	failed_emails.append({
	'trigger_type': trigger['trigger_type'],
	'error': str(e)
	})

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'recipient_email': recipient_email,
	'total_triggers': len(trigger_analysis.get('triggers', [])),
	'sent_emails': sent_emails,
	'failed_emails': failed_emails,
	'analysis_summary': trigger_analysis.get('analysis_summary', {}),
	'ai_insights': trigger_analysis.get('ai_insights', {}),
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"Error testing automated emails: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/fetch-all-properties', methods=['GET'])
	def fetch_all_properties():
	"""Fetch all 600+ properties and store in ChromaDB"""
	try:
	logger.info("🚀 Starting fetch of ALL 600+ properties...")

	# Get parameters with defaults optimized for 600+ properties
	workers = request.args.get('workers', 10, type=int)
	page_size = request.args.get('page_size', 100, type=int) # Increased page size
	max_pages = request.args.get('max_pages', 50, type=int) # Increased max pages

	logger.info(f"📊 Fetch parameters: {workers} workers, {page_size} per page, max {max_pages} pages")

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Fetch all properties using parallel processing
	success = ai_engine.fetch_all_properties_parallel(
	max_workers=workers,
	page_size=page_size,
	max_pages=max_pages
	)

	if success:
	# Get final count from ChromaDB
	total_stored = 0
	if hasattr(ai_engine, 'properties_collection') and ai_engine.properties_collection:
	try:
	total_stored = ai_engine.properties_collection.count()
	except:
	total_stored = "Unknown"

	return jsonify({
	'success': True,
	'message': f'Successfully fetched and stored properties',
	'total_properties': total_stored,
	'parameters_used': {
	'workers': workers,
	'page_size': page_size,
	'max_pages': max_pages
	},
	'timestamp': datetime.now().isoformat()
	})
	else:
	return jsonify({
	'success': False,
	'error': 'Failed to fetch properties'
	}), 500

	except Exception as e:
	logger.error(f"❌ Error fetching all properties: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/multi-ai-recommendations/<int:customer_id>', methods=['POST'])
	def send_multi_ai_recommendations(customer_id):
	"""Send multiple AI-powered recommendation emails (property-based, price-based, location-based)"""
	logger.info(f"🤖 Multi-AI recommendation request for customer {customer_id}")

	try:
	data = request.get_json() or {}
	recipient_email = data.get('email', '[email protected]')
	email_count = data.get('email_count', 10) # Send 10 emails by default

	# Get customer analysis data
	analysis_data = self._get_analysis_data_parallel(customer_id)
	if not analysis_data:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	# Create different types of recommendations
	recommendation_types = [
	'property_based',
	'price_based',
	'location_based',
	'similarity_based',
	'behavioral_based',
	'premium_properties',
	'budget_friendly',
	'trending_properties',
	'family_oriented',
	'investment_opportunities'
	]

	sent_emails = []
	failed_emails = []

	# Send multiple recommendation emails
	for i in range(min(email_count, len(recommendation_types))):
	rec_type = recommendation_types[i]

	try:
	logger.info(f"📧 Generating {rec_type} recommendations for customer {customer_id}")

	# Get specialized recommendations based on type
	recommendations = ai_engine.get_enhanced_ai_recommendations(
	analysis_data, ai_insights, count=5
	)

	# Generate personalized email
	email_result = ai_engine.generate_personalized_email(
	analysis_data, ai_insights, recommendations, recipient_email
	)

	if email_result.get('success'):
	# Customize subject based on recommendation type
	custom_subject = self._get_custom_subject(rec_type, customer_id, ai_insights)
	email_result['subject'] = custom_subject

	# Send email
	success = ai_engine.send_email(recipient_email, email_result)

	if success:
	sent_emails.append({
	'type': rec_type,
	'subject': custom_subject,
	'recommendations_count': len(recommendations),
	'timestamp': datetime.now().isoformat()
	})
	logger.info(f"✅ {rec_type} email sent successfully")
	else:
	failed_emails.append({
	'type': rec_type,
	'error': 'Failed to send email'
	})
	else:
	failed_emails.append({
	'type': rec_type,
	'error': email_result.get('error', 'Failed to generate email')
	})

	except Exception as e:
	failed_emails.append({
	'type': rec_type,
	'error': str(e)
	})
	logger.error(f"❌ Error with {rec_type}: {e}")

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'recipient_email': recipient_email,
	'requested_emails': email_count,
	'sent_emails': sent_emails,
	'failed_emails': failed_emails,
	'total_sent': len(sent_emails),
	'total_failed': len(failed_emails),
	'ai_insights': ai_insights,
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error in multi-AI recommendations: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/chromadb-recommendations/<int:customer_id>', methods=['GET'])
	def get_chromadb_recommendations(customer_id):
	"""Get property recommendations using ChromaDB similarity search"""
	logger.info(f"🔍 ChromaDB recommendations for customer {customer_id}")

	try:
	# Get customer analysis data
	analysis_data = self._get_analysis_data_parallel(customer_id)
	if not analysis_data:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	# Get different types of recommendations from ChromaDB
	recommendations = {
	'similarity_based': ai_engine.get_similarity_based_recommendations(
	analysis_data['data']['properties'],
	ai_engine.extract_customer_preferences(analysis_data, ai_insights),
	count=5
	),
	'preference_based': ai_engine.get_preference_based_recommendations(
	ai_engine.extract_customer_preferences(analysis_data, ai_insights),
	analysis_data['data']['properties'],
	count=5
	),
	'behavioral_based': ai_engine.get_behavioral_recommendations(
	analysis_data, ai_insights, count=5
	)
	}

	# Add AI explanations
	for rec_type, recs in recommendations.items():
	recommendations[rec_type] = ai_engine.add_ai_explanations(
	recs,
	ai_engine.extract_customer_preferences(analysis_data, ai_insights),
	ai_insights
	)

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'recommendations': recommendations,
	'ai_insights': ai_insights,
	'total_recommendations': sum(len(recs) for recs in recommendations.values()),
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error getting ChromaDB recommendations: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/test-sendgrid-connection', methods=['GET'])
	def test_sendgrid_connection():
	"""Test if SendGrid connection is working"""
	logger.info("🧪 Testing SendGrid connection")

	try:
	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Test SendGrid
	test_result = ai_engine.test_sendgrid_email('[email protected]')

	return jsonify({
	'success': test_result.get('success', False),
	'message': 'SendGrid connection test completed',
	'details': test_result,
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error testing SendGrid: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/email-analysis-basis/<int:customer_id>', methods=['GET'])
	def get_email_analysis_basis(customer_id):
	"""Get analysis basis for email recommendations"""
	logger.info(f"📊 Getting email analysis basis for customer {customer_id}")

	try:
	# Get customer analysis data
	analysis_data = self._get_analysis_data_parallel(customer_id)
	if not analysis_data:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	# Extract customer preferences
	preferences = ai_engine.extract_customer_preferences(analysis_data, ai_insights)

	# Analyze behavioral patterns
	behavioral_patterns = ai_engine.analyze_behavioral_patterns(analysis_data, ai_insights)

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'analysis_basis': {
	'customer_preferences': preferences,
	'ai_insights': ai_insights,
	'behavioral_patterns': behavioral_patterns,
	'viewed_properties': analysis_data['data']['properties'][:5], # Sample properties
	'lead_qualification': analysis_data['data']['lead_qualification'],
	'engagement_level': analysis_data['data']['analytics']['engagement_level'],
	'conversion_probability': analysis_data['data']['analytics']['conversion_probability']['final_probability']
	},
	'email_triggers': {
	'property_based': 'Based on property types you viewed most',
	'price_based': f"Based on your price range ₹{preferences.get('price_range', {}).get('min_price', 0)} - ₹{preferences.get('price_range', {}).get('max_price', 0)}",
	'location_based': 'Based on locations you explored',
	'similarity_based': 'Based on properties similar to your favorites',
	'behavioral_based': f"Based on your {behavioral_patterns.get('engagement_level', 'medium')} engagement pattern",
	'premium_properties': 'Based on your interest in high-value properties',
	'budget_friendly': 'Based on your value-conscious browsing',
	'trending_properties': 'Based on market trends and your preferences',
	'family_oriented': 'Based on your interest in family-suitable properties',
	'investment_opportunities': 'Based on your investment potential analysis'
	},
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error getting email analysis basis: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/email-content-preview/<int:customer_id>', methods=['GET'])
	def preview_all_email_content(customer_id):
	"""Preview content of all 10 email types that would be sent"""
	logger.info(f"👁️ Previewing all email content for customer {customer_id}")

	try:
	# Get customer analysis data
	analysis_data = self._get_analysis_data_parallel(customer_id)
	if not analysis_data:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	# Ensure ChromaDB is properly initialized and has properties
	if not hasattr(ai_engine, 'properties_collection') or ai_engine.properties_collection is None:
	logger.info("🚀 ChromaDB not initialized, initializing...")
	ai_engine.initialize_chromadb()

	# Check if ChromaDB has properties
	try:
	if ai_engine.properties_collection:
	collection_count = ai_engine.properties_collection.count()
	logger.info(f"📊 ChromaDB has {collection_count} properties")
	if collection_count == 0:
	logger.info("🚀 ChromaDB is empty, auto-fetching properties...")
	ai_engine.auto_fetch_and_store_properties()
	else:
	logger.warning("⚠️ ChromaDB collection is None, auto-fetching properties...")
	ai_engine.auto_fetch_and_store_properties()
	except Exception as count_error:
	logger.warning(f"⚠️ Could not check ChromaDB count: {count_error}")
	logger.info("🚀 Auto-fetching properties as fallback...")
	ai_engine.auto_fetch_and_store_properties()

	# Email types to generate
	email_types = [
	'property_based',
	'price_based',
	'location_based',
	'similarity_based',
	'behavioral_based',
	'premium_properties',
	'budget_friendly',
	'trending_properties',
	'family_oriented',
	'investment_opportunities'
	]

	email_previews = []

	for email_type in email_types:
	try:
	# Get recommendations using specialized AI model for this type
	recommendations = ai_engine.get_multi_ai_property_recommendations(
	analysis_data, ai_insights, email_type, count=12
	)
	logger.info(f"🤖 AI Model for {email_type} preview: {len(recommendations)} properties")

	# Add fallback if needed
	if len(recommendations) < 10:
	fallback_recommendations = ai_engine.search_similar_properties_chromadb(
	f"property {email_type.replace('_', ' ')} recommendation", [], 12
	)
	# Merge without duplicates
	seen_ids = {str(r.get('id', r.get('propertyId', ''))) for r in recommendations}
	for prop in fallback_recommendations:
	prop_id = str(prop.get('id', prop.get('propertyId', '')))
	if prop_id not in seen_ids and len(recommendations) < 12:
	recommendations.append(prop)
	seen_ids.add(prop_id)

	# Generate email content
	email_content = ai_engine.generate_personalized_email(
	analysis_data, ai_insights, recommendations, '[email protected]', email_type
	)

	if email_content.get('success'):
	email_previews.append({
	'email_type': email_type,
	'subject': email_content.get('subject', self._get_custom_subject(email_type, customer_id, ai_insights)),
	'html_content': email_content.get('html_content', ''),
	'text_content': email_content.get('text_content', ''),
	'recommendations_count': len(recommendations),
	'properties_included': [
	{
	'name': rec.get('property_name', rec.get('name', rec.get('title', 'Property'))),
	'price': rec.get('price', rec.get('marketValue', rec.get('amount', 0))),
	'type': rec.get('property_type', rec.get('type', rec.get('propertyTypeName', 'N/A'))),
	'score': rec.get('ai_score', rec.get('similarity_score', 0))
	} for rec in recommendations[:3]
	],
	'ai_insights': {
	'personality_type': email_content.get('personalization', {}).get('personality_type', 'N/A'),
	'decision_style': ai_insights.get('recommendation_strategy', 'N/A'),
	'urgency_level': email_content.get('personalization', {}).get('urgency_level', 'N/A'),
	'peak_activity': ai_insights.get('peak_time', 'N/A')
	}
	})
	else:
	email_previews.append({
	'email_type': email_type,
	'subject': self._get_custom_subject(email_type, customer_id, ai_insights),
	'error': email_content.get('error', 'Failed to generate content'),
	'recommendations_count': 0,
	'properties_included': []
	})

	except Exception as e:
	logger.error(f"❌ Error generating {email_type}: {e}")
	email_previews.append({
	'email_type': email_type,
	'subject': self._get_custom_subject(email_type, customer_id, ai_insights),
	'error': str(e),
	'recommendations_count': 0,
	'properties_included': []
	})

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'email_previews': email_previews,
	'total_emails': len(email_previews),
	'ai_insights': ai_insights,
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error previewing email content: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/test-all-10-emails/<int:customer_id>', methods=['POST'])
	def test_send_all_10_emails(customer_id):
	"""Test sending all 10 different email types with unique ChromaDB properties"""
	logger.info(f"🧪 Testing all 10 email types for customer {customer_id}")

	try:
	data = request.get_json() or {}
	recipient_email = data.get('email', '[email protected]')

	# Get customer analysis data
	analysis_data = self._get_analysis_data_parallel(customer_id)
	if not analysis_data:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	# Ensure ChromaDB has properties before generating recommendations
	if hasattr(ai_engine, 'properties_collection') and ai_engine.properties_collection:
	try:
	collection_count = ai_engine.properties_collection.count()
	if collection_count == 0:
	logger.info("🚀 ChromaDB is empty, auto-fetching properties...")
	ai_engine.auto_fetch_and_store_properties()
	except Exception as count_error:
	logger.warning(f"⚠️ Could not check ChromaDB count: {count_error}")
	# Try to fetch anyway
	ai_engine.auto_fetch_and_store_properties()
	else:
	logger.warning("⚠️ ChromaDB not initialized, auto-initializing...")
	ai_engine.initialize_chromadb()
	ai_engine.auto_fetch_and_store_properties()

	# Email types with their specific ChromaDB query strategies
	email_strategies = {
	'property_based': 'Get properties matching customer\'s preferred property types',
	'price_based': 'Get properties within customer\'s budget range',
	'location_based': 'Get properties in customer\'s preferred locations',
	'similarity_based': 'Get properties similar to customer\'s viewed properties',
	'behavioral_based': 'Get properties based on customer behavior patterns',
	'premium_properties': 'Get high-end luxury properties',
	'budget_friendly': 'Get value-for-money properties',
	'trending_properties': 'Get currently trending market properties',
	'family_oriented': 'Get family-suitable properties with amenities',
	'investment_opportunities': 'Get properties with high ROI potential'
	}

	sent_emails = []
	failed_emails = []

	for email_type, strategy in email_strategies.items():
	try:
	logger.info(f"📧 Processing {email_type} email with strategy: {strategy}")

	# Use specialized AI models for each email type - request 15 to ensure we get at least 10
	recommendations = ai_engine.get_multi_ai_property_recommendations(
	analysis_data, ai_insights, email_type, count=15
	)

	logger.info(f"🤖 AI Model for {email_type} retrieved {len(recommendations)} properties")

	# If we don't have enough properties, try a fallback query
	if len(recommendations) < 10:
	logger.warning(f"⚠️ Only {len(recommendations)} properties for {email_type}, trying fallback...")
	fallback_recommendations = ai_engine.search_similar_properties_chromadb(
	f"property {email_type.replace('_', ' ')} recommendation", [], 15
	)
	# Merge without duplicates
	seen_ids = {str(r.get('id', r.get('propertyId', ''))) for r in recommendations}
	for prop in fallback_recommendations:
	prop_id = str(prop.get('id', prop.get('propertyId', '')))
	if prop_id not in seen_ids and len(recommendations) < 15:
	recommendations.append(prop)
	seen_ids.add(prop_id)
	logger.info(f"🔄 After fallback: {len(recommendations)} properties for {email_type}")

	# Generate personalized email with email type
	email_result = ai_engine.generate_personalized_email(
	analysis_data, ai_insights, recommendations, recipient_email, email_type
	)

	# Add email type to the content for better file naming
	if isinstance(email_result, dict) and 'success' in email_result:
	email_content = email_result
	else:
	email_content = {'success': True}

	if email_content.get('success'):
	# Customize subject and content based on email type
	custom_subject = self._get_custom_subject(email_type, customer_id, ai_insights)
	email_content['subject'] = custom_subject

	# Send email
	success = ai_engine.send_email(recipient_email, email_content)

	if success:
	sent_emails.append({
	'type': email_type,
	'subject': custom_subject,
	'strategy': strategy,
	'recommendations_count': len(recommendations),
	'properties_from_chromadb': True,
	'timestamp': datetime.now().isoformat(),
	'sample_properties': [
	{
	'name': rec.get('property_name', 'Property'),
	'price': rec.get('price', 0),
	'type': rec.get('property_type', 'N/A'),
	'ai_score': rec.get('ai_score', 0)
	} for rec in recommendations[:3]
	]
	})
	logger.info(f"✅ {email_type} email sent successfully")
	else:
	failed_emails.append({
	'type': email_type,
	'strategy': strategy,
	'error': 'Failed to send email via SendGrid'
	})
	else:
	failed_emails.append({
	'type': email_type,
	'strategy': strategy,
	'error': email_content.get('error', 'Failed to generate email content')
	})

	except Exception as e:
	failed_emails.append({
	'type': email_type,
	'strategy': strategy,
	'error': str(e)
	})
	logger.error(f"❌ Error with {email_type}: {e}")

	return jsonify({
	'success': True,
	'customer_id': customer_id,
	'recipient_email': recipient_email,
	'test_results': {
	'total_attempted': len(email_strategies),
	'total_sent': len(sent_emails),
	'total_failed': len(failed_emails),
	'sent_emails': sent_emails,
	'failed_emails': failed_emails
	},
	'ai_insights': ai_insights,
	'chromadb_status': 'Active - Properties retrieved from vector database',
	'timestamp': datetime.now().isoformat()
	})

	except Exception as e:
	logger.error(f"❌ Error testing all 10 emails: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/chromadb-status', methods=['GET'])
	def check_chromadb_status():
	"""Check ChromaDB status and property count"""
	logger.info("🔍 Checking ChromaDB status")

	try:
	# Get AI engine
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Check ChromaDB initialization
	if not hasattr(ai_engine, 'properties_collection') or not ai_engine.properties_collection:
	return jsonify({
	'success': True,
	'chromadb_initialized': False,
	'property_count': 0,
	'message': 'ChromaDB not initialized'
	})

	# Get property count
	try:
	property_count = ai_engine.properties_collection.count()
	collection_name = ai_engine.properties_collection.name

	return jsonify({
	'success': True,
	'chromadb_initialized': True,
	'collection_name': collection_name,
	'property_count': property_count,
	'status': 'ready' if property_count > 0 else 'empty',
	'message': f'ChromaDB collection "{collection_name}" has {property_count} properties',
	'timestamp': datetime.now().isoformat()
	})

	except Exception as count_error:
	return jsonify({
	'success': False,
	'chromadb_initialized': True,
	'error': f'Collection access error: {count_error}',
	'message': 'ChromaDB collection may be corrupted'
	})

	except Exception as e:
	logger.error(f"❌ Error checking ChromaDB status: {e}")
	return jsonify({
	'success': False,
	'error': str(e)
	}), 500

	@self.app.route('/api/saved-emails', methods=['GET'])
	def list_saved_emails():
	"""List all saved email files"""
	try:
	import os
	import glob
	from datetime import datetime

	emails_dir = "./saved_emails"
	if not os.path.exists(emails_dir):
	return jsonify({'saved_emails': [], 'message': 'No saved emails directory found'})

	# Get all HTML files in the saved emails directory
	email_files = glob.glob(os.path.join(emails_dir, "*.html"))

	saved_emails = []
	for file_path in email_files:
	file_name = os.path.basename(file_path)
	file_stats = os.stat(file_path)
	created_time = datetime.fromtimestamp(file_stats.st_mtime).strftime('%Y-%m-%d %H:%M:%S')

	# Extract email type from filename
	email_type = file_name.split('_')[0] if '_' in file_name else 'unknown'

	saved_emails.append({
	'filename': file_name,
	'filepath': file_path,
	'email_type': email_type.replace('_', ' ').title(),
	'created_time': created_time,
	'size_kb': round(file_stats.st_size / 1024, 2)
	})

	# Sort by creation time (newest first)
	saved_emails.sort(key=lambda x: x['created_time'], reverse=True)

	return jsonify({
	'saved_emails': saved_emails,
	'total_count': len(saved_emails),
	'message': f'Found {len(saved_emails)} saved email files'
	})

	except Exception as e:
	logger.error(f"❌ Error listing saved emails: {e}")
	return jsonify({'error': str(e)}), 500

	@self.app.route('/view-email/<filename>')
	def view_saved_email(filename):
	"""Serve saved email file for viewing"""
	try:
	import os
	from flask import send_file

	emails_dir = "./saved_emails"
	file_path = os.path.join(emails_dir, filename)

	if not os.path.exists(file_path):
	return "Email file not found", 404

	return send_file(file_path, mimetype='text/html')

	except Exception as e:
	logger.error(f"❌ Error serving email file: {e}")
	return f"Error loading email: {str(e)}", 500

	@self.app.route('/api/DebtFinancing', methods=['PUT'])
	def update_debt_financing():
	"""Update debt financing information"""
	logger.info("💰 Debt financing update request")

	try:
	data = request.get_json()
	if not data:
	return jsonify({
	'success': False,
	'error': 'No data provided'
	}), 400

	# Validate required fields
	required_fields = ['customer_id', 'debt_amount', 'financing_type']
	for field in required_fields:
	if field not in data:
	return jsonify({
	'success': False,
	'error': f'Missing required field: {field}'
	}), 400

	# Process debt financing update
	customer_id = data.get('customer_id')
	debt_amount = data.get('debt_amount')
	financing_type = data.get('financing_type')

	# Create debt financing record
	debt_record = {
	'customer_id': customer_id,
	'debt_amount': debt_amount,
	'financing_type': financing_type,
	'interest_rate': data.get('interest_rate', 0),
	'term_months': data.get('term_months', 0),
	'monthly_payment': data.get('monthly_payment', 0),
	'status': data.get('status', 'pending'),
	'updated_at': datetime.now().isoformat(),
	'created_at': datetime.now().isoformat()
	}

	# Try to save to backend first
	backend_response = None
	try:
	backend_response = self.make_api_request(
	'/api/DebtFinancing',
	method='PUT',
	data=debt_record,
	use_fallback=False
	)
	except Exception as e:
	logger.warning(f"⚠️ Backend API failed for debt financing: {e}")

	# If backend fails, use fallback
	if not backend_response:
	logger.info("🔄 Using fallback for debt financing update")
	backend_response = {
	'success': True,
	'message': 'Debt financing updated successfully (fallback mode)',
	'data': debt_record,
	'fallback': True
	}

	return jsonify(backend_response)

	except Exception as e:
	logger.error(f"❌ Error updating debt financing: {e}")
	return jsonify({
	'success': False,
	'error': f'Failed to update debt financing: {str(e)}'
	}), 500

	@self.app.route('/api/Email/sendPlainText', methods=['POST'])
	def send_plain_text_email():
	"""Send plain text email using the external email API"""
	logger.info("📧 Plain text email send request")

	try:
	# Get parameters from query string or JSON body
	if request.is_json:
	data = request.get_json()
	to_email = data.get('toEmail')
	subject = data.get('subject')
	body = data.get('body')
	else:
	to_email = request.args.get('toEmail')
	subject = request.args.get('subject')
	body = request.args.get('body')

	# Validate required parameters
	if not to_email:
	return jsonify({
	'success': False,
	'error': 'toEmail parameter is required'
	}), 400

	if not subject:
	return jsonify({
	'success': False,
	'error': 'subject parameter is required'
	}), 400

	if not body:
	return jsonify({
	'success': False,
	'error': 'body parameter is required'
	}), 400

	# Validate email format
	if '@' not in to_email:
	return jsonify({
	'success': False,
	'error': 'Invalid email address format'
	}), 400

	# Prepare email data for external API
	email_data = {
	'toEmail': to_email,
	'subject': subject,
	'body': body
	}

	# Send email using external API
	external_api_url = f"{self.config['backend_url']}/api/Email/sendPlainText"

	try:
	logger.info(f"🌐 Sending email via external API: {external_api_url}")
	response = requests.post(
	external_api_url,
	params=email_data,
	headers=self.config['ngrok_headers'],
	timeout=30
	)

	if response.status_code == 200:
	logger.info(f"✅ Email sent successfully to {to_email}")
	return jsonify({
	'success': True,
	'message': 'Email sent successfully',
	'recipient': to_email,
	'subject': subject,
	'timestamp': datetime.now().isoformat()
	})
	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 requests.exceptions.Timeout:
	logger.error("⏰ Email API request timeout")
	return jsonify({
	'success': False,
	'error': 'Email service timeout'
	}), 504
	except requests.exceptions.ConnectionError:
	logger.error("🔌 Email API connection error")
	return jsonify({
	'success': False,
	'error': 'Email service unavailable'
	}), 503
	except Exception as e:
	logger.error(f"❌ Email API error: {e}")
	return jsonify({
	'success': False,
	'error': f'Email service error: {str(e)}'
	}), 500

	except Exception as e:
	logger.error(f"❌ Error sending plain text email: {e}")
	return jsonify({
	'success': False,
	'error': f'Failed to send email: {str(e)}'
	}), 500

	@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:
	return jsonify({
	'success': False,
	'error': 'Email address is required'
	}), 400

	# Get customer analysis data (use the same method as the main analysis)
	cache_key = f'lead_analysis_{customer_id}'
	analysis_data = self.get_cached_data(cache_key)

	if not analysis_data:
	# Generate fresh analysis data
	logger.info(f"🌐 Fetching fresh data for email automation - customer {customer_id}")
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')

	if not backend_response:
	return jsonify({
	'success': False,
	'error': 'No customer data found'
	}), 404

	processed_data = self.process_lead_data(backend_response, customer_id)
	analytics = self.generate_analytics(backend_response, customer_id)

	analysis_data = {
	'success': True,
	'customer_id': customer_id,
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': analytics,
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Get AI engine for analysis
	ai_engine = self.get_ai_engine()
	if not ai_engine:
	return jsonify({
	'success': False,
	'error': 'AI engine not available'
	}), 500

	# Generate AI insights from tracking data
	ai_insights = ai_engine.analyze_user_behavior_with_ai(analysis_data)

	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"🏡 New Properties Just Added - Perfect Matches for You!"

	body_lines = [
	f"Dear Valued Customer,",
	"",
	"🎉 Great news! We've just added new properties that match your preferences perfectly!",
	"",
	"📊 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.",
	"",
	"💡 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)
	result['ai_insights'] = {
	'personality_type': 'Analytical',
	'urgency_level': 'Medium',
	'peak_time': 'Evening'
	}

	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.get_enhanced_ai_recommendations(analysis_data, ai_insights, count=5)

	# Create personalized email content
	personality_type = ai_insights.get('ai_personality_type', 'Analytical')
	urgency_level = ai_insights.get('urgency_level', 'Medium')
	decision_style = ai_insights.get('recommendation_strategy', 'Data-driven')
	peak_time = ai_insights.get('peak_time', 'Evening')

	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: {decision_style}",
	f"• Engagement Level: {ai_insights.get('engagement_level', 'High')}",
	f"• Urgency Level: {urgency_level}",
	f"• Peak Activity Time: {peak_time}",
	"",
	"🎯 AI RECOMMENDATIONS:"
	]

	# Add AI recommendations
	if recommendations and isinstance(recommendations, list):
	for i, prop in enumerate(recommendations[:5], 1):
	body_lines.extend([
	f"{i}. {prop.get('property_name', prop.get('title', 'Recommended Property'))}",
	f" 📍 Location: {prop.get('location', 'N/A')}",
	f" 💰 Price: ${prop.get('price', 0):,}",
	f" 🎯 AI Match Score: {prop.get('ai_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([
	"💡 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"
	])

	email_body = "\n".join(body_lines)

	# Send email
	result = self._send_email_via_api(recipient_email, subject, email_body)
	result['type'] = 'ai_recommendations'
	result['ai_insights'] = {
	'personality_type': personality_type,
	'decision_making_style': decision_style,
	'urgency_level': urgency_level,
	'peak_time': peak_time
	}
	result['recommendations_count'] = len(recommendations) 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')

	# Extract AI insights for display
	personality_type = ai_insights.get('ai_personality_type', 'Analytical')
	urgency_level = ai_insights.get('urgency_level', 'Medium')

	# 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
	result['ai_insights'] = {
	'personality_type': personality_type,
	'urgency_level': urgency_level,
	'peak_time': peak_time
	}

	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': body
	}

	external_api_url = f"{self.config['backend_url']}/api/Email/sendPlainText"

	logger.info(f"🌐 Sending email via external API to {recipient_email}")
	response = requests.post(
	external_api_url,
	params=email_data,
	headers=self.config['ngrok_headers'],
	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
	}

	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')
	})

	return new_properties

	except Exception as e:
	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:
	"""Get analysis data for a customer using parallel processing"""
	try:
	# Try to get from cache first
	cache_key = f'lead_analysis_{customer_id}'
	cached_data = self.get_cached_data(cache_key)

	if cached_data:
	logger.info(f"📋 Returning cached analysis data for customer {customer_id}")
	return cached_data

	# Fetch from backend
	backend_response = None
	try:
	backend_response = self.make_api_request(f'/api/PropertyLeadQualification/customer/{customer_id}')
	except Exception as e:
	logger.warning(f"⚠️ Backend API failed: {e}")

	# If backend fails, use mock data
	if not backend_response and MOCK_DATA_AVAILABLE:
	logger.info(f"🔄 Using mock data for customer {customer_id}")
	backend_response = mock_data_service.get_customer_data(customer_id)

	if not backend_response:
	return None

	# Process the data
	processed_data = self.process_lead_data(backend_response, customer_id)
	analytics = self.generate_analytics(backend_response, customer_id)

	analysis_data = {
	'success': True,
	'customer_id': customer_id,
	'timestamp': datetime.now().isoformat(),
	'data': {
	'lead_qualification': analytics.get('lead_qualification', {}),
	'analytics': analytics,
	'properties': processed_data.get('properties', []),
	'summary': processed_data.get('summary', {})
	}
	}

	# Cache the result
	self.cache_data(cache_key, analysis_data)
	return analysis_data

	except Exception as e:
	logger.error(f"❌ Error getting analysis data for customer {customer_id}: {e}")
	return None

	def _get_custom_subject(self, recommendation_type: str, customer_id: int, ai_insights: Dict) -> str:
	"""Generate custom email subject based on recommendation type"""
	personality_type = ai_insights.get('personality_type', 'Analytical')
	urgency_level = ai_insights.get('urgency_level', 'Medium')

	subjects = {
	'property_based': f"🏡 Perfect Properties Matched to Your Preferences - Customer {customer_id}",
	'price_based': f"💰 Properties in Your Budget Range - Exclusive Deals for Customer {customer_id}",
	'location_based': f"📍 Prime Locations Just for You - Customer {customer_id}",
	'similarity_based': f"🔍 Properties Similar to Your Favorites - Customer {customer_id}",
	'behavioral_based': f"🧠 AI-Analyzed Recommendations Based on Your Behavior - Customer {customer_id}",
	'premium_properties': f"⭐ Premium Properties Collection - Customer {customer_id}",
	'budget_friendly': f"💵 Best Value Properties for Smart Investors - Customer {customer_id}",
	'trending_properties': f"📈 Trending Properties in Hot Markets - Customer {customer_id}",
	'family_oriented': f"👨‍👩‍👧‍👦 Family-Perfect Properties - Customer {customer_id}",
	'investment_opportunities': f"💼 Investment Opportunities with High ROI - Customer {customer_id}"
	}

	base_subject = subjects.get(recommendation_type, f"🤖 AI Recommendations for Customer {customer_id}")

	# Add urgency indicators for high urgency
	if urgency_level == 'High':
	base_subject = f"⚡ URGENT: {base_subject}"
	elif urgency_level == 'Very High':
	base_subject = f"🔥 HOT DEALS: {base_subject}"

	return base_subject

	# Include all the original analysis methods from api_service.py
	def clean_for_json(self, obj):
	"""Clean object for JSON serialization"""
	def clean_value(value):
	import numpy as np

	# Handle numpy types
	if isinstance(value, (np.float32, np.float64, np.int32, np.int64)):
	value = float(value)
	elif isinstance(value, np.ndarray):
	return value.tolist()

	# Handle regular floats
	if isinstance(value, float):
	if math.isinf(value) and value > 0:
	return 999999999
	elif math.isinf(value) and value < 0:
	return -999999999
	elif math.isnan(value):
	return 0
	return value

	def clean_dict(d):
	if isinstance(d, dict):
	return {k: clean_value(v) if not isinstance(v, (dict, list)) else clean_dict(v) for k, v in d.items()}
	elif isinstance(d, list):
	return [clean_value(v) if not isinstance(v, (dict, list)) else clean_dict(v) for v in d]
	else:
	return clean_value(d)

	return clean_dict(obj)

	def make_api_request(self, endpoint: str, method: str = 'GET', data: Dict = None, use_fallback: bool = True) -> Any:
	"""Make API request to backend with robust fallback mechanism"""
	max_retries = self.config.get('max_retries', 3)
	retry_delay = self.config.get('retry_delay', 1)

	for attempt in range(max_retries + 1):
	try:
	url = f"{self.config['backend_url']}{endpoint}"
	logger.info(f"🌐 Making {method} request to: {url} (attempt {attempt + 1}/{max_retries + 1})")

	headers = self.config['ngrok_headers']

	# Add timeout for better reliability
	timeout = 30 if attempt < max_retries else 60

	if method.upper() == 'GET':
	response = requests.get(url, headers=headers, timeout=timeout)
	elif method.upper() == 'POST':
	response = requests.post(url, headers=headers, json=data, timeout=timeout)
	elif method.upper() == 'PUT':
	response = requests.put(url, headers=headers, json=data, timeout=timeout)
	else:
	raise ValueError(f"Unsupported HTTP method: {method}")

	if response.status_code == 200:
	logger.info(f"✅ API request successful on attempt {attempt + 1}")
	return response.json()
	elif response.status_code in [429, 502, 503, 504] and attempt < max_retries:
	# Retry on rate limiting or server errors
	logger.warning(f"⚠️ API request failed with {response.status_code}, retrying in {retry_delay}s...")
	time.sleep(retry_delay * (attempt + 1)) # Exponential backoff
	continue
	else:
	logger.error(f"❌ API request failed: {response.status_code} - {response.text}")
	if not use_fallback:
	return None
	break

	except requests.exceptions.Timeout as e:
	logger.warning(f"⏰ Request timeout on attempt {attempt + 1}: {e}")
	if attempt < max_retries:
	time.sleep(retry_delay * (attempt + 1))
	continue
	else:
	logger.error(f"❌ All retry attempts failed due to timeout")
	break
	except requests.exceptions.ConnectionError as e:
	logger.warning(f"🔌 Connection error on attempt {attempt + 1}: {e}")
	if attempt < max_retries:
	time.sleep(retry_delay * (attempt + 1))
	continue
	else:
	logger.error(f"❌ All retry attempts failed due to connection error")
	break
	except Exception as e:
	logger.error(f"❌ API request error on attempt {attempt + 1}: {e}")
	if attempt < max_retries:
	time.sleep(retry_delay * (attempt + 1))
	continue
	else:
	break

	# If all attempts failed and fallback is enabled, return fallback data
	if use_fallback:
	logger.info(f"🔄 All API attempts failed, using fallback data for endpoint: {endpoint}")
	return self.get_fallback_data(endpoint, method, data)

	return None

	def get_fallback_data(self, endpoint: str, method: str, data: Dict = None) -> Any:
	"""Get fallback data when API requests fail"""
	try:
	# Extract customer ID from endpoint if it's a customer-specific endpoint
	customer_id = None
	if '/customer/' in endpoint:
	try:
	customer_id = int(endpoint.split('/customer/')[-1])
	except (ValueError, IndexError):
	pass

	# Use mock data service as fallback
	if MOCK_DATA_AVAILABLE and customer_id:
	logger.info(f"🔄 Using mock data fallback for customer {customer_id}")
	return mock_data_service.get_customer_data(customer_id)

	# Return empty structure for other endpoints
	if 'PropertyLeadQualification' in endpoint:
	return []
	elif 'DebtFinancing' in endpoint:
	return {'success': False, 'message': 'Service temporarily unavailable', 'fallback': True}
	else:
	return {'success': False, 'message': 'Service temporarily unavailable', 'fallback': True}

	except Exception as e:
	logger.error(f"❌ Fallback data generation failed: {e}")
	return None

	# All the original analysis methods from api_service.py are included below

	def process_lead_data(self, data: List[Dict], customer_id: int) -> Dict:
	"""Process and enhance lead qualification data"""
	# Implementation from original api_service.py
	if not data:
	return {
	'customer_id': customer_id,
	'properties': [],
	'summary': {
	'total_properties': 0,
	'total_views': 0,
	'total_duration': 0,
	'average_price': 0,
	'property_types': {},
	'engagement_score': 0
	}
	}

	# Calculate summary statistics
	total_views = sum(prop.get('viewCount', 0) for prop in data)
	total_duration = sum(prop.get('totalDuration', 0) for prop in data)
	total_price = sum(prop.get('price', 0) for prop in data)

	# Property type distribution
	property_types = {}
	for prop in data:
	prop_type = prop.get('propertyTypeName', 'Unknown')
	property_types[prop_type] = property_types.get(prop_type, 0) + 1

	# Calculate engagement score
	engagement_score = self.calculate_engagement_score(data)

	# Enhance each property with additional metrics
	enhanced_properties = []
	for prop in data:
	enhanced_prop = prop.copy()
	enhanced_prop['engagement_score'] = self.calculate_property_engagement(prop)
	enhanced_prop['price_per_sqm'] = self.estimate_price_per_sqm(prop.get('price', 0))
	enhanced_prop['view_frequency'] = self.calculate_view_frequency(prop)
	enhanced_prop['last_viewed_days_ago'] = self.days_since_last_view(prop.get('lastViewedAt'))
	enhanced_properties.append(enhanced_prop)

	return {
	'customer_id': customer_id,
	'properties': enhanced_properties,
	'summary': {
	'total_properties': len(data),
	'total_views': total_views,
	'total_duration': total_duration,
	'average_price': total_price / len(data) if data else 0,
	'property_types': property_types,
	'engagement_score': engagement_score,
	'last_activity': max((prop.get('lastViewedAt') for prop in data), default=None)
	}
	}

	def generate_analytics(self, lead_data: List[Dict], customer_id: int) -> Dict:
	"""Generate comprehensive analytics from lead data"""
	if not lead_data:
	return {'customer_id': customer_id, 'analytics': {}}

	# Process the data
	processed_data = self.process_lead_data(lead_data, customer_id)

	# Generate detailed lead qualification
	lead_qualification = self.generate_detailed_lead_qualification(processed_data)

	# Generate insights
	insights = {
	'customer_id': customer_id,
	'lead_qualification': lead_qualification,
	'engagement_level': self.categorize_engagement(processed_data['summary']['engagement_score']),
	'preferred_property_types': self.get_preferred_property_types(processed_data['summary']['property_types']),
	'price_preferences': self.analyze_price_preferences(lead_data),
	'viewing_patterns': self.analyze_viewing_patterns(lead_data),
	'property_analysis': self.analyze_properties_detailed(lead_data),
	'recommendations': self.generate_recommendations(processed_data),
	'risk_assessment': self.assess_risk(processed_data),
	'opportunity_score': self.calculate_opportunity_score(processed_data),
	'lead_timeline': self.generate_lead_timeline(lead_data),
	'conversion_probability': self.calculate_conversion_probability(processed_data)
	}

	return insights

	# Add placeholder methods for the analysis functions
	def generate_detailed_lead_qualification(self, processed_data: Dict) -> Dict:
	"""Generate detailed lead qualification with warm/cold/hot status"""
	summary = processed_data['summary']
	properties = processed_data['properties']

	# Calculate lead score based on multiple factors
	lead_score = 0
	factors = {}

	# 1. Engagement Score (0-30 points)
	engagement_points = min(summary['engagement_score'] * 0.3, 30)
	lead_score += engagement_points
	factors['engagement'] = {
	'score': summary['engagement_score'],
	'points': engagement_points,
	'max_points': 30,
	'description': f"Based on {summary['total_views']} views and {formatDuration(summary['total_duration'])} total viewing time"
	}

	# 2. Property Diversity (0-15 points)
	property_types = len(summary['property_types'])
	diversity_points = min(property_types * 5, 15)
	lead_score += diversity_points
	factors['property_diversity'] = {
	'score': property_types,
	'points': diversity_points,
	'max_points': 15,
	'description': f"Viewed {property_types} different property types"
	}

	# 3. Recent Activity (0-20 points)
	recent_activity_points = 0
	if summary.get('last_activity'):
	try:
	last_activity = datetime.fromisoformat(summary['last_activity'].replace('Z', '+00:00'))
	days_since = (datetime.now() - last_activity).days
	if days_since <= 1:
	recent_activity_points = 20
	elif days_since <= 3:
	recent_activity_points = 15
	elif days_since <= 7:
	recent_activity_points = 10
	elif days_since <= 30:
	recent_activity_points = 5
	except:
	pass

	lead_score += recent_activity_points
	factors['recent_activity'] = {
	'score': days_since if 'days_since' in locals() else 999,
	'points': recent_activity_points,
	'max_points': 20,
	'description': f"Last activity was {days_since if 'days_since' in locals() else 'unknown'} days ago"
	}

	# 4. Price Range Consistency (0-15 points)
	prices = [p['price'] for p in properties if p['price'] > 0]
	if prices:
	price_range = max(prices) - min(prices)
	price_consistency = 1 - (price_range / max(prices))
	consistency_points = price_consistency * 15
	lead_score += consistency_points
	factors['price_consistency'] = {
	'score': round(price_consistency * 100, 1),
	'points': consistency_points,
	'max_points': 15,
	'description': f"Price range consistency: {round(price_consistency * 100, 1)}%"
	}

	# 5. Viewing Depth (0-20 points)
	deep_views = sum(1 for p in properties if p['viewCount'] > 1)
	depth_points = min(deep_views * 5, 20)
	lead_score += depth_points
	factors['viewing_depth'] = {
	'score': deep_views,
	'points': depth_points,
	'max_points': 20,
	'description': f"{deep_views} properties viewed multiple times"
	}

	# Determine lead status
	if lead_score >= 70:
	lead_status = "HOT"
	status_color = "#e74c3c"
	status_description = "High probability of conversion. Immediate follow-up recommended."
	elif lead_score >= 50:
	lead_status = "WARM"
	status_color = "#f39c12"
	status_description = "Good potential. Regular follow-up needed."
	elif lead_score >= 30:
	lead_status = "LUKEWARM"
	status_color = "#95a5a6"
	status_description = "Some interest shown. Nurturing required."
	else:
	lead_status = "COLD"
	status_color = "#34495e"
	status_description = "Low engagement. Re-engagement campaign needed."

	return {
	'lead_score': round(lead_score, 1),
	'lead_status': lead_status,
	'status_color': status_color,
	'status_description': status_description,
	'max_possible_score': 100,
	'factors': factors,
	'summary': {
	'total_properties_viewed': summary['total_properties'],
	'total_views': summary['total_views'],
	'total_duration': summary['total_duration'],
	'average_price': summary['average_price'],
	'engagement_score': summary['engagement_score']
	}
	}

	def calculate_engagement_score(self, properties: List[Dict]) -> float:
	"""Calculate overall engagement score for customer"""
	if not properties:
	return 0.0

	total_score = 0
	for prop in properties:
	views = prop.get('viewCount', 0)
	duration = prop.get('totalDuration', 0)

	# Weight views and duration
	view_score = min(views * 10, 50) # Max 50 points for views
	duration_score = min(duration / 1000, 50) # Max 50 points for duration

	total_score += view_score + duration_score

	return total_score / len(properties)

	def calculate_property_engagement(self, property_data: Dict) -> float:
	"""Calculate engagement score for a single property"""
	views = property_data.get('viewCount', 0)
	duration = property_data.get('totalDuration', 0)

	view_score = min(views * 10, 50)
	duration_score = min(duration / 1000, 50)

	return view_score + duration_score

	def estimate_price_per_sqm(self, price: float) -> float:
	"""Estimate price per square meter (rough estimation)"""
	return price / 150 if price > 0 else 0

	def calculate_view_frequency(self, property_data: Dict) -> str:
	"""Calculate viewing frequency"""
	views = property_data.get('viewCount', 0)
	if views == 0:
	return "No views"
	elif views == 1:
	return "Single view"
	elif views <= 3:
	return "Low frequency"
	elif views <= 7:
	return "Medium frequency"
	else:
	return "High frequency"

	def days_since_last_view(self, last_viewed: str) -> int:
	"""Calculate days since last view"""
	if not last_viewed:
	return 999

	try:
	last_view_date = datetime.fromisoformat(last_viewed.replace('Z', '+00:00'))
	days_diff = (datetime.now() - last_view_date).days
	return max(0, days_diff)
	except:
	return 999

	def categorize_engagement(self, score: float) -> str:
	"""Categorize engagement level"""
	if score >= 80:
	return "Very High"
	elif score >= 60:
	return "High"
	elif score >= 40:
	return "Medium"
	elif score >= 20:
	return "Low"
	else:
	return "Very Low"

	def get_preferred_property_types(self, property_types: Dict) -> List[str]:
	"""Get preferred property types"""
	if not property_types:
	return []

	sorted_types = sorted(property_types.items(), key=lambda x: x[1], reverse=True)
	return [prop_type for prop_type, count in sorted_types[:3]]

	def analyze_price_preferences(self, properties: List[Dict]) -> Dict:
	"""Analyze price preferences"""
	if not properties:
	return {}

	prices = [prop.get('price', 0) for prop in properties]
	prices = [p for p in prices if p > 0]

	if not prices:
	return {}

	return {
	'min_price': min(prices),
	'max_price': max(prices),
	'avg_price': sum(prices) / len(prices),
	'price_range': max(prices) - min(prices)
	}

	def analyze_viewing_patterns(self, properties: List[Dict]) -> Dict:
	"""Analyze viewing patterns"""
	if not properties:
	return {}

	# Group by viewing time
	morning_views = 0
	afternoon_views = 0
	evening_views = 0

	for prop in properties:
	if prop.get('lastViewedAt'):
	try:
	view_time = datetime.fromisoformat(prop['lastViewedAt'].replace('Z', '+00:00'))
	hour = view_time.hour

	if 6 <= hour < 12:
	morning_views += prop.get('viewCount', 0)
	elif 12 <= hour < 18:
	afternoon_views += prop.get('viewCount', 0)
	else:
	evening_views += prop.get('viewCount', 0)
	except:
	pass

	return {
	'morning_views': morning_views,
	'afternoon_views': afternoon_views,
	'evening_views': evening_views,
	'peak_viewing_time': 'morning' if morning_views > max(afternoon_views, evening_views) else
	'afternoon' if afternoon_views > evening_views else 'evening'
	}

	def analyze_properties_detailed(self, properties: List[Dict]) -> Dict:
	"""Detailed analysis of each property"""
	return {'analyzed_properties': len(properties)}

	def generate_recommendations(self, processed_data: Dict) -> List[str]:
	"""Generate recommendations based on data"""
	recommendations = []
	summary = processed_data['summary']

	if summary['engagement_score'] < 30:
	recommendations.append("Low engagement detected. Consider follow-up communication.")

	if summary['total_views'] < 5:
	recommendations.append("Limited property exploration. Suggest more property options.")

	if summary['average_price'] > 20000000: # 20M
	recommendations.append("High-value customer. Consider premium properties.")

	if len(summary['property_types']) > 3:
	recommendations.append("Diverse property interests. Offer variety in recommendations.")

	return recommendations

	def assess_risk(self, processed_data: Dict) -> Dict:
	"""Assess risk factors"""
	summary = processed_data['summary']

	risk_factors = []
	risk_score = 0

	if summary['engagement_score'] < 20:
	risk_factors.append("Very low engagement")
	risk_score += 30

	if summary['total_views'] < 3:
	risk_factors.append("Limited property exploration")
	risk_score += 20

	if summary['average_price'] > 50000000: # 50M
	risk_factors.append("Very high price range")
	risk_score += 15

	return {
	'risk_score': min(risk_score, 100),
	'risk_level': 'High' if risk_score > 50 else 'Medium' if risk_score > 25 else 'Low',
	'risk_factors': risk_factors
	}

	def calculate_opportunity_score(self, processed_data: Dict) -> float:
	"""Calculate opportunity score"""
	summary = processed_data['summary']

	# Base score from engagement
	score = summary['engagement_score']

	# Bonus for high-value properties
	if summary['average_price'] > 20000000:
	score += 20

	# Bonus for multiple property types
	if len(summary['property_types']) > 2:
	score += 10

	# Bonus for recent activity
	if summary.get('last_activity'):
	try:
	last_activity = datetime.fromisoformat(summary['last_activity'].replace('Z', '+00:00'))
	days_since = (datetime.now() - last_activity).days
	if days_since <= 7:
	score += 15
	elif days_since <= 30:
	score += 10
	except:
	pass

	return min(score, 100)

	def generate_lead_timeline(self, properties: List[Dict]) -> List[Dict]:
	"""Generate timeline of lead activity"""
	timeline = []

	for prop in properties:
	if prop.get('lastViewedAt'):
	try:
	view_date = datetime.fromisoformat(prop['lastViewedAt'].replace('Z', '+00:00'))
	timeline.append({
	'date': view_date.isoformat(),
	'property_name': prop['propertyName'],
	'property_type': prop['propertyTypeName'],
	'price': prop['price'],
	'views': prop['viewCount'],
	'duration': prop['totalDuration'],
	'engagement_score': prop.get('engagement_score', 0)
	})
	except:
	pass

	# Sort by date
	timeline.sort(key=lambda x: x['date'], reverse=True)
	return timeline

	def calculate_conversion_probability(self, processed_data: Dict) -> Dict:
	"""Calculate probability of conversion based on various factors"""
	summary = processed_data['summary']
	properties = processed_data['properties']

	# Base probability
	base_probability = min(summary['engagement_score'], 100)

	# Adjustments based on factors
	adjustments = {}

	# Price range consistency
	prices = [p['price'] for p in properties if p['price'] > 0]
	if prices:
	price_range = max(prices) - min(prices)
	price_consistency = 1 - (price_range / max(prices))
	adjustments['price_consistency'] = price_consistency * 10

	# Recent activity
	if summary.get('last_activity'):
	try:
	last_activity = datetime.fromisoformat(summary['last_activity'].replace('Z', '+00:00'))
	days_since = (datetime.now() - last_activity).days
	if days_since <= 1:
	adjustments['recent_activity'] = 15
	elif days_since <= 3:
	adjustments['recent_activity'] = 10
	elif days_since <= 7:
	adjustments['recent_activity'] = 5
	except:
	pass

	# Property diversity
	property_types = len(summary['property_types'])
	adjustments['property_diversity'] = min(property_types * 3, 15)

	# Deep engagement
	deep_views = sum(1 for p in properties if p['viewCount'] > 1)
	adjustments['deep_engagement'] = min(deep_views * 5, 20)

	# Calculate final probability
	total_adjustment = sum(adjustments.values())
	final_probability = min(base_probability + total_adjustment, 100)

	return {
	'base_probability': base_probability,
	'adjustments': adjustments,
	'total_adjustment': total_adjustment,
	'final_probability': final_probability,
	'confidence_level': 'High' if len(properties) >= 5 else 'Medium' if len(properties) >= 3 else 'Low'
	}

	def get_cached_data(self, key: str) -> Optional[Any]:
	"""Get data from cache if not expired"""
	if key in self.cache and key in self.cache_timestamps:
	if time.time() - self.cache_timestamps[key] < self.config['cache_duration']:
	return self.cache[key]
	else:
	# Remove expired cache
	del self.cache[key]
	del self.cache_timestamps[key]
	return None

	def cache_data(self, key: str, data: Any):
	"""Cache data with timestamp"""
	self.cache[key] = data
	self.cache_timestamps[key] = time.time()

	def start_cache_cleanup(self):
	"""Start background thread to clean up expired cache"""
	def cleanup_cache():
	while True:
	try:
	current_time = time.time()
	expired_keys = [
	key for key, timestamp in self.cache_timestamps.items()
	if current_time - timestamp > self.config['cache_duration']
	]

	for key in expired_keys:
	self.cache.pop(key, None)
	self.cache_timestamps.pop(key, None)

	time.sleep(60) # Clean up every minute
	except Exception as e:
	logger.error(f"Cache cleanup error: {e}")
	time.sleep(60)

	cleanup_thread = threading.Thread(target=cleanup_cache, daemon=True)
	cleanup_thread.start()

	def run(self, host: str = '0.0.0.0', port: int = 5000, debug: bool = False):
	"""Run the Flask application"""
	logger.info(f"🚀 Starting Enhanced Lead Qualification API Service with AI on {host}:{port}")
	logger.info(f"🌐 Backend URL: {self.config['backend_url']}")
	logger.info(f"🤖 AI Features: Enabled")
	logger.info(f"📊 API Endpoints:")
	logger.info(f" - GET /api/health")
	logger.info(f" - GET /api/lead-analysis/<customer_id>")
	logger.info(f" - GET /api/ai-analysis/<customer_id>")
	logger.info(f" - POST /api/ai-recommendations/<customer_id>")
	logger.info(f" - POST /api/multi-ai-recommendations/<customer_id>")
	logger.info(f" - GET /api/chromadb-recommendations/<customer_id>")
	logger.info(f" - GET /api/ai-status/<customer_id>")
	logger.info(f" - POST /api/batch-ai-analysis")
	logger.info(f" - GET /api/fetch-all-properties")
	logger.info(f" - POST /api/clear-cache")
	logger.info(f" - POST /api/test-email")
	logger.info(f" - POST /api/automated-email")
	logger.info(f" - GET /api/email-status")
	logger.info(f" 🆕 NEW EMAIL TESTING ENDPOINTS:")
	logger.info(f" - GET /api/test-sendgrid-connection")
	logger.info(f" - GET /api/email-analysis-basis/<customer_id>")
	logger.info(f" - GET /api/email-content-preview/<customer_id>")
	logger.info(f" - POST /api/test-all-10-emails/<customer_id>")
	logger.info(f" - GET /api/chromadb-status")
	self.app.run(host=host, port=port, debug=debug)

	if __name__ == '__main__':
	api_service = EnhancedLeadQualificationAPI()
	api_service.run(debug=True)