import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
import logging
import asyncio
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import dns.resolver
import re
import json

@dataclass
class EmailHealthMetrics:
    email_address: str
    last_opened: Optional[datetime]
    last_clicked: Optional[datetime]
    bounce_count: int
    bounce_type: str  # 'none', 'soft', 'hard'
    engagement_score: float
    deliverability_status: str
    validation_date: datetime
    risk_indicators: List[str] = field(default_factory=list)

@dataclass
class ListSegmentHealth:
    segment_name: str
    total_contacts: int
    active_contacts: int
    bounced_contacts: int
    unengaged_contacts: int
    health_score: float
    degradation_rate: float
    recommended_actions: List[str] = field(default_factory=list)

class EmailListHygieneAutomation:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.logger = logging.getLogger(__name__)
        
        # Automation thresholds
        self.engagement_thresholds = {
            'critical': 0.1,    # 10% engagement or less
            'poor': 0.2,        # 20% engagement or less
            'fair': 0.4,        # 40% engagement or less
            'good': 0.6         # 60% engagement or more
        }
        
        self.bounce_thresholds = {
            'hard_bounce_removal': 1,      # Remove after 1 hard bounce
            'soft_bounce_removal': 5,      # Remove after 5 soft bounces
            'consecutive_bounces': 3       # Flag after 3 consecutive bounces
        }
        
        self.inactivity_thresholds = {
            'dormant_days': 90,           # No engagement in 90 days
            'inactive_days': 180,         # No engagement in 180 days
            'zombie_days': 365            # No engagement in 365 days
        }
        
        # Initialize automation components
        self.verification_apis = {}
        self.engagement_tracker = {}
        self.cleanup_workflows = {}
        
        self._initialize_automation_systems()
    
    def _initialize_automation_systems(self):
        """Initialize automated list hygiene systems"""
        
        # Configure email verification APIs
        self.verification_apis = {
            'primary': self._configure_primary_verification(),
            'secondary': self._configure_secondary_verification(),
            'real_time': self._configure_realtime_verification()
        }
        
        # Setup engagement tracking
        self.engagement_tracker = {
            'email_platform': self._configure_email_platform_tracking(),
            'web_analytics': self._configure_web_analytics_tracking(),
            'crm_integration': self._configure_crm_tracking()
        }
        
        # Initialize cleanup workflows
        self.cleanup_workflows = {
            'daily_monitoring': self._configure_daily_monitoring(),
            'weekly_cleanup': self._configure_weekly_cleanup(),
            'monthly_deep_clean': self._configure_monthly_deep_clean(),
            'quarterly_audit': self._configure_quarterly_audit()
        }
        
        self.logger.info("Email list hygiene automation systems initialized")
    
    async def execute_automated_hygiene_cycle(self) -> Dict[str, Any]:
        """Execute comprehensive automated hygiene cycle"""
        
        cycle_results = {
            'cycle_start': datetime.now(),
            'contacts_processed': 0,
            'actions_taken': {},
            'health_improvements': {},
            'workflow_results': {},
            'recommendations': []
        }
        
        # Phase 1: Data quality assessment
        self.logger.info("Starting automated data quality assessment")
        quality_assessment = await self._assess_database_quality()
        cycle_results['quality_assessment'] = quality_assessment
        
        # Phase 2: Engagement analysis
        self.logger.info("Analyzing subscriber engagement patterns")
        engagement_analysis = await self._analyze_engagement_patterns()
        cycle_results['engagement_analysis'] = engagement_analysis
        
        # Phase 3: Automated verification
        self.logger.info("Executing automated email verification")
        verification_results = await self._execute_automated_verification()
        cycle_results['verification_results'] = verification_results
        
        # Phase 4: Intelligent segmentation
        self.logger.info("Performing intelligent list segmentation")
        segmentation_results = await self._perform_intelligent_segmentation()
        cycle_results['segmentation_results'] = segmentation_results
        
        # Phase 5: Automated cleanup actions
        self.logger.info("Executing automated cleanup actions")
        cleanup_results = await self._execute_cleanup_actions()
        cycle_results['cleanup_results'] = cleanup_results
        
        # Phase 6: Performance monitoring
        self.logger.info("Monitoring post-cleanup performance")
        performance_monitoring = await self._monitor_cleanup_performance()
        cycle_results['performance_monitoring'] = performance_monitoring
        
        # Generate comprehensive report
        cycle_results['summary_report'] = self._generate_hygiene_report(cycle_results)
        cycle_results['cycle_end'] = datetime.now()
        
        return cycle_results
    
    async def _assess_database_quality(self) -> Dict[str, Any]:
        """Assess overall email database quality"""
        
        assessment = {
            'total_contacts': 0,
            'quality_scores': {},
            'risk_indicators': {},
            'validation_status': {},
            'recommendations': []
        }
        
        # Fetch current database state
        database_state = await self._fetch_database_state()
        assessment['total_contacts'] = len(database_state['contacts'])
        
        # Analyze quality indicators
        quality_indicators = [
            'syntax_validation',
            'domain_validation', 
            'mx_record_validation',
            'smtp_validation',
            'engagement_validation'
        ]
        
        for indicator in quality_indicators:
            indicator_results = await self._analyze_quality_indicator(
                indicator, database_state['contacts']
            )
            assessment['quality_scores'][indicator] = indicator_results
        
        # Identify risk patterns
        risk_patterns = await self._identify_risk_patterns(database_state['contacts'])
        assessment['risk_indicators'] = risk_patterns
        
        # Generate quality-based recommendations
        assessment['recommendations'] = self._generate_quality_recommendations(assessment)
        
        return assessment
    
    async def _analyze_engagement_patterns(self) -> Dict[str, Any]:
        """Analyze subscriber engagement patterns for automated segmentation"""
        
        engagement_analysis = {
            'engagement_segments': {},
            'behavioral_patterns': {},
            'trend_analysis': {},
            'automation_triggers': {}
        }
        
        # Fetch engagement data from multiple sources
        engagement_data = await self._fetch_engagement_data()
        
        # Segment by engagement level
        engagement_segments = self._segment_by_engagement(engagement_data)
        engagement_analysis['engagement_segments'] = engagement_segments
        
        # Analyze behavioral patterns
        behavioral_patterns = await self._analyze_behavioral_patterns(engagement_data)
        engagement_analysis['behavioral_patterns'] = behavioral_patterns
        
        # Track engagement trends
        trend_analysis = await self._analyze_engagement_trends(engagement_data)
        engagement_analysis['trend_analysis'] = trend_analysis
        
        # Define automation triggers
        automation_triggers = self._define_automation_triggers(engagement_analysis)
        engagement_analysis['automation_triggers'] = automation_triggers
        
        return engagement_analysis
    
    def _segment_by_engagement(self, engagement_data: Dict[str, Any]) -> Dict[str, List[str]]:
        """Segment contacts by engagement level"""
        
        segments = {
            'highly_engaged': [],      # Opened or clicked in last 30 days
            'moderately_engaged': [],  # Opened or clicked in last 90 days
            'low_engaged': [],         # Opened or clicked in last 180 days
            'dormant': [],             # No engagement in 180+ days
            'zombie': []               # No engagement in 365+ days
        }
        
        current_date = datetime.now()
        
        for contact_id, engagement_info in engagement_data.items():
            last_engagement = engagement_info.get('last_engagement')
            
            if not last_engagement:
                segments['zombie'].append(contact_id)
                continue
            
            days_since_engagement = (current_date - last_engagement).days
            
            if days_since_engagement <= 30:
                segments['highly_engaged'].append(contact_id)
            elif days_since_engagement <= 90:
                segments['moderately_engaged'].append(contact_id)
            elif days_since_engagement <= 180:
                segments['low_engaged'].append(contact_id)
            elif days_since_engagement <= 365:
                segments['dormant'].append(contact_id)
            else:
                segments['zombie'].append(contact_id)
        
        return segments
    
    async def _execute_automated_verification(self) -> Dict[str, Any]:
        """Execute automated email verification workflow"""
        
        verification_results = {
            'verified_count': 0,
            'invalid_count': 0,
            'risky_count': 0,
            'verification_actions': {},
            'api_performance': {}
        }
        
        # Fetch contacts requiring verification
        contacts_to_verify = await self._identify_verification_candidates()
        
        # Execute verification in batches
        batch_size = self.config.get('verification_batch_size', 1000)
        verification_batches = [
            contacts_to_verify[i:i + batch_size] 
            for i in range(0, len(contacts_to_verify), batch_size)
        ]
        
        for batch_index, batch in enumerate(verification_batches):
            self.logger.info(f"Processing verification batch {batch_index + 1}/{len(verification_batches)}")
            
            batch_results = await self._verify_email_batch(batch)
            
            # Process verification results
            for contact_id, verification_result in batch_results.items():
                if verification_result['status'] == 'valid':
                    verification_results['verified_count'] += 1
                elif verification_result['status'] == 'invalid':
                    verification_results['invalid_count'] += 1
                    await self._handle_invalid_email(contact_id, verification_result)
                else:
                    verification_results['risky_count'] += 1
                    await self._handle_risky_email(contact_id, verification_result)
            
            # Rate limiting and API management
            await asyncio.sleep(self.config.get('batch_delay_seconds', 1))
        
        return verification_results
    
    async def _verify_email_batch(self, batch: List[str]) -> Dict[str, Dict[str, Any]]:
        """Verify a batch of email addresses using multiple APIs"""
        
        batch_results = {}
        
        # Primary verification API
        try:
            primary_results = await self._call_verification_api('primary', batch)
            batch_results.update(primary_results)
        except Exception as e:
            self.logger.warning(f"Primary verification API failed: {e}")
            
            # Fallback to secondary API
            try:
                secondary_results = await self._call_verification_api('secondary', batch)
                batch_results.update(secondary_results)
            except Exception as e:
                self.logger.error(f"Secondary verification API also failed: {e}")
                
                # Manual verification fallback
                manual_results = await self._manual_verification_fallback(batch)
                batch_results.update(manual_results)
        
        return batch_results
    
    async def _perform_intelligent_segmentation(self) -> Dict[str, Any]:
        """Perform intelligent list segmentation based on multiple factors"""
        
        segmentation_results = {
            'segment_definitions': {},
            'contact_assignments': {},
            'segment_health_scores': {},
            'automation_rules': {}
        }
        
        # Define intelligent segments
        segment_definitions = {
            'champions': {
                'criteria': 'high_engagement AND recent_activity AND no_bounces',
                'description': 'Most engaged subscribers with consistent interaction',
                'treatment': 'premium_content_frequency'
            },
            'loyalists': {
                'criteria': 'moderate_engagement AND long_tenure AND low_bounces', 
                'description': 'Long-term subscribers with steady engagement',
                'treatment': 'standard_content_frequency'
            },
            'potential_advocates': {
                'criteria': 'recent_signup AND initial_engagement AND no_bounces',
                'description': 'New subscribers showing early engagement signs',
                'treatment': 'onboarding_sequence'
            },
            'at_risk': {
                'criteria': 'declining_engagement AND no_recent_activity AND some_bounces',
                'description': 'Previously engaged subscribers showing disengagement',
                'treatment': 'reengagement_campaign'
            },
            'hibernating': {
                'criteria': 'no_recent_engagement AND long_inactivity AND minimal_bounces',
                'description': 'Inactive but potentially recoverable subscribers',
                'treatment': 'win_back_sequence'
            },
            'cleanup_candidates': {
                'criteria': 'high_bounces OR spam_complaints OR long_inactivity',
                'description': 'Contacts requiring removal or intensive cleanup',
                'treatment': 'suppression_or_removal'
            }
        }
        
        # Apply segmentation logic
        for segment_name, segment_config in segment_definitions.items():
            segment_contacts = await self._apply_segmentation_criteria(
                segment_config['criteria']
            )
            
            segmentation_results['contact_assignments'][segment_name] = segment_contacts
            segmentation_results['segment_definitions'][segment_name] = segment_config
            
            # Calculate segment health score
            health_score = await self._calculate_segment_health(segment_contacts)
            segmentation_results['segment_health_scores'][segment_name] = health_score
        
        # Generate automation rules
        automation_rules = self._generate_segmentation_automation_rules(segment_definitions)
        segmentation_results['automation_rules'] = automation_rules
        
        return segmentation_results
    
    async def _execute_cleanup_actions(self) -> Dict[str, Any]:
        """Execute automated cleanup actions based on analysis results"""
        
        cleanup_results = {
            'actions_executed': {},
            'contacts_affected': {},
            'performance_impact': {},
            'rollback_procedures': {}
        }
        
        # Define cleanup actions
        cleanup_actions = [
            ('remove_hard_bounces', self._remove_hard_bounces),
            ('suppress_soft_bounce_repeats', self._suppress_soft_bounce_repeats),
            ('flag_spam_complaints', self._flag_spam_complaints),
            ('segment_inactive_contacts', self._segment_inactive_contacts),
            ('remove_invalid_syntax', self._remove_invalid_syntax),
            ('suppress_role_addresses', self._suppress_role_addresses),
            ('remove_disposable_domains', self._remove_disposable_domains)
        ]
        
        # Execute cleanup actions
        for action_name, action_function in cleanup_actions:
            try:
                self.logger.info(f"Executing cleanup action: {action_name}")
                
                action_result = await action_function()
                cleanup_results['actions_executed'][action_name] = {
                    'status': 'completed',
                    'result': action_result,
                    'timestamp': datetime.now()
                }
                
                # Track contacts affected
                contacts_affected = action_result.get('contacts_affected', 0)
                cleanup_results['contacts_affected'][action_name] = contacts_affected
                
            except Exception as e:
                self.logger.error(f"Cleanup action {action_name} failed: {e}")
                cleanup_results['actions_executed'][action_name] = {
                    'status': 'failed',
                    'error': str(e),
                    'timestamp': datetime.now()
                }
        
        return cleanup_results
    
    async def _remove_hard_bounces(self) -> Dict[str, Any]:
        """Remove contacts with hard bounces"""
        
        hard_bounce_contacts = await self._identify_hard_bounce_contacts()
        
        removal_results = {
            'contacts_identified': len(hard_bounce_contacts),
            'contacts_removed': 0,
            'contacts_suppressed': 0,
            'removal_details': []
        }
        
        for contact in hard_bounce_contacts:
            try:
                # Check if contact should be permanently removed or suppressed
                if await self._should_permanently_remove(contact):
                    await self._permanently_remove_contact(contact)
                    removal_results['contacts_removed'] += 1
                else:
                    await self._suppress_contact(contact, reason='hard_bounce')
                    removal_results['contacts_suppressed'] += 1
                
                removal_results['removal_details'].append({
                    'contact_id': contact['id'],
                    'email': contact['email'],
                    'action': 'removed' if await self._should_permanently_remove(contact) else 'suppressed',
                    'bounce_count': contact['bounce_count'],
                    'last_bounce_date': contact['last_bounce_date']
                })
                
            except Exception as e:
                self.logger.error(f"Failed to process hard bounce contact {contact['id']}: {e}")
        
        return removal_results
    
    def _generate_hygiene_report(self, cycle_results: Dict[str, Any]) -> Dict[str, Any]:
        """Generate comprehensive hygiene cycle report"""
        
        report = {
            'executive_summary': {},
            'performance_metrics': {},
            'health_improvements': {},
            'recommendations': {},
            'next_cycle_planning': {}
        }
        
        # Executive summary
        report['executive_summary'] = {
            'cycle_duration': (cycle_results['cycle_end'] - cycle_results['cycle_start']).total_seconds() / 3600,
            'contacts_processed': cycle_results['contacts_processed'],
            'overall_health_improvement': self._calculate_overall_health_improvement(cycle_results),
            'key_achievements': self._identify_key_achievements(cycle_results),
            'critical_issues_resolved': self._identify_resolved_issues(cycle_results)
        }
        
        # Performance metrics
        report['performance_metrics'] = {
            'verification_accuracy': self._calculate_verification_accuracy(cycle_results),
            'cleanup_efficiency': self._calculate_cleanup_efficiency(cycle_results),
            'engagement_improvement': self._calculate_engagement_improvement(cycle_results),
            'deliverability_impact': self._estimate_deliverability_impact(cycle_results)
        }
        
        # Health improvements
        report['health_improvements'] = {
            'bounce_rate_reduction': self._calculate_bounce_reduction(cycle_results),
            'engagement_score_increase': self._calculate_engagement_increase(cycle_results),
            'list_quality_improvement': self._calculate_quality_improvement(cycle_results),
            'risk_factor_mitigation': self._calculate_risk_mitigation(cycle_results)
        }
        
        # Strategic recommendations
        report['recommendations'] = self._generate_strategic_recommendations(cycle_results)
        
        # Next cycle planning
        report['next_cycle_planning'] = {
            'recommended_frequency': self._recommend_cycle_frequency(cycle_results),
            'priority_areas': self._identify_priority_areas(cycle_results),
            'automation_enhancements': self._suggest_automation_enhancements(cycle_results),
            'resource_requirements': self._estimate_resource_requirements(cycle_results)
        }
        
        return report