# Cold email prospect research automation framework
import requests
import json
import time
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import pandas as pd
import re
from urllib.parse import urlparse, urljoin
import asyncio
import aiohttp
from functools import wraps

class ProspectSource(Enum):
    LINKEDIN = "linkedin"
    COMPANY_WEBSITE = "company_website"
    INDUSTRY_REPORTS = "industry_reports"
    NEWS_MENTIONS = "news_mentions"
    SOCIAL_MEDIA = "social_media"
    PROFESSIONAL_NETWORKS = "professional_networks"

class ProspectPriority(Enum):
    HIGH = "high"      # Decision makers with urgent needs
    MEDIUM = "medium"  # Influencers with moderate fit
    LOW = "low"        # General prospects for nurturing

@dataclass
class ProspectProfile:
    prospect_id: str
    first_name: str
    last_name: str
    email: str
    company: str
    title: str
    priority: ProspectPriority
    industry: str
    company_size: Optional[str] = None
    location: Optional[str] = None
    linkedin_url: Optional[str] = None
    company_website: Optional[str] = None
    recent_company_news: List[str] = field(default_factory=list)
    pain_points: List[str] = field(default_factory=list)
    interests: List[str] = field(default_factory=list)
    mutual_connections: List[str] = field(default_factory=list)
    technology_stack: List[str] = field(default_factory=list)
    recent_activities: List[Dict[str, Any]] = field(default_factory=list)
    engagement_history: List[Dict[str, Any]] = field(default_factory=list)
    personalization_data: Dict[str, Any] = field(default_factory=dict)
    last_updated: datetime = field(default_factory=datetime.now)

@dataclass
class CompanyIntelligence:
    company_name: str
    industry: str
    company_size: str
    annual_revenue: Optional[str] = None
    headquarters: Optional[str] = None
    recent_funding: Optional[Dict[str, Any]] = None
    key_technologies: List[str] = field(default_factory=list)
    recent_news: List[Dict[str, str]] = field(default_factory=list)
    growth_indicators: List[str] = field(default_factory=list)
    competitive_landscape: List[str] = field(default_factory=list)
    decision_making_process: Dict[str, Any] = field(default_factory=dict)
    pain_point_indicators: List[str] = field(default_factory=list)

class ProspectResearchEngine:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.logger = logging.getLogger(__name__)
        
        # Research data sources
        self.data_sources = {}
        self.enrichment_apis = {}
        self.social_monitors = {}
        
        # Research cache for efficiency
        self.research_cache = {}
        self.company_cache = {}
        
        # Initialize research components
        self._initialize_research_sources()
        
    def _initialize_research_sources(self):
        """Initialize external data sources for prospect research"""
        
        # Configure API clients (example integrations)
        self.data_sources = {
            'linkedin_api': self._configure_linkedin_api(),
            'clearbit_api': self._configure_clearbit_api(),
            'hunter_io_api': self._configure_hunter_api(),
            'builtwith_api': self._configure_builtwith_api(),
            'news_api': self._configure_news_api()
        }
        
        self.logger.info("Prospect research sources initialized")
    
    def _configure_linkedin_api(self):
        """Configure LinkedIn API client"""
        # In production, this would be a real LinkedIn API client
        return {
            'api_key': self.config.get('linkedin_api_key'),
            'base_url': 'https://api.linkedin.com/v2/',
            'rate_limit': 100  # requests per hour
        }
    
    def _configure_clearbit_api(self):
        """Configure Clearbit API for company enrichment"""
        return {
            'api_key': self.config.get('clearbit_api_key'),
            'base_url': 'https://person.clearbit.com/v2/',
            'rate_limit': 600  # requests per hour
        }
    
    def _configure_hunter_api(self):
        """Configure Hunter.io API for email finding"""
        return {
            'api_key': self.config.get('hunter_api_key'),
            'base_url': 'https://api.hunter.io/v2/',
            'rate_limit': 100  # requests per hour
        }
    
    def _configure_builtwith_api(self):
        """Configure BuiltWith API for technology intelligence"""
        return {
            'api_key': self.config.get('builtwith_api_key'),
            'base_url': 'https://api.builtwith.com/v1/',
            'rate_limit': 200  # requests per hour
        }
    
    def _configure_news_api(self):
        """Configure News API for company intelligence"""
        return {
            'api_key': self.config.get('news_api_key'),
            'base_url': 'https://newsapi.org/v2/',
            'rate_limit': 500  # requests per hour
        }

    async def research_prospect_comprehensive(self, basic_info: Dict[str, str]) -> ProspectProfile:
        """Conduct comprehensive prospect research using multiple data sources"""
        
        prospect_id = f"prospect_{int(time.time())}"
        
        # Initialize prospect profile with basic information
        prospect = ProspectProfile(
            prospect_id=prospect_id,
            first_name=basic_info.get('first_name', ''),
            last_name=basic_info.get('last_name', ''),
            email=basic_info.get('email', ''),
            company=basic_info.get('company', ''),
            title=basic_info.get('title', ''),
            priority=ProspectPriority.MEDIUM,
            industry=basic_info.get('industry', '')
        )
        
        # Parallel research across multiple sources
        research_tasks = [
            self._research_professional_background(prospect),
            self._research_company_intelligence(prospect.company),
            self._research_social_presence(prospect),
            self._research_technology_stack(prospect.company),
            self._research_recent_activities(prospect),
            self._identify_pain_points(prospect)
        ]
        
        research_results = await asyncio.gather(*research_tasks, return_exceptions=True)
        
        # Process research results
        for i, result in enumerate(research_results):
            if not isinstance(result, Exception):
                await self._integrate_research_data(prospect, research_tasks[i].__name__, result)
            else:
                self.logger.warning(f"Research task failed: {research_tasks[i].__name__} - {result}")
        
        # Calculate prospect priority based on research
        prospect.priority = await self._calculate_prospect_priority(prospect)
        
        # Generate personalization data
        prospect.personalization_data = await self._generate_personalization_data(prospect)
        
        # Cache research results
        self.research_cache[prospect_id] = prospect
        
        return prospect
    
    async def _research_professional_background(self, prospect: ProspectProfile) -> Dict[str, Any]:
        """Research prospect's professional background and experience"""
        
        try:
            # LinkedIn profile research (simulated)
            linkedin_data = await self._fetch_linkedin_profile(
                f"{prospect.first_name} {prospect.last_name}",
                prospect.company
            )
            
            # Professional network analysis
            network_data = await self._analyze_professional_network(prospect)
            
            return {
                'linkedin_profile': linkedin_data,
                'professional_network': network_data,
                'career_progression': linkedin_data.get('career_progression', []),
                'education': linkedin_data.get('education', []),
                'certifications': linkedin_data.get('certifications', []),
                'publications': linkedin_data.get('publications', [])
            }
            
        except Exception as e:
            self.logger.error(f"Professional background research failed: {e}")
            return {}
    
    async def _research_company_intelligence(self, company_name: str) -> CompanyIntelligence:
        """Research comprehensive company intelligence"""
        
        # Check cache first
        cache_key = f"company_{company_name.lower().replace(' ', '_')}"
        if cache_key in self.company_cache:
            return self.company_cache[cache_key]
        
        try:
            # Company profile research
            company_data = await self._fetch_company_profile(company_name)
            
            # Recent news and developments
            news_data = await self._fetch_company_news(company_name)
            
            # Technology stack analysis
            tech_stack = await self._analyze_company_technology(company_name)
            
            # Funding and growth indicators
            funding_data = await self._research_company_funding(company_name)
            
            company_intelligence = CompanyIntelligence(
                company_name=company_name,
                industry=company_data.get('industry', ''),
                company_size=company_data.get('size', ''),
                annual_revenue=company_data.get('revenue'),
                headquarters=company_data.get('headquarters'),
                recent_funding=funding_data,
                key_technologies=tech_stack.get('technologies', []),
                recent_news=news_data.get('articles', []),
                growth_indicators=company_data.get('growth_indicators', []),
                competitive_landscape=company_data.get('competitors', [])
            )
            
            # Cache company intelligence
            self.company_cache[cache_key] = company_intelligence
            
            return company_intelligence
            
        except Exception as e:
            self.logger.error(f"Company intelligence research failed: {e}")
            return CompanyIntelligence(
                company_name=company_name,
                industry='Unknown',
                company_size='Unknown'
            )
    
    async def _research_social_presence(self, prospect: ProspectProfile) -> Dict[str, Any]:
        """Research prospect's social media presence and activity"""
        
        try:
            social_data = {
                'linkedin_activity': await self._analyze_linkedin_activity(prospect),
                'twitter_activity': await self._analyze_twitter_activity(prospect),
                'professional_content': await self._find_professional_content(prospect),
                'speaking_engagements': await self._find_speaking_engagements(prospect),
                'thought_leadership': await self._analyze_thought_leadership(prospect)
            }
            
            return social_data
            
        except Exception as e:
            self.logger.error(f"Social presence research failed: {e}")
            return {}
    
    async def _research_technology_stack(self, company_name: str) -> Dict[str, Any]:
        """Research company's technology stack and infrastructure"""
        
        try:
            # Website technology analysis
            tech_analysis = await self._analyze_website_technology(company_name)
            
            # Job posting analysis for technology insights
            job_tech_insights = await self._analyze_job_postings_technology(company_name)
            
            return {
                'website_technologies': tech_analysis.get('technologies', []),
                'infrastructure': tech_analysis.get('infrastructure', {}),
                'hiring_technologies': job_tech_insights,
                'integration_opportunities': await self._identify_integration_opportunities(tech_analysis)
            }
            
        except Exception as e:
            self.logger.error(f"Technology stack research failed: {e}")
            return {}
    
    async def _research_recent_activities(self, prospect: ProspectProfile) -> List[Dict[str, Any]]:
        """Research prospect's recent professional activities"""
        
        try:
            activities = []
            
            # Recent LinkedIn posts and interactions
            linkedin_activity = await self._fetch_recent_linkedin_activity(prospect)
            activities.extend(linkedin_activity)
            
            # Recent company news mentions
            news_mentions = await self._find_prospect_news_mentions(prospect)
            activities.extend(news_mentions)
            
            # Recent conference or event participation
            event_participation = await self._find_recent_events(prospect)
            activities.extend(event_participation)
            
            # Sort by recency
            activities.sort(key=lambda x: x.get('date', ''), reverse=True)
            
            return activities[:10]  # Return top 10 most recent activities
            
        except Exception as e:
            self.logger.error(f"Recent activities research failed: {e}")
            return []
    
    async def _identify_pain_points(self, prospect: ProspectProfile) -> List[str]:
        """Identify potential pain points based on company and role analysis"""
        
        try:
            pain_points = []
            
            # Industry-specific pain points
            industry_pain_points = await self._get_industry_pain_points(prospect.industry)
            pain_points.extend(industry_pain_points)
            
            # Role-specific challenges
            role_challenges = await self._get_role_specific_challenges(prospect.title)
            pain_points.extend(role_challenges)
            
            # Company size-specific issues
            if prospect.company_size:
                size_challenges = await self._get_company_size_challenges(prospect.company_size)
                pain_points.extend(size_challenges)
            
            # Technology-related pain points
            tech_pain_points = await self._identify_technology_pain_points(prospect.company)
            pain_points.extend(tech_pain_points)
            
            # Remove duplicates and prioritize
            unique_pain_points = list(set(pain_points))
            prioritized_pain_points = await self._prioritize_pain_points(
                unique_pain_points, prospect
            )
            
            return prioritized_pain_points[:5]  # Return top 5 pain points
            
        except Exception as e:
            self.logger.error(f"Pain point identification failed: {e}")
            return []
    
    async def _calculate_prospect_priority(self, prospect: ProspectProfile) -> ProspectPriority:
        """Calculate prospect priority based on research data"""
        
        priority_score = 0
        
        # Title-based scoring
        title_scores = {
            'ceo': 10, 'president': 10, 'founder': 10,
            'vp': 8, 'vice president': 8, 'director': 7,
            'manager': 5, 'senior': 6, 'lead': 5
        }
        
        for title_keyword, score in title_scores.items():
            if title_keyword in prospect.title.lower():
                priority_score += score
                break
        
        # Company size scoring
        size_scores = {
            'enterprise': 8, 'large': 6, 'medium': 5, 'small': 3, 'startup': 4
        }
        
        if prospect.company_size:
            for size_keyword, score in size_scores.items():
                if size_keyword in prospect.company_size.lower():
                    priority_score += score
                    break
        
        # Recent activity scoring
        if len(prospect.recent_activities) > 3:
            priority_score += 3
        
        # Pain point relevance scoring
        if len(prospect.pain_points) > 2:
            priority_score += 4
        
        # Technology fit scoring
        if len(prospect.technology_stack) > 0:
            priority_score += 2
        
        # Determine priority level
        if priority_score >= 15:
            return ProspectPriority.HIGH
        elif priority_score >= 8:
            return ProspectPriority.MEDIUM
        else:
            return ProspectPriority.LOW
    
    async def _generate_personalization_data(self, prospect: ProspectProfile) -> Dict[str, Any]:
        """Generate personalization data for outreach messages"""
        
        personalization = {
            'greeting_style': self._determine_greeting_style(prospect),
            'value_proposition_angle': self._select_value_proposition_angle(prospect),
            'pain_point_hooks': self._generate_pain_point_hooks(prospect.pain_points),
            'credibility_builders': self._identify_credibility_builders(prospect),
            'call_to_action_style': self._determine_cta_style(prospect),
            'follow_up_sequence': self._design_follow_up_sequence(prospect),
            'personalization_tokens': self._extract_personalization_tokens(prospect)
        }
        
        return personalization
    
    def _determine_greeting_style(self, prospect: ProspectProfile) -> str:
        """Determine appropriate greeting style based on prospect profile"""
        
        if prospect.priority == ProspectPriority.HIGH:
            return 'formal_executive'
        elif 'startup' in prospect.company.lower() or prospect.title.lower() in ['founder', 'ceo']:
            return 'entrepreneurial_casual'
        else:
            return 'professional_friendly'
    
    def _select_value_proposition_angle(self, prospect: ProspectProfile) -> str:
        """Select the most relevant value proposition angle"""
        
        # Role-based value proposition mapping
        role_value_props = {
            'ceo': 'business_growth',
            'cfo': 'cost_efficiency',
            'cto': 'technical_innovation',
            'vp sales': 'revenue_acceleration',
            'marketing': 'lead_generation',
            'operations': 'process_optimization'
        }
        
        for role_keyword, value_prop in role_value_props.items():
            if role_keyword in prospect.title.lower():
                return value_prop
        
        return 'general_efficiency'
    
    # Mock implementations for external API calls
    async def _fetch_linkedin_profile(self, name: str, company: str) -> Dict[str, Any]:
        """Mock LinkedIn profile fetch"""
        await asyncio.sleep(0.1)  # Simulate API call
        return {
            'profile_url': f'https://linkedin.com/in/{name.lower().replace(" ", "-")}',
            'career_progression': ['Current Role', 'Previous Role'],
            'education': ['University Name'],
            'certifications': ['Professional Certification'],
            'publications': []
        }
    
    async def _fetch_company_profile(self, company_name: str) -> Dict[str, Any]:
        """Mock company profile fetch"""
        await asyncio.sleep(0.1)  # Simulate API call
        return {
            'industry': 'Technology',
            'size': '100-500 employees',
            'revenue': '$10M-$50M',
            'headquarters': 'San Francisco, CA',
            'growth_indicators': ['Recent hiring', 'New product launch'],
            'competitors': ['Competitor A', 'Competitor B']
        }
    
    async def _fetch_company_news(self, company_name: str) -> Dict[str, Any]:
        """Mock company news fetch"""
        await asyncio.sleep(0.1)  # Simulate API call
        return {
            'articles': [
                {'title': 'Company announces new funding', 'date': '2025-11-15'},
                {'title': 'Company launches new product', 'date': '2025-11-10'}
            ]
        }
    
    def batch_research_prospects(self, prospect_list: List[Dict[str, str]]) -> List[ProspectProfile]:
        """Batch research multiple prospects efficiently"""
        
        async def process_batch():
            tasks = [
                self.research_prospect_comprehensive(prospect_data)
                for prospect_data in prospect_list
            ]
            return await asyncio.gather(*tasks, return_exceptions=True)
        
        results = asyncio.run(process_batch())
        
        # Filter out exceptions and return successful research results
        successful_results = [
            result for result in results
            if not isinstance(result, Exception)
        ]
        
        return successful_results

# Usage demonstration
async def demonstrate_prospect_research():
    """Demonstrate comprehensive prospect research"""
    
    config = {
        'linkedin_api_key': 'your_linkedin_key',
        'clearbit_api_key': 'your_clearbit_key',
        'hunter_api_key': 'your_hunter_key',
        'builtwith_api_key': 'your_builtwith_key',
        'news_api_key': 'your_news_key'
    }
    
    research_engine = ProspectResearchEngine(config)
    
    print("=== Cold Email Prospect Research Demo ===")
    
    # Sample prospect data
    prospect_data = {
        'first_name': 'John',
        'last_name': 'Smith',
        'email': 'john.smith@techcorp.com',
        'company': 'TechCorp Solutions',
        'title': 'VP of Engineering',
        'industry': 'Software'
    }
    
    # Research prospect comprehensively
    print(f"Researching prospect: {prospect_data['first_name']} {prospect_data['last_name']}")
    prospect_profile = await research_engine.research_prospect_comprehensive(prospect_data)
    
    print(f"\nResearch Results:")
    print(f"  Priority: {prospect_profile.priority.value}")
    print(f"  Pain Points: {len(prospect_profile.pain_points)} identified")
    print(f"  Recent Activities: {len(prospect_profile.recent_activities)} found")
    print(f"  Technology Stack: {len(prospect_profile.technology_stack)} technologies")
    
    print(f"\nPersonalization Data:")
    for key, value in prospect_profile.personalization_data.items():
        print(f"  {key}: {value}")
    
    return research_engine

if __name__ == "__main__":
    result = asyncio.run(demonstrate_prospect_research())
    print("Prospect research system ready!")

# Cold email deliverability optimization
class ColdEmailDeliverabilityManager:
    def __init__(self, config):
        self.config = config
        self.sending_domains = []
        self.ip_reputation = {}
        self.daily_limits = {}
        
    async def optimize_sending_infrastructure(self):
        """Optimize sending infrastructure for cold email deliverability"""
        
        # Domain warm-up strategy
        await self.implement_domain_warmup()
        
        # IP reputation monitoring
        await self.monitor_ip_reputation()
        
        # Sending volume optimization
        await self.optimize_sending_volumes()
        
        # Template deliverability testing
        await self.test_template_deliverability()
    
    async def implement_domain_warmup(self):
        """Implement gradual domain warm-up process"""
        
        warmup_schedule = {
            'week_1': {'daily_limit': 25, 'recipient_types': 'internal_team'},
            'week_2': {'daily_limit': 50, 'recipient_types': 'warm_contacts'},
            'week_3': {'daily_limit': 100, 'recipient_types': 'opted_in_prospects'},
            'week_4': {'daily_limit': 200, 'recipient_types': 'cold_prospects_tier1'},
            'week_5': {'daily_limit': 300, 'recipient_types': 'cold_prospects_full'}
        }
        
        return warmup_schedule
    
    async def validate_email_before_sending(self, email_address):
        """Validate email deliverability before sending"""
        
        # Syntax validation
        if not self.is_valid_email_syntax(email_address):
            return False
        
        # Domain validation
        if not await self.validate_domain_deliverability(email_address):
            return False
        
        # Mailbox validation
        if not await self.validate_mailbox_exists(email_address):
            return False
        
        return True