Best Woodpecker.co Phone Validation API & Email Verification Integration 2025

import requests
import json
import asyncio
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime

@dataclass
class B2BProfessionalContact:
    email: str
    first_name: Optional[str] = None
    last_name: Optional[str] = None
    position: Optional[str] = None
    company: Optional[str] = None
    domain: Optional[str] = None
    phone: Optional[str] = None
    linkedin_url: Optional[str] = None
    industry: Optional[str] = None
    company_size: Optional[str] = None
    email_validation: Optional[Dict] = None
    phone_validation: Optional[Dict] = None
    professional_intelligence: Optional[Dict] = None
    b2b_score: int = 0
    deliverability_score: int = 0
    professional_level: str = "unknown"
    campaign_priority: str = "medium"
    woodpecker_readiness: str = "pending"
    enhancement_timestamp: str = ""

class Woodpecker1lookupIntegration:
    def __init__(self, woodpecker_api_key: str, lookup_api_key: str):
        self.woodpecker_api_key = woodpecker_api_key
        self.lookup_api_key = lookup_api_key
        self.woodpecker_base_url = "https://api.woodpecker.co/rest/v1"
        self.lookup_base_url = "https://api.1lookup.app/v1"
    
    async def optimize_b2b_campaign(self, campaign_id: str) -> Dict:
        """
        Optimize existing Woodpecker B2B campaign with professional intelligence
        """
        try:
            # Step 1: Get campaign prospects from Woodpecker
            campaign_prospects = await self._get_woodpecker_prospects(campaign_id)
            
            if not campaign_prospects:
                return {"error": "No prospects found in campaign"}
            
            # Step 2: Enhance all prospects with B2B intelligence
            enhanced_prospects = await self._bulk_enhance_for_b2b(campaign_prospects)
            
            # Step 3: Optimize B2B sequences based on professional intelligence
            sequence_optimization = await self._optimize_b2b_sequences(campaign_id, enhanced_prospects)
            
            # Step 4: Update Woodpecker campaign with enhanced data
            update_results = await self._update_woodpecker_campaign(campaign_id, enhanced_prospects)
            
            return {
                "campaign_id": campaign_id,
                "total_prospects": len(campaign_prospects),
                "enhanced_prospects": len(enhanced_prospects),
                "b2b_optimization_stats": self._calculate_b2b_stats(enhanced_prospects),
                "sequence_optimization": sequence_optimization,
                "update_results": update_results
            }
            
        except Exception as e:
            return {"error": f"B2B campaign optimization failed: {e}"}
    
    async def create_optimized_b2b_campaign(self, campaign_name: str, 
                                          prospects: List[Dict],
                                          template_id: str = None) -> Dict:
        """
        Create new Woodpecker B2B campaign with professional optimization
        """
        try:
            # Step 1: Enhance all prospects with comprehensive B2B intelligence
            enhanced_prospects = await self._bulk_enhance_for_b2b(prospects)
            
            # Step 2: Generate optimal B2B sequences based on professional levels
            optimal_sequences = await self._generate_b2b_sequences(enhanced_prospects, template_id)
            
            # Step 3: Create Woodpecker campaign with B2B optimization
            campaign_data = await self._create_woodpecker_campaign(campaign_name, optimal_sequences)
            
            if not campaign_data:
                return {"error": "Failed to create Woodpecker campaign"}
            
            # Step 4: Add enhanced prospects with professional prioritization
            await self._add_b2b_prospects_to_campaign(campaign_data['id'], enhanced_prospects)
            
            return {
                "campaign_id": campaign_data['id'],
                "campaign_name": campaign_name,
                "total_prospects": len(enhanced_prospects),
                "b2b_optimization": self._analyze_b2b_optimization(enhanced_prospects),
                "professional_segmentation": self._analyze_professional_segments(enhanced_prospects)
            }
            
        except Exception as e:
            return {"error": f"B2B campaign creation failed: {e}"}
    
    async def _bulk_enhance_for_b2b(self, prospects: List[Dict]) -> List[B2BProfessionalContact]:
        """Enhance multiple prospects for optimal B2B engagement"""
        enhancement_tasks = []
        
        for prospect_data in prospects:
            b2b_contact = B2BProfessionalContact(
                email=prospect_data.get('email', ''),
                first_name=prospect_data.get('first_name') or prospect_data.get('firstName'),
                last_name=prospect_data.get('last_name') or prospect_data.get('lastName'),
                position=prospect_data.get('position') or prospect_data.get('title'),
                company=prospect_data.get('company') or prospect_data.get('company_name'),
                domain=prospect_data.get('domain') or prospect_data.get('company_domain'),
                linkedin_url=prospect_data.get('linkedin_url'),
                industry=prospect_data.get('industry'),
                company_size=prospect_data.get('company_size'),
                enhancement_timestamp=datetime.now().isoformat()
            )
            
            task = self._enhance_single_contact_for_b2b(b2b_contact)
            enhancement_tasks.append(task)
        
        # Process all enhancements in parallel
        await asyncio.gather(*enhancement_tasks, return_exceptions=True)
        
        return [task.result() for task in enhancement_tasks if not isinstance(task.result(), Exception)]
    
    async def _enhance_single_contact_for_b2b(self, contact: B2BProfessionalContact) -> None:
        """Comprehensive single contact enhancement for B2B campaigns"""
        enhancement_tasks = []
        
        # Professional email validation
        if contact.email:
            task1 = self._validate_professional_email(contact.email)
            enhancement_tasks.append(task1)
        
        # Business phone enrichment
        if contact.first_name and contact.last_name and contact.company:
            task2 = self._enrich_business_phone(contact)
            enhancement_tasks.append(task2)
        
        # Professional intelligence gathering
        task3 = self._gather_professional_intelligence(contact)
        enhancement_tasks.append(task3)
        
        # Execute all enhancements
        results = await asyncio.gather(*enhancement_tasks, return_exceptions=True)
        
        # Process professional email validation
        if len(results) > 0 and not isinstance(results[0], Exception):
            contact.email_validation = results[0]
            contact.deliverability_score = results[0].get('deliverability_score', 0)
        
        # Process business phone enrichment
        if len(results) > 1 and not isinstance(results[1], Exception) and results[1]:
            contact.phone_validation = results[1]
            contact.phone = results[1].get('business_phone')
        
        # Process professional intelligence
        if len(results) > 2 and not isinstance(results[2], Exception):
            contact.professional_intelligence = results[2]
            if results[2]:
                contact.industry = results[2].get('industry') or contact.industry
                contact.company_size = results[2].get('company_size') or contact.company_size
        
        # Calculate B2B scores and classifications
        contact.b2b_score = self._calculate_b2b_professional_score(contact)
        contact.professional_level = self._assess_professional_level(contact)
        contact.campaign_priority = self._determine_b2b_priority(contact)
        contact.woodpecker_readiness = self._assess_woodpecker_readiness(contact)
    
    async def _validate_professional_email(self, email: str) -> Dict:
        """Advanced professional email validation for B2B campaigns"""
        url = f"{self.lookup_base_url}/email/validate"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'email': email,
            'professional_validation': True,
            'b2b_deliverability': True,
            'corporate_email_check': True,
            'business_domain_analysis': True
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.post(url, json=payload, headers=headers) as response:
                if response.status == 200:
                    result = await response.json()
                    
                    # Calculate professional deliverability score
                    score = 0
                    if result.get('deliverable'): score += 35
                    if result.get('corporate_email'): score += 25
                    if result.get('valid_format'): score += 20
                    if not result.get('spam_trap'): score += 20
                    
                    result['deliverability_score'] = score
                    result['professional_quality'] = 'high' if score >= 80 else 'medium' if score >= 60 else 'low'
                    return result
                return {'deliverable': False, 'deliverability_score': 0, 'professional_quality': 'low'}
    
    async def _enrich_business_phone(self, contact: B2BProfessionalContact) -> Optional[Dict]:
        """Business phone enrichment for B2B follow-up campaigns"""
        url = f"{self.lookup_base_url}/enrich/business_phone"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'first_name': contact.first_name,
            'last_name': contact.last_name,
            'company': contact.company,
            'domain': contact.domain,
            'email': contact.email,
            'position': contact.position,
            'b2b_focus': True,
            'direct_dial_preference': True
        }
        
        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, json=payload, headers=headers) as response:
                    if response.status == 200:
                        result = await response.json()
                        
                        # Assess B2B phone quality
                        if result.get('valid'):
                            result['b2b_phone_score'] = 85 if result.get('direct_dial') else 65
                            result['follow_up_potential'] = 'high' if result.get('business_hours_reachable') else 'medium'
                        else:
                            result['b2b_phone_score'] = 0
                            result['follow_up_potential'] = 'low'
                        
                        return result
        except:
            pass
        
        return None
    
    async def _gather_professional_intelligence(self, contact: B2BProfessionalContact) -> Optional[Dict]:
        """Gather comprehensive professional intelligence for B2B targeting"""
        url = f"{self.lookup_base_url}/enrich/professional"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'first_name': contact.first_name,
            'last_name': contact.last_name,
            'company': contact.company,
            'domain': contact.domain,
            'position': contact.position,
            'email': contact.email,
            'b2b_intelligence': True,
            'decision_maker_analysis': True,
            'company_insights': True
        }
        
        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, json=payload, headers=headers) as response:
                    if response.status == 200:
                        return await response.json()
        except:
            pass
        
        return None
    
    def _calculate_b2b_professional_score(self, contact: B2BProfessionalContact) -> int:
        """Calculate comprehensive B2B professional engagement score"""
        score = 0
        
        # Professional email quality (0-35 points)
        if contact.deliverability_score:
            score += int(contact.deliverability_score * 0.35)
        
        # Professional position influence (0-30 points)
        if contact.position:
            position_lower = contact.position.lower()
            if any(title in position_lower for title in ['ceo', 'founder', 'president', 'owner']):
                score += 30
            elif any(title in position_lower for title in ['vp', 'vice president', 'director', 'head', 'chief']):
                score += 25
            elif any(title in position_lower for title in ['manager', 'lead', 'senior', 'principal']):
                score += 15
            elif any(title in position_lower for title in ['coordinator', 'specialist', 'analyst']):
                score += 10
        
        # Company size and industry relevance (0-20 points)
        if contact.company_size:
            if contact.company_size in ['large', 'enterprise']:
                score += 20
            elif contact.company_size in ['medium', 'mid-market']:
                score += 15
            elif contact.company_size in ['small', 'startup']:
                score += 10
        
        # Contact completeness (0-10 points)
        completeness = 0
        if contact.email: completeness += 3
        if contact.phone: completeness += 3
        if contact.linkedin_url: completeness += 2
        if contact.company and contact.position: completeness += 2
        score += completeness
        
        # Professional intelligence bonus (0-5 points)
        if contact.professional_intelligence:
            intel_quality = len([v for v in contact.professional_intelligence.values() if v])
            score += min(intel_quality, 5)
        
        return min(score, 100)
    
    def _assess_professional_level(self, contact: B2BProfessionalContact) -> str:
        """Assess professional level for B2B targeting"""
        if contact.position:
            position_lower = contact.position.lower()
            
            if any(title in position_lower for title in ['ceo', 'founder', 'president', 'owner']):
                return "c_level"
            elif any(title in position_lower for title in ['vp', 'vice president', 'director', 'head', 'chief']):
                return "executive"
            elif any(title in position_lower for title in ['manager', 'lead', 'senior', 'principal']):
                return "management"
            elif any(title in position_lower for title in ['coordinator', 'specialist', 'analyst']):
                return "individual_contributor"
        
        return "unknown"
    
    def _determine_b2b_priority(self, contact: B2BProfessionalContact) -> str:
        """Determine B2B campaign priority based on professional score"""
        if contact.b2b_score >= 80 and contact.professional_level in ['c_level', 'executive']:
            return "high"
        elif contact.b2b_score >= 60 and contact.professional_level in ['executive', 'management']:
            return "medium"
        else:
            return "low"
    
    def _assess_woodpecker_readiness(self, contact: B2BProfessionalContact) -> str:
        """Assess contact's readiness for Woodpecker B2B campaigns"""
        readiness_score = 0
        
        # Email quality
        if contact.email_validation and contact.email_validation.get('deliverable'):
            readiness_score += 40
        
        # Professional level
        if contact.professional_level in ['c_level', 'executive']:
            readiness_score += 30
        elif contact.professional_level == 'management':
            readiness_score += 20
        
        # Contact completeness
        if contact.company and contact.position:
            readiness_score += 20
        
        # Professional intelligence
        if contact.professional_intelligence:
            readiness_score += 10
        
        if readiness_score >= 80:
            return "excellent"
        elif readiness_score >= 60:
            return "good"
        elif readiness_score >= 40:
            return "fair"
        else:
            return "limited"
    
    async def _optimize_b2b_sequences(self, campaign_id: str, 
                                    prospects: List[B2BProfessionalContact]) -> Dict:
        """Optimize Woodpecker sequences for different professional levels"""
        optimization = {
            'c_level_prospects': [],
            'executive_prospects': [],
            'management_prospects': [],
            'individual_contributor_prospects': [],
            'sequence_recommendations': {}
        }
        
        # Segment prospects by professional level
        for prospect in prospects:
            level = prospect.professional_level
            if level == 'c_level':
                optimization['c_level_prospects'].append(prospect)
            elif level == 'executive':
                optimization['executive_prospects'].append(prospect)
            elif level == 'management':
                optimization['management_prospects'].append(prospect)
            else:
                optimization['individual_contributor_prospects'].append(prospect)
        
        # Generate sequence recommendations for each professional level
        optimization['sequence_recommendations'] = {
            'c_level': self._generate_c_level_sequence(),
            'executive': self._generate_executive_sequence(),
            'management': self._generate_management_sequence(),
            'individual_contributor': self._generate_ic_sequence()
        }
        
        return optimization
    
    def _generate_c_level_sequence(self) -> Dict:
        """Generate C-level executive sequence"""
        return {
            'sequence_type': 'executive_brief',
            'total_steps': 5,
            'intervals': [3, 7, 5, 10, 14],  # Days between steps
            'approach': 'strategic_value_focused',
            'tone': 'professional_executive',
            'personalization_level': 'high'
        }
    
    def _generate_executive_sequence(self) -> Dict:
        """Generate executive level sequence"""
        return {
            'sequence_type': 'professional_comprehensive',
            'total_steps': 6,
            'intervals': [2, 5, 4, 7, 6, 12],
            'approach': 'solution_oriented',
            'tone': 'professional_consultative',
            'personalization_level': 'high'
        }
    
    def _generate_management_sequence(self) -> Dict:
        """Generate management level sequence"""
        return {
            'sequence_type': 'management_focused',
            'total_steps': 7,
            'intervals': [1, 3, 4, 5, 6, 8, 12],
            'approach': 'practical_benefits',
            'tone': 'professional_friendly',
            'personalization_level': 'medium'
        }
    
    def _generate_ic_sequence(self) -> Dict:
        """Generate individual contributor sequence"""
        return {
            'sequence_type': 'individual_contributor',
            'total_steps': 5,
            'intervals': [1, 3, 5, 7, 10],
            'approach': 'educational_supportive',
            'tone': 'friendly_helpful',
            'personalization_level': 'medium'
        }

# Example usage for B2B optimization
async def main():
    integration = Woodpecker1lookupIntegration(
        woodpecker_api_key="your_woodpecker_api_key",
        lookup_api_key="your_1lookup_api_key"
    )
    
    # Optimize existing B2B campaign
    optimization_result = await integration.optimize_b2b_campaign("your_campaign_id")
    
    print("B2B Campaign Optimization Results:")
    print(f"Total Prospects: {optimization_result.get('total_prospects')}")
    print(f"Enhanced Prospects: {optimization_result.get('enhanced_prospects')}")
    
    b2b_stats = optimization_result.get('b2b_optimization_stats', {})
    print(f"
B2B Optimization Statistics:")
    print(f"C-Level Prospects: {b2b_stats.get('c_level_count', 0)}")
    print(f"Executive Prospects: {b2b_stats.get('executive_count', 0)}")
    print(f"Average B2B Score: {b2b_stats.get('average_b2b_score', 0):.1f}")
    print(f"Professional Quality: {b2b_stats.get('professional_quality_rate', 0):.1f}%")

# Run the integration
if __name__ == "__main__":
    import aiohttp
    asyncio.run(main())