Best Lemlist 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 HyperPersonalizedContact:
    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
    twitter_handle: Optional[str] = None
    email_validation: Optional[Dict] = None
    phone_validation: Optional[Dict] = None
    social_intelligence: Optional[Dict] = None
    personalization_variables: Optional[Dict] = None
    engagement_score: int = 0
    deliverability_score: int = 0
    best_contact_method: str = "email"
    personalization_depth: str = "basic"
    campaign_priority: str = "medium"
    enhancement_timestamp: str = ""

class Lemlist1lookupIntegration:
    def __init__(self, lemlist_api_key: str, lookup_api_key: str):
        self.lemlist_api_key = lemlist_api_key
        self.lookup_api_key = lookup_api_key
        self.lemlist_base_url = "https://api.lemlist.com/api"
        self.lookup_base_url = "https://api.1lookup.app/v1"
    
    async def create_hyper_personalized_campaign(self, campaign_name: str, 
                                               contacts: List[Dict],
                                               template_id: str = None) -> Dict:
        """
        Create Lemlist campaign with hyper-personalized contacts
        """
        try:
            # Step 1: Enhance all contacts with comprehensive intelligence
            enhanced_contacts = await self._bulk_enhance_for_personalization(contacts)
            
            # Step 2: Generate personalization variables for each contact
            for contact in enhanced_contacts:
                contact.personalization_variables = await self._generate_personalization_variables(contact)
            
            # Step 3: Create optimized Lemlist campaign
            campaign_data = await self._create_lemlist_campaign(campaign_name, template_id)
            
            if not campaign_data:
                return {"error": "Failed to create Lemlist campaign"}
            
            # Step 4: Add enhanced contacts to campaign
            await self._add_enhanced_contacts_to_campaign(campaign_data['_id'], enhanced_contacts)
            
            # Step 5: Apply personalization optimizations
            optimization_results = await self._optimize_campaign_personalization(
                campaign_data['_id'], enhanced_contacts
            )
            
            return {
                "campaign_id": campaign_data['_id'],
                "campaign_name": campaign_name,
                "total_contacts": len(enhanced_contacts),
                "personalization_stats": self._calculate_personalization_stats(enhanced_contacts),
                "optimization_results": optimization_results
            }
            
        except Exception as e:
            return {"error": f"Campaign creation failed: {e}"}
    
    async def optimize_existing_campaign(self, campaign_id: str) -> Dict:
        """
        Optimize existing Lemlist campaign with enhanced personalization
        """
        try:
            # Get campaign contacts from Lemlist
            campaign_contacts = await self._get_lemlist_campaign_contacts(campaign_id)
            
            if not campaign_contacts:
                return {"error": "No contacts found in campaign"}
            
            # Enhance contacts with comprehensive intelligence
            enhanced_contacts = await self._bulk_enhance_for_personalization(campaign_contacts)
            
            # Update campaign with enhanced data
            update_results = await self._update_campaign_with_enhancements(campaign_id, enhanced_contacts)
            
            return {
                "campaign_id": campaign_id,
                "contacts_enhanced": len(enhanced_contacts),
                "personalization_improvement": self._calculate_personalization_improvement(enhanced_contacts),
                "update_results": update_results
            }
            
        except Exception as e:
            return {"error": f"Campaign optimization failed: {e}"}
    
    async def _bulk_enhance_for_personalization(self, contacts: List[Dict]) -> List[HyperPersonalizedContact]:
        """Enhance multiple contacts for maximum personalization"""
        enhancement_tasks = []
        
        for contact_data in contacts:
            personalized_contact = HyperPersonalizedContact(
                email=contact_data.get('email', ''),
                first_name=contact_data.get('firstName') or contact_data.get('first_name'),
                last_name=contact_data.get('lastName') or contact_data.get('last_name'),
                position=contact_data.get('position'),
                company=contact_data.get('companyName') or contact_data.get('company'),
                domain=contact_data.get('companyDomain') or contact_data.get('domain'),
                linkedin_url=contact_data.get('linkedinUrl'),
                enhancement_timestamp=datetime.now().isoformat()
            )
            
            task = self._enhance_single_contact_for_personalization(personalized_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_personalization(self, contact: HyperPersonalizedContact) -> None:
        """Comprehensive single contact enhancement for personalization"""
        enhancement_tasks = []
        
        # Email validation with deliverability scoring
        if contact.email:
            task1 = self._validate_email_for_personalization(contact.email)
            enhancement_tasks.append(task1)
        
        # Phone enrichment for multi-channel campaigns
        if contact.first_name and contact.last_name:
            task2 = self._enrich_phone_for_multichannel(contact)
            enhancement_tasks.append(task2)
        
        # Social intelligence gathering
        task3 = self._gather_social_intelligence(contact)
        enhancement_tasks.append(task3)
        
        # Execute all enhancements
        results = await asyncio.gather(*enhancement_tasks, return_exceptions=True)
        
        # Process 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 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('phone_number')
        
        # Process social intelligence
        if len(results) > 2 and not isinstance(results[2], Exception):
            contact.social_intelligence = results[2]
            if results[2] and results[2].get('twitter'):
                contact.twitter_handle = results[2]['twitter'].get('username')
        
        # Calculate engagement and personalization scores
        contact.engagement_score = self._calculate_engagement_score(contact)
        contact.best_contact_method = self._determine_best_contact_method(contact)
        contact.personalization_depth = self._assess_personalization_depth(contact)
        contact.campaign_priority = self._determine_campaign_priority(contact)
    
    async def _validate_email_for_personalization(self, email: str) -> Dict:
        """Advanced email validation for personalization campaigns"""
        url = f"{self.lookup_base_url}/email/validate"
        headers = {
            'Authorization': f'Bearer {self.lookup_api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'email': email,
            'personalization_check': True,
            'engagement_prediction': True,
            'deliverability_optimization': True,
            'reputation_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 comprehensive deliverability score
                    score = 0
                    if result.get('deliverable'): score += 30
                    if result.get('valid_format'): score += 20
                    if not result.get('spam_trap'): score += 20
                    if result.get('reputation') == 'excellent': score += 30
                    elif result.get('reputation') == 'good': score += 20
                    
                    result['deliverability_score'] = score
                    return result
                return {'deliverable': False, 'deliverability_score': 0}
    
    async def _enrich_phone_for_multichannel(self, contact: HyperPersonalizedContact) -> Optional[Dict]:
        """Phone enrichment for multi-channel personalization"""
        url = f"{self.lookup_base_url}/enrich/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,
            'linkedin_url': contact.linkedin_url
        }
        
        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
    
    async def _gather_social_intelligence(self, contact: HyperPersonalizedContact) -> Optional[Dict]:
        """Gather comprehensive social intelligence for personalization"""
        url = f"{self.lookup_base_url}/enrich/social"
        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,
            'email': contact.email,
            'linkedin_url': contact.linkedin_url,
            'deep_analysis': 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
    
    async def _generate_personalization_variables(self, contact: HyperPersonalizedContact) -> Dict:
        """Generate comprehensive personalization variables for Lemlist"""
        variables = {}
        
        # Basic contact variables
        variables['verified_email'] = contact.email if contact.email_validation and contact.email_validation.get('deliverable') else 'not_verified'
        variables['verified_phone'] = contact.phone if contact.phone_validation and contact.phone_validation.get('valid') else 'not_available'
        
        # Engagement prediction
        variables['engagement_likelihood'] = self._get_engagement_likelihood(contact.engagement_score)
        variables['best_contact_time'] = self._predict_best_contact_time(contact)
        variables['preferred_contact_method'] = contact.best_contact_method
        
        # Social intelligence variables
        if contact.social_intelligence:
            variables['linkedin_activity'] = contact.social_intelligence.get('linkedin', {}).get('recent_activity', 'no_data')
            variables['twitter_activity'] = contact.social_intelligence.get('twitter', {}).get('recent_tweets', 'not_active')
            variables['social_engagement_level'] = self._assess_social_engagement(contact.social_intelligence)
        
        # Company intelligence
        if contact.company:
            variables['company_size'] = await self._get_company_size(contact.domain)
            variables['industry_insights'] = await self._get_industry_insights(contact.company)
        
        # Personalization depth indicators
        variables['personalization_score'] = contact.engagement_score
        variables['data_completeness'] = self._calculate_data_completeness(contact)
        variables['campaign_priority'] = contact.campaign_priority
        
        return variables
    
    def _calculate_engagement_score(self, contact: HyperPersonalizedContact) -> int:
        """Calculate comprehensive engagement prediction score"""
        score = 0
        
        # Email deliverability (0-30 points)
        if contact.deliverability_score:
            score += int(contact.deliverability_score * 0.3)
        
        # Contact completeness (0-25 points)
        completeness = 0
        if contact.email: completeness += 8
        if contact.phone: completeness += 8
        if contact.linkedin_url: completeness += 4
        if contact.twitter_handle: completeness += 3
        if contact.position: completeness += 2
        score += completeness
        
        # Social presence (0-20 points)
        if contact.social_intelligence:
            social_score = len([v for v in contact.social_intelligence.values() if v]) * 4
            score += min(social_score, 20)
        
        # Position influence (0-15 points)
        if contact.position:
            position_lower = contact.position.lower()
            if any(title in position_lower for title in ['ceo', 'founder', 'president']):
                score += 15
            elif any(title in position_lower for title in ['vp', 'director', 'head', 'chief']):
                score += 12
            elif any(title in position_lower for title in ['manager', 'lead', 'senior']):
                score += 8
        
        # Phone validation bonus (0-10 points)
        if contact.phone_validation and contact.phone_validation.get('valid'):
            score += 10
        
        return min(score, 100)
    
    def _determine_best_contact_method(self, contact: HyperPersonalizedContact) -> str:
        """Determine optimal contact method for engagement"""
        scores = {}
        
        # Email scoring
        if contact.email_validation and contact.email_validation.get('deliverable'):
            scores['email'] = contact.deliverability_score
        
        # Phone scoring
        if contact.phone_validation and contact.phone_validation.get('valid'):
            scores['phone'] = 85  # Base phone score
        
        # LinkedIn scoring
        if contact.linkedin_url and contact.social_intelligence:
            linkedin_data = contact.social_intelligence.get('linkedin', {})
            if linkedin_data.get('active'):
                scores['linkedin'] = 75
        
        # Return best method or default to email
        return max(scores.items(), key=lambda x: x[1])[0] if scores else 'email'
    
    def _assess_personalization_depth(self, contact: HyperPersonalizedContact) -> str:
        """Assess available personalization depth"""
        data_points = 0
        
        if contact.email: data_points += 1
        if contact.phone: data_points += 1
        if contact.position: data_points += 1
        if contact.linkedin_url: data_points += 1
        if contact.twitter_handle: data_points += 1
        if contact.social_intelligence and len(contact.social_intelligence) > 0: data_points += 2
        
        if data_points >= 6:
            return "ultra_deep"
        elif data_points >= 4:
            return "deep"
        elif data_points >= 2:
            return "medium"
        else:
            return "basic"
    
    def _determine_campaign_priority(self, contact: HyperPersonalizedContact) -> str:
        """Determine campaign priority based on engagement potential"""
        if contact.engagement_score >= 80:
            return "high"
        elif contact.engagement_score >= 60:
            return "medium"
        else:
            return "low"

# Example usage for hyper-personalized campaigns
async def main():
    integration = Lemlist1lookupIntegration(
        lemlist_api_key="your_lemlist_api_key",
        lookup_api_key="your_1lookup_api_key"
    )
    
    # Sample contacts for hyper-personalization
    sample_contacts = [
        {
            'email': 'john@example.com',
            'firstName': 'John',
            'lastName': 'Smith',
            'position': 'Marketing Director',
            'companyName': 'Example Corp',
            'companyDomain': 'example.com'
        },
        # Add more contacts...
    ]
    
    # Create hyper-personalized campaign
    campaign_result = await integration.create_hyper_personalized_campaign(
        campaign_name="Ultra-Personalized Outreach Q1",
        contacts=sample_contacts,
        template_id="your_template_id"
    )
    
    print("Hyper-Personalized Campaign Results:")
    print(f"Campaign ID: {campaign_result.get('campaign_id')}")
    print(f"Total Contacts: {campaign_result.get('total_contacts')}")
    
    personalization_stats = campaign_result.get('personalization_stats', {})
    print(f"
Personalization Statistics:")
    print(f"Ultra-Deep Personalization: {personalization_stats.get('ultra_deep_count', 0)} contacts")
    print(f"Average Engagement Score: {personalization_stats.get('average_engagement_score', 0):.1f}")
    print(f"Multi-Channel Ready: {personalization_stats.get('multichannel_ready', 0)} contacts")

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