# AI-Powered Outreach Automation System
import asyncio
import json
from datetime import datetime
from typing import Dict, List, Optional
from dataclasses import dataclass
import openai

@dataclass
class Prospect:
    name: str
    company: str
    title: str
    email: str
    industry: str
    pain_points: List[str]
    recent_activity: str
    intent_signals: List[str]

class OutreachPersonalizationEngine:
    def __init__(self, api_key: str):
        self.client = openai.Client(api_key=api_key)
        self.templates = {
            'cold_outreach': """Hi {name},

I noticed {company} is {trigger_event}. Companies in {industry} typically struggle with {pain_point} at this stage.

{similar_company} faced the same challenge and {specific_result}.

Worth a quick conversation to share what worked for them?

{sender_name}
P.S. I put together a 2-minute video showing exactly how they did it: {video_link}""",
            
            'follow_up': """Hi {name},

I know you're busy with {company_activity}.

{similar_company} initially had the same concerns about {likely_objection}. They started small with {starting_point} and saw {metric} within {timeframe}.

If it's not a priority right now, when would be a better time to revisit?

{sender_name}""",
            
            'objection_response': """I appreciate your honesty about {objection}, {name}.

Two thoughts:
1. {similar_company} started with our pilot program at {lower_price} and expanded after seeing {roi_result}
2. The real question: what's the cost of NOT solving {stated_problem}?

{customer_name} calculated they were losing {amount} monthly from {specific_issue}.

Would it help to run a quick ROI calculation with your numbers?"""
        }
    
    async def research_prospect(self, prospect: Prospect) -> Dict:
        """Research prospect using AI and public data"""
        research_prompt = f"""
        Research this sales prospect and provide personalization data:
        
        Prospect: {prospect.name} at {prospect.company}
        Title: {prospect.title}
        Industry: {prospect.industry}
        Recent Activity: {prospect.recent_activity}
        Intent Signals: {prospect.intent_signals}
        
        Provide:
        1. Most likely pain point for their role/company
        2. Relevant trigger event or observation
        3. Similar company for social proof
        4. Specific result/metric to reference
        5. Best value proposition angle
        
        Return as JSON.
        """
        
        response = await self.client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a top sales researcher who identifies compelling personalization angles."},
                {"role": "user", "content": research_prompt}
            ],
            temperature=0.7
        )
        
        return json.loads(response.choices[0].message.content)
    
    async def generate_personalized_message(self, prospect: Prospect, message_type: str) -> str:
        """Generate personalized message using AI"""
        
        # Research prospect
        research_data = await self.research_prospect(prospect)
        
        # Get template
        template = self.templates.get(message_type, self.templates['cold_outreach'])
        
        # Generate personalized variables
        variables = {
            'name': prospect.name.split()[0],
            'company': prospect.company,
            'trigger_event': research_data.get('trigger_event', 'scaling rapidly'),
            'industry': prospect.industry,
            'pain_point': research_data.get('pain_point', 'sales inefficiency'),
            'similar_company': research_data.get('similar_company', 'Acme Corp'),
            'specific_result': research_data.get('specific_result', 'increased sales 40%'),
            'sender_name': 'Alex Johnson',
            'video_link': f"https://video.example.com/{prospect.company.lower()}"
        }
        
        # Format message
        personalized_message = template.format(**variables)
        
        return personalized_message
    
    async def execute_outreach_sequence(self, prospects: List[Prospect]) -> List[Dict]:
        """Execute personalized outreach for multiple prospects"""
        
        results = []
        
        for prospect in prospects:
            try:
                # Generate personalized message
                message = await self.generate_personalized_message(prospect, 'cold_outreach')
                
                # Simulate sending (replace with actual email API)
                sent_result = await self.send_email(prospect, message)
                
                results.append({
                    'prospect': prospect,
                    'message': message,
                    'sent': sent_result['success'],
                    'timestamp': datetime.now()
                })
                
                # Rate limiting
                await asyncio.sleep(2)
                
            except Exception as e:
                results.append({
                    'prospect': prospect,
                    'error': str(e),
                    'sent': False
                })
        
        return results
    
    async def send_email(self, prospect: Prospect, message: str) -> Dict:
        """Send email via your email service API"""
        # Replace with actual email service integration
        # (SendGrid, Mailgun, etc.)
        return {
            'success': True,
            'message_id': f"msg_{datetime.now().timestamp()}"
        }
    
    def analyze_campaign_performance(self, results: List[Dict]) -> Dict:
        """Analyze campaign performance metrics"""
        
        total_sent = sum(1 for r in results if r.get('sent', False))
        
        return {
            'total_prospects': len(results),
            'messages_sent': total_sent,
            'send_rate': total_sent / len(results) if results else 0,
            'estimated_open_rate': 0.45,  # Track from email service
            'estimated_reply_rate': 0.31,  # Track from email service
            'estimated_meeting_rate': 0.12  # Track from CRM
        }

# Usage Example
async def main():
    engine = OutreachPersonalizationEngine("your-openai-api-key")
    
    # Example prospects
    prospects = [
        Prospect(
            name="Sarah Johnson",
            company="TechStartup Inc",
            title="VP of Sales",
            email="sarah@techstartup.com",
            industry="SaaS",
            pain_points=["long sales cycles", "poor lead quality"],
            recent_activity="hired 3 new SDRs",
            intent_signals=["visited pricing page", "downloaded ROI calculator"]
        ),
        Prospect(
            name="Mike Chen",
            company="GrowthCo",
            title="Chief Revenue Officer",
            email="mike@growthco.com",
            industry="FinTech",
            pain_points=["scaling challenges", "pipeline visibility"],
            recent_activity="raised Series B funding",
            intent_signals=["researching CRM solutions", "attending sales webinars"]
        )
    ]
    
    # Execute outreach campaign
    results = await engine.execute_outreach_sequence(prospects)
    
    # Analyze performance
    performance = engine.analyze_campaign_performance(results)
    
    print(f"Campaign Results:")
    print(f"Messages sent: {performance['messages_sent']}")
    print(f"Expected replies: {performance['estimated_reply_rate']:.1%}")
    print(f"Expected meetings: {performance['estimated_meeting_rate']:.1%}")
    
    # Show example message
    if results:
        print(f"\nExample Message:")
        print(results[0]['message'])

if __name__ == "__main__":
    asyncio.run(main())