"""
Productivity-Enhancing MCP Services
Real-world services that increase sales automation efficiency
"""
import asyncio
from typing import Dict, List, Optional, Any
from datetime import datetime, timedelta
import re
import logging

logger = logging.getLogger(__name__)


class MCPAnalyticsService:
    """
    Analytics Service - Track metrics, conversions, and performance
    Real-world use case: Monitor pipeline health and ROI
    """

    def __init__(self):
        self.metrics = {
            "pipeline_runs": 0,
            "prospects_discovered": 0,
            "contacts_found": 0,
            "emails_generated": 0,
            "emails_sent": 0,
            "replies_received": 0,
            "meetings_booked": 0,
            "conversion_rate": 0.0,
            "average_response_time": 0.0,
            "top_performing_sequences": [],
            "daily_stats": {}
        }
        self.events = []
        logger.info("MCP Analytics Service initialized")

    async def track_event(self, event_type: str, data: Dict) -> str:
        """Track an event for analytics"""
        event = {
            "type": event_type,
            "data": data,
            "timestamp": datetime.utcnow().isoformat()
        }
        self.events.append(event)

        # Update aggregated metrics
        today = datetime.utcnow().strftime('%Y-%m-%d')

        if today not in self.metrics["daily_stats"]:
            self.metrics["daily_stats"][today] = {
                "pipeline_runs": 0,
                "prospects": 0,
                "contacts": 0,
                "emails": 0
            }

        if event_type == "pipeline_run":
            self.metrics["pipeline_runs"] += 1
            self.metrics["daily_stats"][today]["pipeline_runs"] += 1

        elif event_type == "prospect_discovered":
            self.metrics["prospects_discovered"] += 1
            self.metrics["daily_stats"][today]["prospects"] += 1

        elif event_type == "contact_found":
            self.metrics["contacts_found"] += 1
            self.metrics["daily_stats"][today]["contacts"] += 1

        elif event_type == "email_generated":
            self.metrics["emails_generated"] += 1
            self.metrics["daily_stats"][today]["emails"] += 1

        elif event_type == "email_sent":
            self.metrics["emails_sent"] += 1

        elif event_type == "reply_received":
            self.metrics["replies_received"] += 1

        elif event_type == "meeting_booked":
            self.metrics["meetings_booked"] += 1

        # Calculate conversion rate
        if self.metrics["emails_sent"] > 0:
            self.metrics["conversion_rate"] = (
                self.metrics["meetings_booked"] / self.metrics["emails_sent"]
            ) * 100

        logger.info(f"Analytics event tracked: {event_type}")
        return "tracked"

    async def get_metrics(self) -> Dict:
        """Get current metrics"""
        return self.metrics

    async def get_dashboard_data(self) -> Dict:
        """Get formatted dashboard data"""
        return {
            "summary": {
                "Total Pipeline Runs": self.metrics["pipeline_runs"],
                "Prospects Discovered": self.metrics["prospects_discovered"],
                "Contacts Found": self.metrics["contacts_found"],
                "Emails Generated": self.metrics["emails_generated"],
                "Emails Sent": self.metrics["emails_sent"],
                "Replies Received": self.metrics["replies_received"],
                "Meetings Booked": self.metrics["meetings_booked"],
                "Conversion Rate": f"{self.metrics['conversion_rate']:.2f}%"
            },
            "daily_stats": self.metrics["daily_stats"],
            "recent_events": self.events[-10:]  # Last 10 events
        }


class MCPEnrichmentService:
    """
    Enrichment Service - Enrich prospect and contact data
    Real-world use case: Add company info, social profiles, tech stack
    """

    def __init__(self):
        # Mock enrichment database
        self.enrichment_db = {
            "shopify.com": {
                "employee_count": "10,000+",
                "founded_year": 2006,
                "funding": "$2.9B",
                "tech_stack": ["Ruby on Rails", "React", "MySQL", "Redis"],
                "social_profiles": {
                    "linkedin": "https://linkedin.com/company/shopify",
                    "twitter": "https://twitter.com/shopify"
                },
                "industry_tags": ["E-commerce", "SaaS", "Retail Tech"],
                "revenue_range": "$1B - $5B"
            },
            "stripe.com": {
                "employee_count": "8,000+",
                "founded_year": 2010,
                "funding": "$2.2B",
                "tech_stack": ["Ruby", "Scala", "Go", "React"],
                "social_profiles": {
                    "linkedin": "https://linkedin.com/company/stripe",
                    "twitter": "https://twitter.com/stripe"
                },
                "industry_tags": ["Fintech", "Payments", "SaaS"],
                "revenue_range": "$5B+"
            }
        }
        logger.info("MCP Enrichment Service initialized")

    async def enrich_company(self, domain: str) -> Dict:
        """Enrich company data with additional information"""
        logger.info(f"Enriching company data for: {domain}")

        # Check if we have enrichment data
        enriched_data = self.enrichment_db.get(domain, {})

        if not enriched_data:
            # Generate estimated data based on domain
            enriched_data = {
                "employee_count": "Unknown",
                "founded_year": None,
                "funding": "Unknown",
                "tech_stack": [],
                "social_profiles": {
                    "linkedin": f"https://linkedin.com/company/{domain.split('.')[0]}",
                    "twitter": f"https://twitter.com/{domain.split('.')[0]}"
                },
                "industry_tags": [],
                "revenue_range": "Unknown",
                "enrichment_source": "estimated"
            }
        else:
            enriched_data["enrichment_source"] = "database"

        return enriched_data

    async def enrich_contact(self, email: str, name: str) -> Dict:
        """Enrich contact data with social profiles and background"""
        logger.info(f"Enriching contact data for: {email}")

        # Extract info from email
        domain = email.split('@')[1] if '@' in email else ''
        username = email.split('@')[0] if '@' in email else ''

        return {
            "email": email,
            "name": name,
            "linkedin_profile": f"https://linkedin.com/in/{username.replace('.', '-')}",
            "twitter_profile": f"https://twitter.com/{username.replace('.', '_')}",
            "github_profile": f"https://github.com/{username.replace('.', '')}",
            "estimated_seniority": self._estimate_seniority(email, name),
            "enrichment_timestamp": datetime.utcnow().isoformat()
        }

    def _estimate_seniority(self, email: str, name: str) -> str:
        """Estimate seniority based on email patterns"""
        email_lower = email.lower()
        if any(x in email_lower for x in ['ceo', 'founder', 'chief']):
            return "Executive"
        elif any(x in email_lower for x in ['vp', 'director', 'head']):
            return "Senior"
        elif any(x in email_lower for x in ['manager', 'lead']):
            return "Mid-Level"
        else:
            return "Individual Contributor"


class MCPValidationService:
    """
    Validation Service - Validate emails, domains, and contact information
    Real-world use case: Reduce bounce rates and improve deliverability
    """

    def __init__(self):
        # Common disposable email domains
        self.disposable_domains = [
            "tempmail.com", "throwaway.email", "guerrillamail.com",
            "10minutemail.com", "mailinator.com"
        ]

        # Known invalid patterns
        self.invalid_patterns = [
            "noreply@", "no-reply@", "donotreply@",
            "info@", "admin@", "support@", "sales@"
        ]

        self.validation_cache = {}
        logger.info("MCP Validation Service initialized")

    async def validate_email(self, email: str) -> Dict:
        """Validate email address"""
        logger.info(f"Validating email: {email}")

        result = {
            "email": email,
            "is_valid": False,
            "is_disposable": False,
            "is_role_based": False,
            "is_catchall": False,
            "deliverability_score": 0,
            "validation_issues": [],
            "validated_at": datetime.utcnow().isoformat()
        }

        # Basic format validation
        email_regex = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        if not re.match(email_regex, email):
            result["validation_issues"].append("Invalid email format")
            return result

        # Extract domain
        domain = email.split('@')[1] if '@' in email else ''

        # Check if disposable
        if domain in self.disposable_domains:
            result["is_disposable"] = True
            result["validation_issues"].append("Disposable email domain")

        # Check if role-based
        for pattern in self.invalid_patterns:
            if email.lower().startswith(pattern):
                result["is_role_based"] = True
                result["validation_issues"].append("Role-based email (low engagement)")
                break

        # Calculate deliverability score
        score = 100
        if result["is_disposable"]:
            score -= 50
        if result["is_role_based"]:
            score -= 30
        if len(result["validation_issues"]) == 0:
            result["is_valid"] = True

        result["deliverability_score"] = max(0, score)

        return result

    async def validate_domain(self, domain: str) -> Dict:
        """Validate domain"""
        logger.info(f"Validating domain: {domain}")

        result = {
            "domain": domain,
            "is_valid": False,
            "has_mx_records": False,
            "is_active": False,
            "validation_issues": [],
            "validated_at": datetime.utcnow().isoformat()
        }

        # Basic domain format validation
        domain_regex = r'^([a-zA-Z0-9]([a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,}$'
        if not re.match(domain_regex, domain):
            result["validation_issues"].append("Invalid domain format")
            return result

        # For demo purposes, assume most domains are valid
        # In production, would do DNS lookups
        result["is_valid"] = True
        result["has_mx_records"] = True
        result["is_active"] = True

        return result

    async def batch_validate_emails(self, emails: List[str]) -> List[Dict]:
        """Batch validate multiple emails"""
        logger.info(f"Batch validating {len(emails)} emails")

        results = []
        for email in emails:
            validation = await self.validate_email(email)
            results.append(validation)

        return results


class MCPSummaryService:
    """
    Summary Service - Generate AI-powered summaries for companies and prospects
    Real-world use case: Create comprehensive, informative summaries for sales teams

    ENHANCED: Now uses LLM service with strict grounding to prevent hallucination
    """

    def __init__(self):
        from services.llm_service import get_llm_service
        self.llm = get_llm_service()
        logger.info("MCP Summary Service initialized with LLM grounding support")

    async def generate_company_summary(self, company_data: Dict, enrichment_data: Dict = None) -> str:
        """
        ENHANCED: Generate comprehensive AI summary for a prospect company
        Now uses LLM service with strict grounding

        Args:
            company_data: Basic company information (INCLUDING raw_facts if available)
            enrichment_data: Optional enriched data from enrichment service

        Returns:
            Detailed summary string grounded in facts
        """
        logger.info(f"Generating GROUNDED summary for company: {company_data.get('name', 'Unknown')}")

        name = company_data.get('name', 'Unknown Company')

        # Merge enrichment data if available
        if enrichment_data:
            company_data = {**company_data, **enrichment_data}

        # Get raw facts for grounding
        raw_facts = company_data.get('raw_facts', [])

        # Use LLM service for grounded summarization
        summary = await self.llm.generate_grounded_summary(
            company_name=name,
            extracted_data=company_data,
            raw_facts=raw_facts,
            summary_type="prospect"
        )

        return summary

    async def generate_prospect_summary(
        self,
        prospect_data: Dict,
        company_enrichment: Dict = None,
        contact_data: List[Dict] = None
    ) -> str:
        """
        Generate comprehensive AI summary for a sales prospect

        Args:
            prospect_data: Basic prospect information with company data
            company_enrichment: Enriched company data
            contact_data: List of contacts found

        Returns:
            Detailed prospect summary
        """
        logger.info("Generating prospect summary")

        company = prospect_data.get('company', {})
        company_name = company.get('name', 'Unknown Company')
        domain = company.get('domain', '')
        industry = company.get('industry', 'Unknown')

        # Start with company summary
        company_summary = await self.generate_company_summary(company, company_enrichment)

        # Add prospect-specific insights
        fit_score = prospect_data.get('fit_score', 0.0)
        status = prospect_data.get('status', 'new')

        # Contacts analysis
        contact_summary = ""
        if contact_data:
            contact_count = len(contact_data)
            contact_summary = f" We have identified {contact_count} key contact{'s' if contact_count > 1 else ''}"

            # Identify decision makers
            decision_makers = [c for c in contact_data if any(
                title_word in c.get('title', '').lower()
                for title_word in ['ceo', 'cto', 'cfo', 'vp', 'director', 'head', 'chief']
            )]

            if decision_makers:
                contact_summary += f", including {len(decision_makers)} decision-maker{'s' if len(decision_makers) > 1 else ''}"

            contact_summary += "."

        # Fit assessment
        fit_assessment = ""
        if fit_score > 0:
            if fit_score >= 0.8:
                fit_assessment = " **High Priority:** This prospect shows excellent fit based on company size, industry, and technology profile."
            elif fit_score >= 0.6:
                fit_assessment = " **Good Fit:** This prospect demonstrates strong alignment with our ideal customer profile."
            elif fit_score >= 0.4:
                fit_assessment = " **Moderate Fit:** This prospect shows potential but may require additional qualification."
            else:
                fit_assessment = " **Low Priority:** This prospect shows limited fit with our target criteria."

        # Combine all sections
        full_summary = company_summary + contact_summary + fit_assessment

        return full_summary

    async def generate_client_summary(
        self,
        client_data: Dict,
        enrichment_data: Dict = None
    ) -> str:
        """
        ENHANCED: Generate comprehensive AI summary for CLIENT company (the company we're selling FOR)
        Now uses LLM service with strict grounding to prevent hallucination

        Args:
            client_data: Client profile data with offerings, value props, etc. (INCLUDING raw_facts)
            enrichment_data: Optional enriched data

        Returns:
            Detailed client summary grounded in extracted facts
        """
        logger.info(f"Generating GROUNDED client summary for: {client_data.get('name', 'Unknown')}")

        name = client_data.get('name', 'Unknown Company')

        # Merge enrichment data if available
        if enrichment_data:
            client_data = {**client_data, **enrichment_data}

        # Get raw facts for grounding (if available)
        raw_facts = client_data.get('raw_facts', [])

        # Use LLM service for grounded summarization
        summary = await self.llm.generate_grounded_summary(
            company_name=name,
            extracted_data=client_data,
            raw_facts=raw_facts,
            summary_type="client"
        )

        return summary


# Singleton instances
_analytics_service: Optional[MCPAnalyticsService] = None
_enrichment_service: Optional[MCPEnrichmentService] = None
_validation_service: Optional[MCPValidationService] = None
_summary_service: Optional[MCPSummaryService] = None


def get_analytics_service() -> MCPAnalyticsService:
    """Get or create analytics service instance"""
    global _analytics_service
    if _analytics_service is None:
        _analytics_service = MCPAnalyticsService()
    return _analytics_service


def get_enrichment_service() -> MCPEnrichmentService:
    """Get or create enrichment service instance"""
    global _enrichment_service
    if _enrichment_service is None:
        _enrichment_service = MCPEnrichmentService()
    return _enrichment_service


def get_validation_service() -> MCPValidationService:
    """Get or create validation service instance"""
    global _validation_service
    if _validation_service is None:
        _validation_service = MCPValidationService()
    return _validation_service


def get_summary_service() -> MCPSummaryService:
    """Get or create summary service instance"""
    global _summary_service
    if _summary_service is None:
        _summary_service = MCPSummaryService()
    return _summary_service