"""
Lead Scorer Agent

Scores and qualifies leads using AI-driven scoring models,
behavioral analysis, and demographic data.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class LeadScorerAgent(BaseAgent):
    """
    Scores and qualifies leads based on multiple criteria.

    Features:
    - Behavioral scoring
    - Demographic scoring
    - Firmographic analysis
    - Engagement tracking
    - Predictive analytics
    - Lead qualification
    """

    def __init__(self):
        super().__init__(
            name='lead-scorer',
            description='Score and qualify leads using AI-driven models',
            category='business',
            version='1.0.0',
            tags=['leads', 'scoring', 'qualification', 'sales', 'analytics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Score and qualify leads.

        Args:
            params: {
                'lead_ids': List[str],
                'scoring_model': 'behavioral|demographic|combined|predictive',
                'threshold': int,  # Minimum score for qualification
                'options': {
                    'recalculate_existing': bool,
                    'update_crm': bool,
                    'auto_assign': bool,
                    'send_alerts': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'scored_leads': List[Dict],
                'qualified_leads': List[Dict],
                'scoring_breakdown': Dict,
                'recommendations': List[str]
            }
        """
        lead_ids = params.get('lead_ids', [])
        scoring_model = params.get('scoring_model', 'combined')
        threshold = params.get('threshold', 70)
        options = params.get('options', {})

        self.logger.info(
            f"Scoring {len(lead_ids) if lead_ids else 'all'} leads "
            f"using {scoring_model} model"
        )

        # Mock scored leads
        scored_leads = [
            {
                'id': 'LEAD-001',
                'name': 'David Chen',
                'email': 'david.chen@enterprise.com',
                'company': 'Enterprise Solutions Inc',
                'title': 'Director of IT',
                'total_score': 92,
                'grade': 'A',
                'status': 'sales_qualified',
                'scoring_breakdown': {
                    'demographic_score': 35,
                    'firmographic_score': 28,
                    'behavioral_score': 29,
                    'engagement_score': 0
                },
                'demographic_factors': {
                    'job_title_match': 10,
                    'seniority_level': 15,
                    'department': 10
                },
                'firmographic_factors': {
                    'company_size': 10,
                    'industry_fit': 8,
                    'revenue_range': 10
                },
                'behavioral_factors': {
                    'website_visits': 7,
                    'content_downloads': 10,
                    'email_engagement': 8,
                    'webinar_attendance': 4
                },
                'recent_activities': [
                    'Downloaded enterprise pricing guide',
                    'Attended product webinar',
                    'Visited pricing page 5 times',
                    'Requested demo'
                ],
                'signals': [
                    'High intent - downloaded pricing 3 times',
                    'Active buyer - multiple touchpoints',
                    'Decision maker role',
                    'Enterprise company size'
                ],
                'recommended_action': 'Assign to senior sales rep immediately',
                'estimated_deal_size': '$150,000',
                'probability_to_close': 0.68
            },
            {
                'id': 'LEAD-002',
                'name': 'Emily Martinez',
                'email': 'emily.m@startup.io',
                'company': 'StartupXYZ',
                'title': 'Founder & CEO',
                'total_score': 78,
                'grade': 'B',
                'status': 'marketing_qualified',
                'scoring_breakdown': {
                    'demographic_score': 30,
                    'firmographic_score': 18,
                    'behavioral_score': 25,
                    'engagement_score': 5
                },
                'demographic_factors': {
                    'job_title_match': 15,
                    'seniority_level': 15,
                    'department': 0
                },
                'firmographic_factors': {
                    'company_size': 5,
                    'industry_fit': 8,
                    'revenue_range': 5
                },
                'behavioral_factors': {
                    'website_visits': 8,
                    'content_downloads': 7,
                    'email_engagement': 6,
                    'webinar_attendance': 4
                },
                'recent_activities': [
                    'Downloaded startup guide',
                    'Subscribed to newsletter',
                    'Visited features page',
                    'Watched demo video'
                ],
                'signals': [
                    'Decision maker',
                    'Multiple content engagements',
                    'Startup - lower budget',
                    'High engagement rate'
                ],
                'recommended_action': 'Nurture with startup success content',
                'estimated_deal_size': '$25,000',
                'probability_to_close': 0.45
            },
            {
                'id': 'LEAD-003',
                'name': 'Michael Brown',
                'email': 'mbrown@midsize.com',
                'company': 'MidSize Corp',
                'title': 'Senior Manager',
                'total_score': 65,
                'grade': 'C',
                'status': 'lead',
                'scoring_breakdown': {
                    'demographic_score': 25,
                    'firmographic_score': 22,
                    'behavioral_score': 18,
                    'engagement_score': 0
                },
                'demographic_factors': {
                    'job_title_match': 8,
                    'seniority_level': 10,
                    'department': 7
                },
                'firmographic_factors': {
                    'company_size': 8,
                    'industry_fit': 7,
                    'revenue_range': 7
                },
                'behavioral_factors': {
                    'website_visits': 5,
                    'content_downloads': 5,
                    'email_engagement': 6,
                    'webinar_attendance': 2
                },
                'recent_activities': [
                    'Visited homepage',
                    'Downloaded case study',
                    'Opened 2 emails'
                ],
                'signals': [
                    'Mid-level manager - may not be decision maker',
                    'Moderate engagement',
                    'Good company fit'
                ],
                'recommended_action': 'Continue nurturing with educational content',
                'estimated_deal_size': '$50,000',
                'probability_to_close': 0.28
            },
            {
                'id': 'LEAD-004',
                'name': 'Jessica Lee',
                'email': 'jlee@competitor.com',
                'company': 'Competitor Solutions',
                'title': 'Product Manager',
                'total_score': 42,
                'grade': 'D',
                'status': 'disqualified',
                'scoring_breakdown': {
                    'demographic_score': 15,
                    'firmographic_score': 10,
                    'behavioral_score': 17,
                    'engagement_score': 0
                },
                'disqualification_reason': 'Works at competitor company',
                'recommended_action': 'Remove from active leads',
                'estimated_deal_size': '$0',
                'probability_to_close': 0.0
            }
        ]

        qualified_leads = [lead for lead in scored_leads if lead['total_score'] >= threshold]

        # Mock scoring model details
        scoring_model_info = {
            'model_type': scoring_model,
            'version': '2.1.0',
            'last_trained': '2025-11-01',
            'accuracy': 0.87,
            'factors_used': 45,
            'weights': {
                'demographic': 0.35,
                'firmographic': 0.28,
                'behavioral': 0.30,
                'engagement': 0.07
            },
            'threshold_recommendations': {
                'sales_qualified': 70,
                'marketing_qualified': 50,
                'nurture': 30,
                'disqualify': 0
            }
        }

        # Mock scoring distribution
        score_distribution = {
            'grade_A': {'count': 12, 'range': '90-100', 'avg_score': 94},
            'grade_B': {'count': 28, 'range': '70-89', 'avg_score': 78},
            'grade_C': {'count': 45, 'range': '50-69', 'avg_score': 58},
            'grade_D': {'count': 34, 'range': '0-49', 'avg_score': 32}
        }

        # Mock conversion metrics
        conversion_metrics = {
            'grade_A_to_opportunity': 0.72,
            'grade_B_to_opportunity': 0.48,
            'grade_C_to_opportunity': 0.21,
            'grade_D_to_opportunity': 0.05,
            'avg_time_to_opportunity_days': {
                'grade_A': 12,
                'grade_B': 28,
                'grade_C': 45,
                'grade_D': 90
            }
        }

        return {
            'status': 'success',
            'scoring_model': scoring_model_info,
            'scored_leads': scored_leads,
            'total_leads_scored': len(scored_leads),
            'qualified_leads': qualified_leads,
            'total_qualified': len(qualified_leads),
            'qualification_rate': len(qualified_leads) / len(scored_leads) if scored_leads else 0,
            'score_distribution': score_distribution,
            'conversion_metrics': conversion_metrics,
            'average_score': sum(lead['total_score'] for lead in scored_leads) / len(scored_leads) if scored_leads else 0,
            'grade_counts': {
                'A': len([l for l in scored_leads if l.get('grade') == 'A']),
                'B': len([l for l in scored_leads if l.get('grade') == 'B']),
                'C': len([l for l in scored_leads if l.get('grade') == 'C']),
                'D': len([l for l in scored_leads if l.get('grade') == 'D'])
            },
            'high_priority_leads': [
                lead for lead in scored_leads
                if lead['total_score'] >= 85
            ],
            'auto_assigned_leads': len(qualified_leads) if options.get('auto_assign') else 0,
            'alerts_sent': [
                'High-value lead LEAD-001 requires immediate attention',
                '12 Grade A leads need sales follow-up within 24 hours'
            ] if options.get('send_alerts') else [],
            'recommendations': [
                'Focus immediate sales effort on 12 Grade A leads',
                'Set up automated nurture campaign for Grade C leads',
                'Review and update scoring model - 87% accuracy',
                'Disqualify 34 low-scoring leads to clean pipeline',
                'LEAD-001 shows buying signals - prioritize for demo',
                'Consider lowering qualification threshold to 65 for more MQLs',
                'Grade B leads need mid-funnel content engagement'
            ],
            'next_steps': [
                'Assign Grade A leads to senior sales reps',
                'Move qualified leads to sales pipeline',
                'Update CRM with new scores and grades',
                'Set up automated workflows based on scores',
                'Schedule weekly scoring model review',
                'Track conversion rates by grade'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate lead scoring parameters."""
        valid_models = ['behavioral', 'demographic', 'combined', 'predictive']

        scoring_model = params.get('scoring_model', 'combined')
        if scoring_model not in valid_models:
            self.logger.error(f"Invalid scoring model: {scoring_model}")
            return False

        threshold = params.get('threshold')
        if threshold and (threshold < 0 or threshold > 100):
            self.logger.error(f"Invalid threshold: {threshold}. Must be 0-100")
            return False

        return True
