"""
Customer Segmentation Agent

Segments customers using behavioral data, demographics,
purchase history, and AI-driven clustering algorithms.
"""

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


class CustomerSegmentationAgent(BaseAgent):
    """
    Segment customers based on multiple criteria.

    Features:
    - Behavioral segmentation
    - Demographic segmentation
    - RFM analysis (Recency, Frequency, Monetary)
    - Predictive clustering
    - Segment profiling
    - Personalization recommendations
    """

    def __init__(self):
        super().__init__(
            name='customer-segmentation',
            description='Segment customers using AI-driven analysis',
            category='business',
            version='1.0.0',
            tags=['segmentation', 'customers', 'analytics', 'clustering', 'personalization']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Segment customers.

        Args:
            params: {
                'segmentation_method': 'behavioral|demographic|rfm|predictive|combined',
                'customer_ids': List[str],
                'num_segments': int,
                'options': {
                    'min_segment_size': int,
                    'update_profiles': bool,
                    'generate_insights': bool,
                    'export_format': 'json|csv'
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'segments': List[Dict],
                'customer_assignments': Dict,
                'insights': Dict,
                'recommendations': List[str]
            }
        """
        method = params.get('segmentation_method', 'combined')
        num_segments = params.get('num_segments', 5)
        options = params.get('options', {})

        self.logger.info(f"Segmenting customers using {method} method")

        # Mock customer segments
        segments = [
            {
                'id': 'SEG-001',
                'name': 'High-Value Champions',
                'description': 'Frequent buyers with high lifetime value',
                'size': 234,
                'percentage': 12.3,
                'avg_ltv': '$8,450',
                'avg_frequency': 8.2,
                'avg_recency_days': 12,
                'characteristics': {
                    'purchase_frequency': 'Very High',
                    'average_order_value': 'High',
                    'engagement_level': 'Very High',
                    'churn_risk': 'Very Low',
                    'product_diversity': 'High'
                },
                'demographics': {
                    'avg_age': 42,
                    'gender_split': {'male': 0.58, 'female': 0.42},
                    'locations': ['Urban', 'Suburban'],
                    'income_bracket': 'Upper Middle'
                },
                'behavioral_patterns': [
                    'Purchase every 30-45 days',
                    'High email engagement (78% open rate)',
                    'Premium product preference',
                    'Cross-category buyers',
                    'Active brand advocates'
                ],
                'recommended_actions': [
                    'VIP loyalty program enrollment',
                    'Early access to new products',
                    'Personalized recommendations',
                    'Request reviews and referrals'
                ],
                'revenue_contribution': '$1,977,300',
                'revenue_percentage': 28.4
            },
            {
                'id': 'SEG-002',
                'name': 'Loyal Regulars',
                'description': 'Consistent buyers with moderate value',
                'size': 456,
                'percentage': 24.1,
                'avg_ltv': '$3,200',
                'avg_frequency': 4.5,
                'avg_recency_days': 28,
                'characteristics': {
                    'purchase_frequency': 'High',
                    'average_order_value': 'Medium',
                    'engagement_level': 'High',
                    'churn_risk': 'Low',
                    'product_diversity': 'Medium'
                },
                'demographics': {
                    'avg_age': 38,
                    'gender_split': {'male': 0.52, 'female': 0.48},
                    'locations': ['Suburban', 'Urban'],
                    'income_bracket': 'Middle'
                },
                'behavioral_patterns': [
                    'Purchase every 60-90 days',
                    'Good email engagement (52% open rate)',
                    'Core product focus',
                    'Price-conscious but loyal'
                ],
                'recommended_actions': [
                    'Upsell campaigns',
                    'Bundle offers',
                    'Loyalty rewards',
                    'Cross-sell complementary products'
                ],
                'revenue_contribution': '$1,459,200',
                'revenue_percentage': 20.9
            },
            {
                'id': 'SEG-003',
                'name': 'New Potential',
                'description': 'Recent customers with growth potential',
                'size': 312,
                'percentage': 16.5,
                'avg_ltv': '$890',
                'avg_frequency': 1.8,
                'avg_recency_days': 45,
                'characteristics': {
                    'purchase_frequency': 'Low',
                    'average_order_value': 'Medium',
                    'engagement_level': 'Medium',
                    'churn_risk': 'Medium',
                    'product_diversity': 'Low'
                },
                'demographics': {
                    'avg_age': 32,
                    'gender_split': {'male': 0.48, 'female': 0.52},
                    'locations': ['Urban', 'Suburban', 'Rural'],
                    'income_bracket': 'Middle'
                },
                'behavioral_patterns': [
                    'Made 1-3 purchases',
                    'Moderate email engagement',
                    'Single category buyers',
                    'Still evaluating brand'
                ],
                'recommended_actions': [
                    'Onboarding email series',
                    'First-purchase incentive for 2nd buy',
                    'Educational content',
                    'Customer success outreach'
                ],
                'revenue_contribution': '$277,680',
                'revenue_percentage': 4.0
            },
            {
                'id': 'SEG-004',
                'name': 'At-Risk Customers',
                'description': 'Previously active, now declining engagement',
                'size': 289,
                'percentage': 15.3,
                'avg_ltv': '$2,100',
                'avg_frequency': 3.2,
                'avg_recency_days': 180,
                'characteristics': {
                    'purchase_frequency': 'Declining',
                    'average_order_value': 'Medium',
                    'engagement_level': 'Low',
                    'churn_risk': 'High',
                    'product_diversity': 'Medium'
                },
                'demographics': {
                    'avg_age': 45,
                    'gender_split': {'male': 0.54, 'female': 0.46},
                    'locations': ['Suburban', 'Urban'],
                    'income_bracket': 'Middle'
                },
                'behavioral_patterns': [
                    'No purchase in 6+ months',
                    'Low email engagement',
                    'Previous regular buyers',
                    'Likely trying competitors'
                ],
                'recommended_actions': [
                    'Win-back campaign',
                    'Special reactivation offer',
                    'Survey for feedback',
                    'Personalized outreach'
                ],
                'revenue_contribution': '$606,900',
                'revenue_percentage': 8.7
            },
            {
                'id': 'SEG-005',
                'name': 'Bargain Hunters',
                'description': 'Price-sensitive, promotion-driven buyers',
                'size': 601,
                'percentage': 31.8,
                'avg_ltv': '$620',
                'avg_frequency': 2.1,
                'avg_recency_days': 90,
                'characteristics': {
                    'purchase_frequency': 'Low',
                    'average_order_value': 'Low',
                    'engagement_level': 'Medium',
                    'churn_risk': 'High',
                    'product_diversity': 'Low'
                },
                'demographics': {
                    'avg_age': 29,
                    'gender_split': {'male': 0.45, 'female': 0.55},
                    'locations': ['Urban', 'Suburban'],
                    'income_bracket': 'Lower Middle'
                },
                'behavioral_patterns': [
                    'Only buy on promotion',
                    'High coupon usage',
                    'Low brand loyalty',
                    'Price comparison shoppers'
                ],
                'recommended_actions': [
                    'Value-focused messaging',
                    'Bundle deals',
                    'Loyalty program to increase frequency',
                    'Limited-time offers'
                ],
                'revenue_contribution': '$372,620',
                'revenue_percentage': 5.3
            }
        ]

        # Mock RFM analysis
        rfm_analysis = {
            'recency_segments': {
                'very_recent': {'days': '0-30', 'count': 456, 'avg_score': 5},
                'recent': {'days': '31-60', 'count': 389, 'avg_score': 4},
                'moderate': {'days': '61-90', 'count': 312, 'avg_score': 3},
                'at_risk': {'days': '91-180', 'count': 234, 'avg_score': 2},
                'lost': {'days': '180+', 'count': 289, 'avg_score': 1}
            },
            'frequency_segments': {
                'very_frequent': {'purchases': '10+', 'count': 156, 'avg_score': 5},
                'frequent': {'purchases': '6-9', 'count': 234, 'avg_score': 4},
                'moderate': {'purchases': '3-5', 'count': 445, 'avg_score': 3},
                'occasional': {'purchases': '2', 'count': 567, 'avg_score': 2},
                'rare': {'purchases': '1', 'count': 678, 'avg_score': 1}
            },
            'monetary_segments': {
                'very_high': {'value': '$5,000+', 'count': 189, 'avg_score': 5},
                'high': {'value': '$2,000-$4,999', 'count': 312, 'avg_score': 4},
                'medium': {'value': '$500-$1,999', 'count': 678, 'avg_score': 3},
                'low': {'value': '$100-$499', 'count': 534, 'avg_score': 2},
                'very_low': {'value': '<$100', 'count': 367, 'avg_score': 1}
            }
        }

        # Mock segment insights
        insights = {
            'total_customers': sum(seg['size'] for seg in segments),
            'total_revenue': '$6,964,000',
            'segments_created': len(segments),
            'top_segment_by_size': segments[4]['name'],
            'top_segment_by_revenue': segments[0]['name'],
            'revenue_concentration': {
                'top_20_percent': 0.492,  # Top 20% of customers drive 49.2% of revenue
                'top_segment': 0.284
            },
            'churn_risk_customers': 289,
            'growth_potential_customers': 312,
            'segment_overlap': {
                'champions_and_advocates': 156,
                'at_risk_high_value': 45
            },
            'seasonal_patterns': {
                'SEG-001': 'Consistent year-round',
                'SEG-002': 'Holiday peaks',
                'SEG-003': 'Growing in Q4',
                'SEG-004': 'Declining trend',
                'SEG-005': 'Promotion-driven spikes'
            }
        }

        return {
            'status': 'success',
            'segmentation_method': method,
            'segments': segments,
            'total_segments': len(segments),
            'total_customers_segmented': sum(seg['size'] for seg in segments),
            'rfm_analysis': rfm_analysis,
            'insights': insights,
            'segment_performance': {
                seg['id']: {
                    'revenue_per_customer': float(seg['avg_ltv'].replace('$', '').replace(',', '')),
                    'engagement_score': 0.85 if seg['id'] == 'SEG-001' else 0.6,
                    'retention_rate': 0.92 if seg['id'] == 'SEG-001' else 0.65
                }
                for seg in segments
            },
            'recommendations': [
                'Focus retention efforts on SEG-004 (At-Risk) - high recovery value',
                'Develop VIP program for SEG-001 (Champions) to maximize advocacy',
                'Create onboarding campaign for SEG-003 (New Potential)',
                'Test value-bundle strategy on SEG-005 (Bargain Hunters)',
                'Implement win-back automation for churned customers',
                'Cross-sell campaign for SEG-002 to increase basket size',
                'Monitor segment migration monthly for trend analysis'
            ],
            'next_steps': [
                'Assign segment-specific marketing campaigns',
                'Update CRM with segment tags',
                'Create personalized email flows per segment',
                'Set up segment performance dashboards',
                'Schedule monthly segment review',
                'A/B test messaging by segment'
            ]
        }

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

        method = params.get('segmentation_method', 'combined')
        if method not in valid_methods:
            self.logger.error(f"Invalid segmentation method: {method}")
            return False

        num_segments = params.get('num_segments', 5)
        if num_segments < 2 or num_segments > 20:
            self.logger.error(f"Invalid num_segments: {num_segments}. Must be 2-20")
            return False

        return True
