"""
Funnel Analyzer Agent

Analyzes conversion funnels to identify drop-off points,
conversion rates, and optimization opportunities.
"""

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


class FunnelAnalyzerAgent(BaseAgent):
    """
    Analyzes conversion funnels.

    Supports:
    - Multi-step funnel analysis
    - Drop-off identification
    - Conversion rate optimization
    - Segment-based analysis
    - Time-based funnel analysis
    - A/B test funnel comparison
    """

    def __init__(self):
        super().__init__(
            name='funnel-analyzer',
            description='Analyze conversion funnels and identify optimization opportunities',
            category='data',
            version='1.0.0',
            tags=['funnel-analysis', 'conversion', 'optimization', 'analytics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze a conversion funnel.

        Args:
            params: {
                'funnel_name': str,
                'steps': [
                    {
                        'name': str,
                        'event': str,
                        'filters': Dict[str, Any]
                    }
                ],
                'data_source': str,
                'time_period': {
                    'start_date': str,
                    'end_date': str
                },
                'options': {
                    'segment_by': List[str],
                    'compare_periods': bool,
                    'include_time_analysis': bool,
                    'detect_bottlenecks': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'funnel_name': str,
                'total_steps': int,
                'overall_conversion_rate': float,
                'step_analysis': List[Dict[str, Any]],
                'bottlenecks': List[Dict[str, Any]],
                'insights': List[str],
                'recommendations': List[str],
                'execution_time_seconds': float
            }
        """
        funnel_name = params.get('funnel_name', 'Conversion Funnel')
        steps = params.get('steps', [])
        data_source = params.get('data_source')
        time_period = params.get('time_period', {})
        options = params.get('options', {})

        self.logger.info(
            f"Analyzing funnel '{funnel_name}' with {len(steps)} steps"
        )

        # Mock funnel analysis
        step_analysis = self._analyze_funnel_steps(steps)

        return {
            'status': 'success',
            'funnel_name': funnel_name,
            'data_source': data_source,
            'total_steps': len(step_analysis),
            'overall_conversion_rate': 4.2,
            'total_users_entered': 50000,
            'total_users_completed': 2100,
            'execution_time_seconds': 4.5,
            'time_period': {
                'start_date': time_period.get('start_date', '2025-11-01'),
                'end_date': time_period.get('end_date', '2025-11-16'),
                'duration_days': 16
            },
            'step_analysis': step_analysis,
            'conversion_summary': {
                'best_step_conversion': 0.85,
                'worst_step_conversion': 0.42,
                'average_step_conversion': 0.68,
                'median_time_to_convert': 320  # seconds
            },
            'bottlenecks': [
                {
                    'step': 'Add to Cart',
                    'position': 2,
                    'users_entered': 32500,
                    'users_exited': 18850,
                    'drop_off_rate': 0.58,
                    'severity': 'high',
                    'estimated_impact': 'Fixing could increase overall conversion by 15%'
                },
                {
                    'step': 'Checkout',
                    'position': 3,
                    'users_entered': 13650,
                    'users_exited': 8190,
                    'drop_off_rate': 0.40,
                    'severity': 'medium',
                    'estimated_impact': 'Fixing could increase overall conversion by 8%'
                }
            ] if options.get('detect_bottlenecks') else [],
            'segment_analysis': {
                'mobile': {
                    'conversion_rate': 3.2,
                    'total_users': 30000
                },
                'desktop': {
                    'conversion_rate': 5.8,
                    'total_users': 20000
                }
            } if options.get('segment_by') else {},
            'time_analysis': {
                'average_time_in_funnel': 480,  # seconds
                'median_time_in_funnel': 320,
                'fastest_conversion': 45,
                'slowest_conversion': 1800,
                'drop_off_by_time': {
                    'under_1_min': 0.15,
                    '1_5_min': 0.35,
                    '5_30_min': 0.28,
                    'over_30_min': 0.22
                }
            } if options.get('include_time_analysis') else {},
            'period_comparison': {
                'current_period_conversion': 4.2,
                'previous_period_conversion': 3.8,
                'change_percentage': 10.5,
                'trend': 'improving'
            } if options.get('compare_periods') else {},
            'visualizations': [
                'funnel_visualization.png',
                'drop_off_analysis.png',
                'time_to_convert.png',
                'segment_comparison.png'
            ],
            'insights': [
                'Overall conversion rate of 4.2% is above industry average',
                'Major drop-off at "Add to Cart" step (58% exit)',
                'Desktop users convert 80% better than mobile users',
                'Conversion rate improving 10.5% vs previous period',
                'Users who complete funnel in under 5 minutes convert at 3x rate'
            ],
            'recommendations': [
                'Optimize "Add to Cart" experience to reduce 58% drop-off',
                'Improve mobile checkout flow (3.2% vs 5.8% desktop)',
                'Add exit intent popups at high drop-off points',
                'Simplify checkout process to reduce friction',
                'A/B test one-click checkout option',
                'Implement cart abandonment email sequence'
            ],
            'opportunity_analysis': {
                'potential_additional_conversions': 7500,
                'estimated_revenue_impact': 375000,
                'quick_wins': [
                    'Reduce form fields in checkout',
                    'Add trust badges',
                    'Optimize mobile experience'
                ]
            }
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate funnel analysis parameters."""
        if 'steps' not in params or len(params['steps']) < 2:
            self.logger.error("At least 2 funnel steps are required")
            return False

        if 'data_source' not in params:
            self.logger.error("Missing required field: data_source")
            return False

        return True

    def _analyze_funnel_steps(
        self,
        steps: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """Analyze each funnel step."""
        step_analysis = []
        users = 50000

        default_steps = [
            {'name': 'Landing Page', 'conversion': 0.65},
            {'name': 'Add to Cart', 'conversion': 0.42},
            {'name': 'Checkout', 'conversion': 0.60},
            {'name': 'Payment', 'conversion': 0.75},
            {'name': 'Confirmation', 'conversion': 0.95}
        ]

        steps_to_use = steps if steps else default_steps

        for i, step in enumerate(steps_to_use[:5]):
            step_name = step.get('name', default_steps[i]['name'])
            conversion_rate = step.get('conversion', default_steps[i]['conversion'])

            users_completed = int(users * conversion_rate)
            drop_off = users - users_completed

            step_analysis.append({
                'step_number': i + 1,
                'step_name': step_name,
                'users_entered': users,
                'users_completed': users_completed,
                'users_dropped': drop_off,
                'conversion_rate': round(conversion_rate * 100, 1),
                'drop_off_rate': round((1 - conversion_rate) * 100, 1),
                'cumulative_conversion_rate': round((users_completed / 50000) * 100, 1)
            })

            users = users_completed

        return step_analysis
