"""
Business Intelligence Reporter Agent

Generates comprehensive BI reports with data visualization, trend analysis,
predictive insights, and executive dashboards.
"""

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


class BusinessIntelligenceReporterAgent(BaseAgent):
    """
    Generates business intelligence reports and dashboards.

    Features:
    - Data aggregation
    - Trend analysis
    - Predictive insights
    - Visual dashboards
    - KPI tracking
    - Executive summaries
    """

    def __init__(self):
        super().__init__(
            name='business-intelligence-reporter',
            description='Generate comprehensive BI reports and dashboards',
            category='business',
            version='1.0.0',
            tags=['bi', 'analytics', 'reporting', 'dashboards', 'insights']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate BI reports.

        Args:
            params: {
                'report_type': 'executive|sales|financial|operational|custom',
                'time_period': Dict,
                'metrics': List[str],
                'format': 'dashboard|pdf|excel|presentation',
                'options': {
                    'include_predictions': bool,
                    'include_comparisons': bool,
                    'include_visualizations': bool,
                    'executive_summary': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'report': Dict,
                'insights': List[Dict],
                'visualizations': List[Dict],
                'recommendations': List[str]
            }
        """
        report_type = params.get('report_type', 'executive')
        time_period = params.get('time_period', {})
        output_format = params.get('format', 'dashboard')
        options = params.get('options', {})

        self.logger.info(f"Generating {report_type} BI report")

        # Mock executive dashboard data
        executive_dashboard = {
            'report_id': 'BI-EXEC-2025-11',
            'report_type': report_type,
            'period': 'November 2025',
            'generated_date': '2025-11-16',
            'generated_by': 'BI Reporter Agent',
            'kpis': {
                'revenue': {
                    'current': 485000,
                    'previous': 434000,
                    'target': 500000,
                    'variance_actual': 0.117,
                    'variance_target': -0.030,
                    'trend': 'up',
                    'status': 'on_track',
                    'forecast_eom': 512000
                },
                'customers': {
                    'current': 847,
                    'previous': 812,
                    'target': 875,
                    'variance_actual': 0.043,
                    'variance_target': -0.032,
                    'trend': 'up',
                    'status': 'slightly_behind',
                    'new_this_month': 42,
                    'churned_this_month': 7,
                    'net_growth': 35
                },
                'mrr': {
                    'current': 145000,
                    'previous': 138000,
                    'target': 150000,
                    'variance_actual': 0.051,
                    'variance_target': -0.033,
                    'trend': 'up',
                    'status': 'on_track'
                },
                'churn_rate': {
                    'current': 0.023,
                    'previous': 0.028,
                    'target': 0.020,
                    'variance_actual': -0.179,  # Improvement
                    'variance_target': 0.150,  # Still above target
                    'trend': 'improving',
                    'status': 'needs_attention'
                },
                'cac': {
                    'current': 2850,
                    'previous': 3100,
                    'target': 2500,
                    'variance_actual': -0.081,
                    'variance_target': 0.140,
                    'trend': 'improving',
                    'status': 'needs_improvement'
                },
                'ltv': {
                    'current': 28500,
                    'previous': 27200,
                    'target': 30000,
                    'variance_actual': 0.048,
                    'variance_target': -0.050,
                    'trend': 'up',
                    'status': 'on_track'
                },
                'ltv_cac_ratio': {
                    'current': 10.0,
                    'previous': 8.8,
                    'target': 12.0,
                    'variance_actual': 0.136,
                    'variance_target': -0.167,
                    'trend': 'up',
                    'status': 'good'
                }
            }
        }

        # Mock insights
        insights = [
            {
                'id': 'INS-001',
                'category': 'revenue',
                'type': 'positive',
                'priority': 'high',
                'title': 'Revenue Growth Accelerating',
                'description': 'Revenue grew 11.7% MoM, up from 7.2% last month',
                'impact': 'On track to exceed quarterly target by 8%',
                'confidence': 0.89,
                'data_points': ['revenue_trend', 'deal_velocity', 'pipeline_coverage']
            },
            {
                'id': 'INS-002',
                'category': 'customer',
                'type': 'neutral',
                'priority': 'medium',
                'title': 'Customer Growth Steady',
                'description': 'Added 42 new customers but slightly behind target of 50',
                'impact': 'May miss quarterly customer acquisition goal by 5%',
                'confidence': 0.82,
                'recommendation': 'Increase marketing spend in December'
            },
            {
                'id': 'INS-003',
                'category': 'churn',
                'type': 'positive',
                'priority': 'high',
                'title': 'Churn Rate Improving',
                'description': 'Churn decreased from 2.8% to 2.3%, 17.9% improvement',
                'impact': 'Retention initiatives showing results',
                'confidence': 0.91,
                'recommendation': 'Continue customer success programs'
            },
            {
                'id': 'INS-004',
                'category': 'efficiency',
                'type': 'positive',
                'priority': 'medium',
                'title': 'CAC Optimization Success',
                'description': 'Customer acquisition cost down 8.1% to $2,850',
                'impact': 'Marketing efficiency improving, but still 14% above target',
                'confidence': 0.85,
                'recommendation': 'Further optimize paid channels'
            },
            {
                'id': 'INS-005',
                'category': 'sales',
                'type': 'warning',
                'priority': 'high',
                'title': 'Sales Cycle Lengthening',
                'description': 'Average sales cycle increased from 42 to 47 days',
                'impact': 'May impact Q4 revenue if not addressed',
                'confidence': 0.88,
                'recommendation': 'Review and streamline qualification process'
            }
        ]

        # Mock sales performance data
        sales_performance = {
            'total_revenue': 485000,
            'revenue_by_product': {
                'Enterprise Plan': {'revenue': 285000, 'percentage': 0.588, 'growth': 0.15},
                'Professional Plan': {'revenue': 145000, 'percentage': 0.299, 'growth': 0.09},
                'Starter Plan': {'revenue': 55000, 'percentage': 0.113, 'growth': -0.02}
            },
            'revenue_by_region': {
                'North America': {'revenue': 292000, 'percentage': 0.602, 'growth': 0.12},
                'Europe': {'revenue': 145500, 'percentage': 0.300, 'growth': 0.11},
                'Asia Pacific': {'revenue': 47500, 'percentage': 0.098, 'growth': 0.09}
            },
            'top_sales_reps': [
                {'name': 'Sarah J.', 'revenue': 125000, 'deals': 15, 'quota_attainment': 1.25},
                {'name': 'Mike C.', 'revenue': 98000, 'deals': 12, 'quota_attainment': 0.98},
                {'name': 'Emily D.', 'revenue': 87000, 'deals': 11, 'quota_attainment': 0.87}
            ],
            'pipeline': {
                'total_value': 2450000,
                'weighted_value': 1144500,
                'deals_count': 89,
                'avg_deal_size': 27528,
                'coverage_ratio': 2.45
            }
        }

        # Mock financial metrics
        financial_metrics = {
            'revenue': {
                'actual': 485000,
                'budget': 500000,
                'variance': -15000,
                'variance_pct': -0.030
            },
            'expenses': {
                'actual': 342000,
                'budget': 350000,
                'variance': -8000,
                'variance_pct': -0.023,
                'breakdown': {
                    'personnel': 215000,
                    'marketing': 68000,
                    'infrastructure': 34000,
                    'other': 25000
                }
            },
            'gross_profit': {
                'actual': 388000,
                'budget': 400000,
                'margin': 0.800,
                'target_margin': 0.800
            },
            'operating_profit': {
                'actual': 143000,
                'budget': 150000,
                'margin': 0.295,
                'target_margin': 0.300
            },
            'cash_balance': 2450000,
            'burn_rate_monthly': -45000,
            'runway_months': 54.4
        }

        # Mock operational metrics
        operational_metrics = {
            'support': {
                'ticket_volume': 234,
                'avg_response_time_hours': 3.2,
                'avg_resolution_time_hours': 14.5,
                'csat_score': 4.3,
                'first_contact_resolution': 0.64
            },
            'product': {
                'active_users': 15234,
                'dau_mau_ratio': 0.42,
                'feature_adoption_rate': 0.67,
                'uptime_percentage': 99.94,
                'avg_page_load_time_ms': 1250
            },
            'marketing': {
                'website_visitors': 45678,
                'leads_generated': 892,
                'conversion_rate': 0.0195,
                'mql_to_sql_rate': 0.34,
                'cost_per_lead': 125
            }
        }

        # Mock trend analysis
        trends = {
            'revenue_trend': {
                'direction': 'upward',
                'strength': 'strong',
                'seasonality': 'Q4_peak',
                'prediction_next_month': 534000,
                'confidence': 0.87
            },
            'customer_growth_trend': {
                'direction': 'upward',
                'strength': 'moderate',
                'cagr_12m': 0.145,
                'prediction_eoy': 923
            },
            'churn_trend': {
                'direction': 'downward',
                'strength': 'moderate',
                'improvement_rate': -0.089,  # Negative is good for churn
                'prediction_next_quarter': 0.021
            }
        }

        # Mock visualizations
        visualizations = [
            {
                'id': 'VIZ-001',
                'type': 'line_chart',
                'title': 'Revenue Trend (12 Months)',
                'data_points': 12,
                'metrics': ['revenue', 'target', 'forecast'],
                'url': 'https://bi.company.com/viz/revenue-trend'
            },
            {
                'id': 'VIZ-002',
                'type': 'bar_chart',
                'title': 'Revenue by Product',
                'data_points': 3,
                'metrics': ['revenue', 'growth'],
                'url': 'https://bi.company.com/viz/product-revenue'
            },
            {
                'id': 'VIZ-003',
                'type': 'pie_chart',
                'title': 'Revenue by Region',
                'data_points': 3,
                'metrics': ['revenue_distribution'],
                'url': 'https://bi.company.com/viz/regional-revenue'
            },
            {
                'id': 'VIZ-004',
                'type': 'funnel_chart',
                'title': 'Sales Funnel Conversion',
                'data_points': 6,
                'metrics': ['conversion_rate'],
                'url': 'https://bi.company.com/viz/sales-funnel'
            },
            {
                'id': 'VIZ-005',
                'type': 'gauge_chart',
                'title': 'KPI Scorecard',
                'data_points': 7,
                'metrics': ['kpi_status'],
                'url': 'https://bi.company.com/viz/kpi-scorecard'
            }
        ]

        # Mock executive summary
        executive_summary = '''
## Executive Summary - November 2025

### Overall Performance: ON TRACK ✓

**Revenue:** $485K (+11.7% MoM) - Strong growth momentum continuing. On track to exceed Q4 target by 8%. December forecast: $512K.

**Key Highlights:**
- Revenue growth accelerating: 11.7% MoM (up from 7.2% last month)
- Churn rate improved 17.9% to 2.3% - retention initiatives working
- CAC decreased 8.1% to $2,850 - marketing efficiency improving
- LTV:CAC ratio at healthy 10:1, improving toward target of 12:1
- Customer base grew to 847 (+42 new, -7 churned)

**Areas Needing Attention:**
- Sales cycle lengthened to 47 days (up from 42) - investigate qualification process
- Customer acquisition slightly behind target (42 vs 50) - consider marketing boost in December
- CAC still 14% above target despite improvement
- Churn rate at 2.3% vs target of 2.0% - continue focus on retention

**Strategic Priorities:**
1. Accelerate customer acquisition to meet quarterly targets
2. Continue sales cycle optimization initiatives
3. Maintain momentum on churn reduction programs
4. Further optimize paid marketing channels

**Financial Health:** Strong. $2.45M cash, 54-month runway, healthy margins at 80% gross and 29.5% operating.

**Forecast:** December revenue projected at $512K. Q4 target achievement: 108% (exceeding by $75K).
        '''.strip()

        # Mock comparative analysis
        comparative_analysis = {
            'mom_comparison': {
                'revenue': {'current': 485000, 'previous': 434000, 'change': 0.117},
                'customers': {'current': 847, 'previous': 812, 'change': 0.043},
                'mrr': {'current': 145000, 'previous': 138000, 'change': 0.051}
            },
            'yoy_comparison': {
                'revenue': {'current': 485000, 'previous_year': 342000, 'change': 0.418},
                'customers': {'current': 847, 'previous_year': 587, 'change': 0.443},
                'mrr': {'current': 145000, 'previous_year': 98000, 'change': 0.480}
            },
            'vs_budget': {
                'revenue': {'actual': 485000, 'budget': 500000, 'variance_pct': -0.030},
                'customers': {'actual': 847, 'budget': 875, 'variance_pct': -0.032},
                'expenses': {'actual': 342000, 'budget': 350000, 'variance_pct': -0.023}
            }
        }

        return {
            'status': 'success',
            'report': executive_dashboard,
            'report_type': report_type,
            'format': output_format,
            'executive_summary': executive_summary if options.get('executive_summary') else None,
            'insights': insights,
            'total_insights': len(insights),
            'critical_insights': len([i for i in insights if i['priority'] == 'high']),
            'sales_performance': sales_performance,
            'financial_metrics': financial_metrics,
            'operational_metrics': operational_metrics,
            'trends': trends if options.get('include_predictions') else None,
            'comparative_analysis': comparative_analysis if options.get('include_comparisons') else None,
            'visualizations': visualizations if options.get('include_visualizations') else None,
            'report_metadata': {
                'refresh_frequency': 'daily',
                'data_sources': ['CRM', 'Accounting', 'Analytics', 'Support'],
                'last_data_sync': '2025-11-16 06:00:00',
                'data_quality_score': 0.96,
                'report_url': 'https://bi.company.com/reports/executive-nov-2025'
            },
            'recommendations': [
                'Revenue momentum strong - maintain current sales initiatives',
                'Investigate sales cycle lengthening - may impact Q4 targets',
                'Boost marketing spend in December to hit customer acquisition goal',
                'Churn reduction programs working - maintain investment',
                'CAC improving but still above target - further optimize paid channels',
                'LTV:CAC ratio healthy at 10:1 - continue toward 12:1 target',
                'Strong financial position - consider strategic investments',
                'Monitor sales cycle closely - address if continues to lengthen'
            ],
            'action_items': [
                {
                    'priority': 'high',
                    'action': 'Review sales qualification process',
                    'owner': 'VP Sales',
                    'due_date': '2025-11-30'
                },
                {
                    'priority': 'high',
                    'action': 'Increase marketing budget for December',
                    'owner': 'CMO',
                    'due_date': '2025-11-20'
                },
                {
                    'priority': 'medium',
                    'action': 'Analyze paid channel performance',
                    'owner': 'Marketing Director',
                    'due_date': '2025-11-25'
                },
                {
                    'priority': 'medium',
                    'action': 'Document churn reduction success factors',
                    'owner': 'Customer Success',
                    'due_date': '2025-12-05'
                }
            ],
            'next_steps': [
                'Share executive summary with leadership team',
                'Schedule monthly business review meeting',
                'Update Q4 forecast based on current trends',
                'Brief department heads on action items',
                'Monitor sales cycle daily for remainder of quarter',
                'Prepare year-end planning based on strong performance',
                'Set up automated alerts for key metric thresholds'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate BI reporting parameters."""
        valid_report_types = [
            'executive', 'sales', 'financial', 'operational', 'custom'
        ]
        valid_formats = ['dashboard', 'pdf', 'excel', 'presentation']

        report_type = params.get('report_type')
        if report_type and report_type not in valid_report_types:
            self.logger.error(f"Invalid report type: {report_type}")
            return False

        output_format = params.get('format')
        if output_format and output_format not in valid_formats:
            self.logger.error(f"Invalid format: {output_format}")
            return False

        return True
