"""
Analytics Reporter Agent

Generates comprehensive analytics reports with insights,
visualizations, and actionable recommendations.
"""

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


class AnalyticsReporterAgent(BaseAgent):
    """
    Generates analytics reports with insights and visualizations.

    Supports:
    - Automated report generation
    - Multi-format exports (PDF, HTML, Excel, CSV)
    - Scheduled reporting
    - Interactive dashboards
    - Custom metrics and KPIs
    - Trend analysis and forecasting
    """

    def __init__(self):
        super().__init__(
            name='analytics-reporter',
            description='Generate comprehensive analytics reports',
            category='data',
            version='1.0.0',
            tags=['reporting', 'analytics', 'insights', 'visualization']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate an analytics report.

        Args:
            params: {
                'report_type': 'executive|operational|financial|marketing|product',
                'data_source': str,
                'time_period': {
                    'start_date': str,
                    'end_date': str,
                    'granularity': 'daily|weekly|monthly|quarterly|yearly'
                },
                'metrics': List[str],
                'dimensions': List[str],
                'filters': Dict[str, Any],
                'visualizations': List[str],  # chart types
                'format': 'pdf|html|excel|csv|json',
                'options': {
                    'include_insights': bool,
                    'include_recommendations': bool,
                    'include_forecasts': bool,
                    'compare_previous_period': bool,
                    'send_email': bool,
                    'recipients': List[str]
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'report_id': str,
                'report_type': str,
                'file_path': str,
                'file_size_mb': float,
                'metrics_included': int,
                'visualizations_created': int,
                'insights_generated': int,
                'time_period': Dict[str, str],
                'key_findings': List[str],
                'recommendations': List[str],
                'execution_time_seconds': float,
                'preview_url': str
            }
        """
        report_type = params.get('report_type', 'executive')
        data_source = params.get('data_source')
        time_period = params.get('time_period', {})
        metrics = params.get('metrics', [])
        format_type = params.get('format', 'pdf')
        options = params.get('options', {})

        self.logger.info(
            f"Generating {report_type} report in {format_type} format"
        )

        # Mock report generation
        return {
            'status': 'success',
            'report_id': f'report_{report_type}_20251116',
            'report_type': report_type,
            'file_path': f'/reports/{report_type}_report_20251116.{format_type}',
            'file_size_mb': 2.4,
            'format': format_type,
            'metrics_included': len(metrics) or 12,
            'visualizations_created': 8,
            'insights_generated': 15,
            'pages': 18,
            'time_period': {
                'start_date': time_period.get('start_date', '2025-10-01'),
                'end_date': time_period.get('end_date', '2025-10-31'),
                'granularity': time_period.get('granularity', 'daily')
            },
            'key_findings': [
                'Revenue increased 23% compared to previous period',
                'Customer acquisition cost decreased by 15%',
                'User engagement rate improved by 8.5%',
                'Conversion rate reached all-time high of 4.2%',
                'Churn rate reduced to 2.1% from 3.4%'
            ],
            'metrics_summary': {
                'total_revenue': 458900,
                'total_users': 12450,
                'active_users': 8932,
                'conversion_rate': 4.2,
                'avg_order_value': 87.50
            },
            'recommendations': [
                'Increase marketing spend in channels with highest ROI',
                'Focus on customer retention programs to maintain low churn',
                'Expand successful product categories based on sales data',
                'Optimize checkout flow to improve conversion further',
                'Implement personalization to boost engagement'
            ],
            'trends_identified': [
                'Mobile traffic growing 18% month-over-month',
                'Weekend sales outperforming weekday by 12%',
                'Premium tier subscriptions accelerating'
            ],
            'execution_time_seconds': 8.3,
            'preview_url': 'https://reports.example.com/preview/abc123',
            'scheduled_next_run': '2025-12-16T09:00:00Z' if options.get('schedule') else None
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate reporting parameters."""
        if 'data_source' not in params:
            self.logger.error("Missing required field: data_source")
            return False

        valid_report_types = ['executive', 'operational', 'financial', 'marketing', 'product']
        report_type = params.get('report_type', 'executive')

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

        valid_formats = ['pdf', 'html', 'excel', 'csv', 'json']
        format_type = params.get('format', 'pdf')

        if format_type not in valid_formats:
            self.logger.error(f"Invalid format: {format_type}")
            return False

        return True
