"""
Recommendation Engine Agent

Generates personalized recommendations using collaborative filtering,
content-based filtering, and hybrid approaches.
"""

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


class RecommendationEngineAgent(BaseAgent):
    """
    Generates personalized recommendations.

    Supports:
    - Collaborative filtering (user-based, item-based)
    - Content-based filtering
    - Hybrid recommendation models
    - Real-time personalization
    - Cold-start handling
    - A/B testing recommendation strategies
    """

    def __init__(self):
        super().__init__(
            name='recommendation-engine',
            description='Generate personalized recommendations',
            category='data',
            version='1.0.0',
            tags=['recommendations', 'personalization', 'machine-learning', 'collaborative-filtering']
        )

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

        Args:
            params: {
                'user_id': str,
                'recommendation_type': 'product|content|user|feature',
                'algorithm': 'collaborative|content_based|hybrid|matrix_factorization',
                'data_source': str,
                'context': {
                    'current_item': str,
                    'session_data': Dict[str, Any],
                    'user_preferences': Dict[str, Any]
                },
                'options': {
                    'num_recommendations': int,
                    'diversity_weight': float,
                    'include_explanations': bool,
                    'filter_purchased': bool,
                    'boost_trending': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'user_id': str,
                'recommendations': List[Dict[str, Any]],
                'algorithm_used': str,
                'model_confidence': float,
                'execution_time_seconds': float,
                'personalization_score': float,
                'insights': List[str]
            }
        """
        user_id = params.get('user_id')
        recommendation_type = params.get('recommendation_type', 'product')
        algorithm = params.get('algorithm', 'hybrid')
        data_source = params.get('data_source')
        context = params.get('context', {})
        options = params.get('options', {})

        num_recommendations = options.get('num_recommendations', 10)

        self.logger.info(
            f"Generating {num_recommendations} {recommendation_type} recommendations "
            f"for user {user_id} using {algorithm}"
        )

        # Mock recommendation generation
        recommendations = self._generate_mock_recommendations(
            recommendation_type,
            num_recommendations,
            options
        )

        return {
            'status': 'success',
            'user_id': user_id,
            'recommendation_type': recommendation_type,
            'algorithm_used': algorithm,
            'data_source': data_source,
            'total_recommendations': len(recommendations),
            'execution_time_seconds': 0.45,
            'recommendations': recommendations,
            'model_confidence': 0.87,
            'personalization_score': 0.92,
            'diversity_score': 0.78,
            'coverage': {
                'catalog_items': 50000,
                'recommended_categories': 8,
                'diversity_index': 0.78
            },
            'context_signals': {
                'current_session_items': 3,
                'user_preference_match': 0.85,
                'trending_factor': 0.42,
                'seasonal_relevance': 0.68
            },
            'recommendation_strategies': {
                'collaborative_weight': 0.50,
                'content_based_weight': 0.30,
                'trending_weight': 0.15 if options.get('boost_trending') else 0.0,
                'diversity_weight': options.get('diversity_weight', 0.05)
            },
            'performance_metrics': {
                'expected_ctr': 0.12,
                'expected_conversion': 0.04,
                'expected_engagement': 0.35,
                'estimated_revenue_lift': 0.18
            },
            'user_profile': {
                'segment': 'power_user',
                'lifetime_value': 2450,
                'purchase_count': 12,
                'avg_order_value': 87.50,
                'preferred_categories': ['Electronics', 'Books', 'Home']
            },
            'insights': [
                'Recommendations personalized based on 12 previous purchases',
                'High confidence in top 5 recommendations (>90%)',
                'User shows strong preference for Electronics category',
                'Seasonal trends incorporated for higher relevance',
                'Diversity optimization applied to avoid filter bubble'
            ],
            'ab_test_variant': 'hybrid_v2',
            'fallback_used': False,
            'cold_start_handled': False
        }

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

        valid_algorithms = ['collaborative', 'content_based', 'hybrid', 'matrix_factorization']
        algorithm = params.get('algorithm', 'hybrid')

        if algorithm not in valid_algorithms:
            self.logger.error(f"Invalid algorithm: {algorithm}")
            return False

        valid_types = ['product', 'content', 'user', 'feature']
        rec_type = params.get('recommendation_type', 'product')

        if rec_type not in valid_types:
            self.logger.error(f"Invalid recommendation type: {rec_type}")
            return False

        return True

    def _generate_mock_recommendations(
        self,
        rec_type: str,
        count: int,
        options: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Generate mock recommendations."""
        recommendations = []

        for i in range(count):
            score = 0.95 - (i * 0.05)
            recommendations.append({
                'rank': i + 1,
                'item_id': f'ITEM_{1000 + i}',
                'title': f'Recommended {rec_type.title()} {i + 1}',
                'score': round(score, 3),
                'confidence': round(score * 0.9, 3),
                'category': ['Electronics', 'Books', 'Home', 'Sports'][i % 4],
                'price': 49.99 + (i * 10),
                'popularity_rank': 100 + (i * 50),
                'explanation': f'Based on your interest in similar {rec_type}s and purchase history' if options.get('include_explanations') else None,
                'reasoning': {
                    'collaborative_score': round(score * 0.5, 3),
                    'content_similarity': round(score * 0.3, 3),
                    'trending_score': round(score * 0.2, 3)
                } if options.get('include_explanations') else None,
                'metadata': {
                    'in_stock': True,
                    'avg_rating': 4.5 - (i * 0.1),
                    'num_reviews': 250 - (i * 10),
                    'new_arrival': i < 3
                }
            })

        return recommendations
