"""
Model Monitoring Agent

Monitors deployed ML models for performance, drift, and anomalies.
Provides real-time alerts and automated remediation.
"""

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


class ModelMonitoringAgent(BaseAgent):
    """
    Monitors deployed ML models in production.

    Features:
    - Performance monitoring (accuracy, latency, throughput)
    - Data drift detection
    - Model drift detection
    - Concept drift detection
    - Anomaly detection
    - Real-time alerting
    - Automated remediation triggers
    - Dashboard and visualization
    """

    def __init__(self):
        super().__init__(
            name='model-monitoring-agent',
            description='Monitor deployed ML models for performance and drift',
            category='ai_ml',
            version='1.0.0',
            tags=['ml', 'monitoring', 'drift-detection', 'observability', 'mlops']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Monitor ML model in production.

        Args:
            params: {
                'model_info': {
                    'model_id': str,
                    'model_name': str,
                    'version': str,
                    'endpoint': str,
                    'deployment_date': str
                },
                'monitoring_config': {
                    'performance_metrics': [
                        'accuracy', 'precision', 'recall', 'f1',
                        'latency', 'throughput', 'error_rate'
                    ],
                    'drift_detection': {
                        'data_drift': {
                            'enabled': bool,
                            'method': 'ks_test|chi_square|psi|kl_divergence',
                            'threshold': float,
                            'window_size': int
                        },
                        'model_drift': {
                            'enabled': bool,
                            'baseline_accuracy': float,
                            'threshold': float
                        },
                        'concept_drift': {
                            'enabled': bool,
                            'method': 'adwin|ddm|eddm|page_hinkley',
                            'sensitivity': float
                        }
                    },
                    'anomaly_detection': {
                        'enabled': bool,
                        'predictions': bool,
                        'inputs': bool,
                        'outputs': bool,
                        'method': 'isolation_forest|autoencoder|statistics'
                    }
                },
                'alerting': {
                    'channels': ['email', 'slack', 'pagerduty', 'webhook'],
                    'rules': List[{
                        'metric': str,
                        'condition': str,
                        'threshold': float,
                        'severity': 'low|medium|high|critical',
                        'cooldown_minutes': int
                    }],
                    'escalation': bool
                },
                'remediation': {
                    'auto_rollback': {
                        'enabled': bool,
                        'conditions': List[str]
                    },
                    'auto_retrain': {
                        'enabled': bool,
                        'trigger_conditions': List[str]
                    },
                    'circuit_breaker': {
                        'enabled': bool,
                        'error_threshold': float,
                        'timeout_seconds': int
                    }
                },
                'data_collection': {
                    'log_predictions': bool,
                    'log_inputs': bool,
                    'log_ground_truth': bool,
                    'sampling_rate': float,
                    'retention_days': int
                },
                'time_window': {
                    'start_time': str,
                    'end_time': str,
                    'granularity': 'minute|hour|day'
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'monitoring_id': str,
                'model_info': {
                    'model_id': str,
                    'model_name': str,
                    'version': str,
                    'uptime_percentage': float,
                    'requests_processed': int
                },
                'performance_metrics': {
                    'current': {
                        'accuracy': float,
                        'precision': float,
                        'recall': float,
                        'f1_score': float,
                        'latency_p50_ms': float,
                        'latency_p95_ms': float,
                        'latency_p99_ms': float,
                        'throughput_rps': float,
                        'error_rate': float
                    },
                    'baseline': {
                        'accuracy': float,
                        'latency_p95_ms': float,
                        'throughput_rps': float
                    },
                    'degradation': {
                        'accuracy_drop': float,
                        'latency_increase': float,
                        'throughput_decrease': float
                    }
                },
                'drift_analysis': {
                    'data_drift': {
                        'detected': bool,
                        'drift_score': float,
                        'drifted_features': List[str],
                        'severity': 'none|low|medium|high',
                        'drift_details': Dict[str, Any]
                    },
                    'model_drift': {
                        'detected': bool,
                        'accuracy_degradation': float,
                        'performance_decline': float,
                        'severity': 'none|low|medium|high'
                    },
                    'concept_drift': {
                        'detected': bool,
                        'drift_point': str,
                        'confidence': float,
                        'severity': 'none|low|medium|high'
                    }
                },
                'anomalies': {
                    'total_detected': int,
                    'prediction_anomalies': int,
                    'input_anomalies': int,
                    'output_anomalies': int,
                    'anomaly_examples': List[Dict[str, Any]],
                    'anomaly_rate': float
                },
                'alerts_triggered': List[{
                    'alert_id': str,
                    'timestamp': str,
                    'severity': str,
                    'metric': str,
                    'message': str,
                    'current_value': float,
                    'threshold': float,
                    'status': 'active|resolved',
                    'resolution_time': str
                }],
                'remediation_actions': List[{
                    'action_type': str,
                    'triggered_at': str,
                    'trigger_reason': str,
                    'status': 'pending|in_progress|completed|failed',
                    'details': Dict[str, Any]
                }],
                'data_quality': {
                    'missing_values_rate': float,
                    'schema_violations': int,
                    'invalid_predictions': int,
                    'out_of_range_inputs': int
                },
                'traffic_analysis': {
                    'total_requests': int,
                    'requests_per_hour': float,
                    'peak_rps': float,
                    'error_count': int,
                    'timeout_count': int,
                    'retry_count': int
                },
                'system_health': {
                    'cpu_utilization': float,
                    'memory_utilization': float,
                    'disk_usage': float,
                    'network_throughput_mbps': float,
                    'pod_restarts': int
                },
                'recommendations': List[str]
            }
        """
        model_info = params.get('model_info', {})
        monitoring_config = params.get('monitoring_config', {})

        model_name = model_info.get('model_name', 'model')
        model_version = model_info.get('version', 'v1')

        self.logger.info(
            f"Monitoring model {model_name} version {model_version}"
        )

        # Mock monitoring results
        data_drift_detected = True
        model_drift_detected = False
        concept_drift_detected = False

        return {
            'status': 'success',
            'monitoring_id': f'monitor_{model_name}_{model_version}',
            'monitoring_period': {
                'start_time': '2025-11-16T00:00:00Z',
                'end_time': '2025-11-16T23:59:59Z',
                'duration_hours': 24
            },
            'model_info': {
                'model_id': model_info.get('model_id', 'model_001'),
                'model_name': model_name,
                'version': model_version,
                'deployment_date': model_info.get('deployment_date', '2025-11-10T00:00:00Z'),
                'uptime_percentage': 99.87,
                'requests_processed': 1234567,
                'days_in_production': 6
            },
            'performance_metrics': {
                'current': {
                    'accuracy': 0.9234,
                    'precision': 0.9156,
                    'recall': 0.9323,
                    'f1_score': 0.9239,
                    'latency_p50_ms': 23.4,
                    'latency_p95_ms': 56.7,
                    'latency_p99_ms': 89.2,
                    'throughput_rps': 850.5,
                    'error_rate': 0.0013,
                    'availability': 99.87
                },
                'baseline': {
                    'accuracy': 0.9712,
                    'precision': 0.9623,
                    'recall': 0.9689,
                    'f1_score': 0.9656,
                    'latency_p95_ms': 45.2,
                    'throughput_rps': 1250.0,
                    'error_rate': 0.0005
                },
                'degradation': {
                    'accuracy_drop': 0.0478,
                    'accuracy_drop_percentage': 4.92,
                    'latency_increase': 11.5,
                    'latency_increase_percentage': 25.4,
                    'throughput_decrease': 399.5,
                    'throughput_decrease_percentage': 32.0,
                    'error_rate_increase': 0.0008
                }
            },
            'drift_analysis': {
                'data_drift': {
                    'detected': data_drift_detected,
                    'drift_score': 0.34,
                    'threshold': 0.2,
                    'drifted_features': [
                        'feature_5',
                        'feature_12',
                        'feature_23'
                    ],
                    'severity': 'medium',
                    'drift_details': {
                        'feature_5': {
                            'drift_score': 0.45,
                            'method': 'ks_test',
                            'p_value': 0.0023
                        },
                        'feature_12': {
                            'drift_score': 0.38,
                            'method': 'ks_test',
                            'p_value': 0.0056
                        },
                        'feature_23': {
                            'drift_score': 0.29,
                            'method': 'ks_test',
                            'p_value': 0.0123
                        }
                    },
                    'first_detected': '2025-11-15T14:30:00Z'
                },
                'model_drift': {
                    'detected': model_drift_detected,
                    'accuracy_degradation': 0.0478,
                    'performance_decline': 4.92,
                    'severity': 'low',
                    'trend': 'declining'
                },
                'concept_drift': {
                    'detected': concept_drift_detected,
                    'drift_point': None,
                    'confidence': 0.0,
                    'severity': 'none',
                    'method': 'adwin'
                }
            },
            'anomalies': {
                'total_detected': 1234,
                'prediction_anomalies': 456,
                'input_anomalies': 678,
                'output_anomalies': 100,
                'anomaly_rate': 0.001,
                'anomaly_examples': [
                    {
                        'id': 'anomaly_001',
                        'type': 'prediction',
                        'timestamp': '2025-11-16T15:23:45Z',
                        'anomaly_score': 0.92,
                        'description': 'Prediction confidence unusually low'
                    },
                    {
                        'id': 'anomaly_002',
                        'type': 'input',
                        'timestamp': '2025-11-16T16:45:12Z',
                        'anomaly_score': 0.87,
                        'description': 'Input feature values out of expected range'
                    }
                ],
                'anomaly_trend': 'increasing'
            },
            'alerts_triggered': [
                {
                    'alert_id': 'alert_001',
                    'timestamp': '2025-11-16T14:30:00Z',
                    'severity': 'high',
                    'metric': 'data_drift',
                    'message': 'Data drift detected in 3 features',
                    'current_value': 0.34,
                    'threshold': 0.2,
                    'status': 'active',
                    'resolution_time': None,
                    'channels_notified': ['slack', 'email']
                },
                {
                    'alert_id': 'alert_002',
                    'timestamp': '2025-11-16T18:15:00Z',
                    'severity': 'medium',
                    'metric': 'accuracy',
                    'message': 'Model accuracy dropped below threshold',
                    'current_value': 0.9234,
                    'threshold': 0.95,
                    'status': 'active',
                    'resolution_time': None,
                    'channels_notified': ['slack']
                }
            ],
            'remediation_actions': [
                {
                    'action_type': 'auto_retrain_triggered',
                    'triggered_at': '2025-11-16T14:35:00Z',
                    'trigger_reason': 'Data drift detected above threshold',
                    'status': 'in_progress',
                    'details': {
                        'estimated_completion': '2025-11-16T18:35:00Z',
                        'training_job_id': 'train_job_123'
                    }
                }
            ],
            'data_quality': {
                'total_samples_analyzed': 1234567,
                'missing_values_rate': 0.0023,
                'missing_values_count': 2839,
                'schema_violations': 45,
                'invalid_predictions': 67,
                'out_of_range_inputs': 234,
                'duplicate_requests': 123,
                'data_quality_score': 0.9976
            },
            'traffic_analysis': {
                'total_requests': 1234567,
                'requests_per_hour': 51440.3,
                'requests_per_second_avg': 14.3,
                'peak_rps': 234.5,
                'error_count': 1605,
                'timeout_count': 234,
                'retry_count': 456,
                'cache_hit_rate': 0.34,
                'traffic_pattern': 'stable'
            },
            'system_health': {
                'cpu_utilization': 67.5,
                'cpu_limit': 100.0,
                'memory_utilization': 72.3,
                'memory_limit_gb': 16.0,
                'disk_usage': 45.6,
                'disk_total_gb': 100.0,
                'network_throughput_mbps': 234.5,
                'pod_restarts': 2,
                'gpu_utilization': 0.0,
                'health_status': 'healthy'
            },
            'prediction_distribution': {
                'class_0': 0.334,
                'class_1': 0.333,
                'class_2': 0.333,
                'distribution_shift': 0.012,
                'entropy': 1.098
            },
            'feature_statistics': {
                'numerical_features': {
                    'feature_1': {
                        'mean': 0.45,
                        'std': 0.23,
                        'min': 0.01,
                        'max': 0.99,
                        'drift_score': 0.08
                    }
                },
                'categorical_features': {
                    'feature_cat_1': {
                        'unique_values': 5,
                        'mode': 'category_a',
                        'entropy': 1.56,
                        'drift_score': 0.12
                    }
                }
            },
            'recommendations': [
                'ALERT: Data drift detected in 3 features - retraining recommended',
                'Model accuracy dropped by 4.9% from baseline - investigate root cause',
                'Auto-retraining triggered and currently in progress',
                'Latency increased by 25% - consider scaling infrastructure',
                'Throughput decreased by 32% - check resource constraints',
                'Anomaly detection rate is within acceptable bounds (0.1%)',
                'System health is good - CPU and memory within normal ranges',
                'Consider adding more monitoring for drifted features',
                'Review feature engineering for features 5, 12, and 23',
                'Set up A/B test to validate retrained model before deployment',
                'Increase sampling rate for prediction logging during drift periods',
                'Schedule maintenance window for infrastructure upgrades'
            ]
        }

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

        model_info = params['model_info']
        required_fields = ['model_id', 'model_name', 'version']
        for field in required_fields:
            if field not in model_info:
                self.logger.error(f"Missing required field: model_info.{field}")
                return False

        return True
