#!/usr/bin/env python3
"""
Post-Launch Monitoring System Implementation
Project Starlight - Steganography Detection System

This module implements automated monitoring checklist execution,
trajectory verification, system status monitoring, and contingency procedures.
"""

import json
import math
import datetime
import hashlib
import re
import base64
from typing import Dict, List, Optional, Any, Union
from dataclasses import dataclass
from collections import defaultdict

@dataclass
class MonitoringResult:
    """Results from monitoring checklist execution."""
    item_id: str
    status: str  # PASS, FAIL, WARN
    metrics: Dict[str, float]
    timestamp: str
    details: str
    requires_action: bool

class PostLaunchMonitor:
    """Main post-launch monitoring system."""
    
    def __init__(self, config_file: str = "post_launch_monitoring_checklist.json"):
        """Initialize monitoring system with configuration."""
        self.config = self._load_config(config_file)
        self.results = []
        self.active_alerts = []
        
    def _load_config(self, config_file: str) -> Dict:
        """Load monitoring configuration from JSON file."""
        try:
            with open(config_file, 'r') as f:
                return json.load(f)
        except FileNotFoundError:
            # Return default configuration if file not found
            return self._default_config()
    
    def _default_config(self) -> Dict:
        """Default monitoring configuration."""
        return {
            "post_launch_monitoring": {
                "checklist_items": [
                    {
                        "id": "DEFAULT_001",
                        "category": "System Health",
                        "title": "Basic Health Check",
                        "frequency": "5 minutes",
                        "priority": "HIGH"
                    }
                ]
            }
        }
    
    def execute_trajectory_verification(self) -> List[MonitoringResult]:
        """Execute trajectory verification checks."""
        results = []
        
        # Model Performance Trajectory Check
        current_accuracy = self._simulate_accuracy_metric()
        accuracy_threshold = 0.85
        status = "PASS" if current_accuracy >= accuracy_threshold else "FAIL"
        
        result = MonitoringResult(
            item_id="TRAJ_001",
            status=status,
            metrics={
                "accuracy": current_accuracy,
                "threshold": accuracy_threshold,
                "delta": current_accuracy - accuracy_threshold
            },
            timestamp=datetime.datetime.now().isoformat(),
            details=f"Model accuracy: {current_accuracy:.3f} (threshold: {accuracy_threshold})",
            requires_action=status == "FAIL"
        )
        results.append(result)
        
        # Data Ingestion Trajectory Check
        processing_rate = self._simulate_processing_rate()
        queue_depth = self._simulate_queue_depth()
        
        status = "PASS" if processing_rate >= 1000 and queue_depth <= 500 else "WARN"
        result = MonitoringResult(
            item_id="TRAJ_002",
            status=status,
            metrics={
                "processing_rate": processing_rate,
                "queue_depth": queue_depth
            },
            timestamp=datetime.datetime.now().isoformat(),
            details=f"Processing: {processing_rate}/min, Queue: {queue_depth}",
            requires_action=status == "FAIL"
        )
        results.append(result)
        
        return results
    
    def execute_system_monitoring(self) -> List[MonitoringResult]:
        """Execute system status monitoring checks."""
        results = []
        
        # Resource Utilization Check
        cpu_usage = self._simulate_cpu_usage()
        memory_usage = self._simulate_memory_usage()
        disk_usage = self._simulate_disk_usage()
        
        status = "PASS"
        if cpu_usage > 80 or memory_usage > 85 or disk_usage > 90:
            status = "FAIL"
        elif cpu_usage > 70 or memory_usage > 75 or disk_usage > 80:
            status = "WARN"
        
        result = MonitoringResult(
            item_id="SYS_001",
            status=status,
            metrics={
                "cpu_usage": cpu_usage,
                "memory_usage": memory_usage,
                "disk_usage": disk_usage
            },
            timestamp=datetime.datetime.now().isoformat(),
            details=f"CPU: {cpu_usage:.1f}%, Memory: {memory_usage:.1f}%, Disk: {disk_usage:.1f}%",
            requires_action=status == "FAIL"
        )
        results.append(result)
        
        # Service Health Check
        services_status = self._check_service_health()
        failed_services = [s for s, status in services_status.items() if status != "healthy"]
        
        status = "PASS" if not failed_services else "FAIL"
        result = MonitoringResult(
            item_id="SYS_002",
            status=status,
            metrics={
                "total_services": len(services_status),
                "healthy_services": len(services_status) - len(failed_services),
                "failed_services": len(failed_services)
            },
            timestamp=datetime.datetime.now().isoformat(),
            details=f"Services: {len(services_status) - len(failed_services)}/{len(services_status)} healthy",
            requires_action=len(failed_services) > 0
        )
        results.append(result)
        
        return results
    
    def execute_abort_procedures(self, trigger_condition: str) -> Dict:
        """Execute abort procedures based on trigger condition."""
        abort_actions = {
            "critical_failure": self._critical_failure_abort(),
            "performance_degradation": self._performance_degradation_abort(),
            "security_breach": self._security_breach_abort()
        }
        
        return abort_actions.get(trigger_condition, {"status": "unknown_trigger"})
    
    def _critical_failure_abort(self) -> Dict:
        """Execute critical failure abort procedure."""
        abort_steps = [
            "Redirecting traffic to backup system",
            "Shutting down detection services gracefully",
            "Preserving system state and logs",
            "Notifying incident response team",
            "Activating disaster recovery protocol"
        ]
        
        executed_steps = []
        for step in abort_steps:
            # Simulate step execution
            execution_time = datetime.datetime.now().isoformat()
            executed_steps.append({
                "step": step,
                "status": "completed",
                "timestamp": execution_time
            })
        
        return {
            "abort_type": "critical_failure",
            "status": "executed",
            "steps_executed": executed_steps,
            "rollback_available": True,
            "estimated_recovery_time": "15 minutes"
        }
    
    def _performance_degradation_abort(self) -> Dict:
        """Execute performance degradation abort procedure."""
        return {
            "abort_type": "performance_degradation",
            "status": "executed",
            "actions": [
                "Stopped current model serving",
                "Loaded previous stable model version",
                "Validated rollback model performance",
                "Logged degradation incident",
                "Triggered model retraining pipeline"
            ],
            "rollback_complete": True,
            "retraining_triggered": True
        }
    
    def _security_breach_abort(self) -> Dict:
        """Execute security breach abort procedure."""
        return {
            "abort_type": "security_breach",
            "status": "executed",
            "actions": [
                "Isolated affected system components",
                "Enabled enhanced monitoring",
                "Switched to hardened detection mode",
                "Preserved forensic evidence",
                "Engaged security team"
            ],
            "security_level": "high",
            "forensics_preserved": True
        }
    
    def execute_contingency_planning(self, scenario: str) -> Dict:
        """Execute contingency planning for various failure scenarios."""
        contingency_actions = {
            "model_drift": self._handle_model_drift(),
            "data_pipeline_failure": self._handle_data_pipeline_failure(),
            "security_incident": self._handle_security_incident()
        }
        
        return contingency_actions.get(scenario, {"status": "unknown_scenario"})
    
    def _handle_model_drift(self) -> Dict:
        """Handle model drift contingency scenario."""
        return {
            "scenario": "model_drift",
            "actions_taken": [
                "Enabled ensemble model voting",
                "Increased model validation frequency",
                "Triggered automated retraining",
                "Switched to conservative detection thresholds",
                "Notified ML engineering team"
            ],
            "mitigation_status": "active",
            "retraining_triggered": True
        }
    
    def _handle_data_pipeline_failure(self) -> Dict:
        """Handle data pipeline failure contingency scenario."""
        return {
            "scenario": "data_pipeline_failure",
            "actions_taken": [
                "Switched to cached data sources",
                "Scaled processing resources",
                "Enabled simplified processing pipeline",
                "Activated data replication protocols"
            ],
            "pipeline_status": "recovery_mode",
            "backup_active": True
        }
    
    def _handle_security_incident(self) -> Dict:
        """Handle security incident contingency scenario."""
        return {
            "scenario": "security_incident",
            "actions_taken": [
                "Isolated affected system components",
                "Enabled enhanced monitoring",
                "Switched to hardened detection mode",
                "Preserved forensic evidence",
                "Engaged security team"
            ],
            "security_level": "elevated",
            "investigation_active": True
        }
    
    def run_full_monitoring_cycle(self) -> Dict:
        """Run complete monitoring cycle and return results."""
        cycle_start = datetime.datetime.now()
        
        # Execute all monitoring components
        trajectory_results = self.execute_trajectory_verification()
        system_results = self.execute_system_monitoring()
        
        all_results = trajectory_results + system_results
        self.results.extend(all_results)
        
        # Generate summary
        failed_items = [r for r in all_results if r.status == "FAIL"]
        warning_items = [r for r in all_results if r.status == "WARN"]
        
        summary = {
            "cycle_start": cycle_start.isoformat(),
            "cycle_duration": (datetime.datetime.now() - cycle_start).total_seconds(),
            "total_checks": len(all_results),
            "passed": len([r for r in all_results if r.status == "PASS"]),
            "failed": len(failed_items),
            "warnings": len(warning_items),
            "requires_action": len(failed_items) > 0,
            "results": [
                {
                    "item_id": r.item_id,
                    "status": r.status,
                    "metrics": r.metrics,
                    "details": r.details
                } for r in all_results
            ]
        }
        
        return summary
    
    # Simulation methods for demonstration
    def _simulate_accuracy_metric(self) -> float:
        """Simulate current model accuracy."""
        base_accuracy = 0.87
        variation = (math.sin(datetime.datetime.now().timestamp() / 3600) * 0.05)
        return max(0.0, min(1.0, base_accuracy + variation))
    
    def _simulate_processing_rate(self) -> int:
        """Simulate data processing rate."""
        base_rate = 1200
        variation = int(math.cos(datetime.datetime.now().timestamp() / 1800) * 200)
        return max(0, base_rate + variation)
    
    def _simulate_queue_depth(self) -> int:
        """Simulate queue depth."""
        base_depth = 250
        variation = int(math.sin(datetime.datetime.now().timestamp() / 900) * 150)
        return max(0, base_depth + variation)
    
    def _simulate_cpu_usage(self) -> float:
        """Simulate CPU usage percentage."""
        base_cpu = 65.0
        variation = (math.sin(datetime.datetime.now().timestamp() / 600) * 20)
        return max(0.0, min(100.0, base_cpu + variation))
    
    def _simulate_memory_usage(self) -> float:
        """Simulate memory usage percentage."""
        base_memory = 72.0
        variation = (math.cos(datetime.datetime.now().timestamp() / 800) * 15)
        return max(0.0, min(100.0, base_memory + variation))
    
    def _simulate_disk_usage(self) -> float:
        """Simulate disk usage percentage."""
        base_disk = 45.0
        variation = (math.sin(datetime.datetime.now().timestamp() / 3600) * 10)
        return max(0.0, min(100.0, base_disk + variation))
    
    def _check_service_health(self) -> Dict[str, str]:
        """Simulate service health checks."""
        services = [
            "steganography_detector",
            "model_inference_engine", 
            "data_preprocessor",
            "api_gateway",
            "database_connection"
        ]
        
        # Simulate one service being unhealthy occasionally
        health_status = {}
        for service in services:
            # 95% chance service is healthy
            is_healthy = (hashlib.md5(f"{service}{datetime.datetime.now().date()}".encode()).hexdigest()[0] != '0')
            health_status[service] = "healthy" if is_healthy else "unhealthy"
        
        return health_status

def main():
    """Main execution function for post-launch monitoring system."""
    print("🚀 Project Starlight - Post-Launch Monitoring System")
    print("=" * 60)
    
    # Initialize monitoring system
    monitor = PostLaunchMonitor()
    
    # Run full monitoring cycle
    print("📊 Running monitoring cycle...")
    cycle_results = monitor.run_full_monitoring_cycle()
    
    # Display results
    print(f"\n✅ Monitoring Cycle Complete")
    print(f"   Total Checks: {cycle_results['total_checks']}")
    print(f"   Passed: {cycle_results['passed']}")
    print(f"   Failed: {cycle_results['failed']}")
    print(f"   Warnings: {cycle_results['warnings']}")
    print(f"   Duration: {cycle_results['cycle_duration']:.2f} seconds")
    
    # Show detailed results
    print(f"\n📋 Detailed Results:")
    for result in cycle_results['results']:
        status_icon = "✅" if result['status'] == "PASS" else "❌" if result['status'] == "FAIL" else "⚠️"
        print(f"   {status_icon} {result['item_id']}: {result['details']}")
    
    # Demonstrate abort procedures
    print(f"\n🚨 Testing Abort Procedures...")
    abort_result = monitor.execute_abort_procedures("critical_failure")
    print(f"   Critical Failure Abort: {abort_result['status']}")
    print(f"   Steps Executed: {len(abort_result['steps_executed'])}")
    
    # Demonstrate contingency planning
    print(f"\n🛡️ Testing Contingency Planning...")
    contingency_result = monitor.execute_contingency_planning("model_drift")
    print(f"   Model Drift Handling: {contingency_result['mitigation_status']}")
    print(f"   Actions Taken: {len(contingency_result['actions_taken'])}")
    
    # Generate execution report
    report = {
        "execution_time": datetime.datetime.now().isoformat(),
        "monitoring_cycle": cycle_results,
        "abort_procedures_tested": ["critical_failure"],
        "contingency_scenarios_tested": ["model_drift"],
        "system_status": "operational" if cycle_results['failed'] == 0 else "degraded"
    }
    
    # Save execution report
    with open("monitoring_execution_report.json", "w") as f:
        json.dump(report, f, indent=2)
    
    print(f"\n📄 Execution report saved to: monitoring_execution_report.json")
    print(f"🎯 System Status: {report['system_status'].upper()}")
    
    return report

if __name__ == "__main__":
    main()