# demo/mock_arf.py
"""
Enhanced Mock ARF with scenario-aware metrics
Generates different values based on scenario characteristics
"""
import random
import time
from typing import Dict, Any, List
import json

# Scenario-specific configurations
SCENARIO_CONFIGS = {
    "Cache Miss Storm": {
        "detection_confidence_range": (0.97, 0.995),  # 97-99.5%
        "detection_time_range": (35, 55),  # 35-55 seconds
        "accuracy_range": (0.97, 0.995),  # 97-99.5%
        "similar_incidents_range": (2, 5),  # 2-5 similar incidents
        "similarity_score_range": (0.88, 0.96),  # 88-96% similarity
        "pattern_confidence_range": (0.91, 0.97),  # 91-97% confidence
        "success_rate_range": (0.82, 0.93),  # 82-93% success rate
        "cost_savings_range": (5000, 9000),  # $5K-$9K savings
        "resolution_time_range": (10, 18),  # 10-18 minutes
        "affected_users_range": (30000, 60000),  # 30K-60K users
        "tags": ["cache", "redis", "latency", "memory"]
    },
    "Database Connection Pool Exhaustion": {
        "detection_confidence_range": (0.92, 0.98),
        "detection_time_range": (40, 65),
        "accuracy_range": (0.95, 0.985),
        "similar_incidents_range": (1, 4),
        "similarity_score_range": (0.85, 0.94),
        "pattern_confidence_range": (0.88, 0.95),
        "success_rate_range": (0.78, 0.88),
        "cost_savings_range": (3500, 5500),
        "resolution_time_range": (15, 25),
        "affected_users_range": (15000, 30000),
        "tags": ["database", "postgres", "connections", "pool"]
    },
    "Kubernetes Memory Leak": {
        "detection_confidence_range": (0.94, 0.99),
        "detection_time_range": (30, 50),
        "accuracy_range": (0.96, 0.99),
        "similar_incidents_range": (3, 6),
        "similarity_score_range": (0.89, 0.95),
        "pattern_confidence_range": (0.90, 0.96),
        "success_rate_range": (0.85, 0.92),
        "cost_savings_range": (4500, 7500),
        "resolution_time_range": (12, 22),
        "affected_users_range": (20000, 40000),
        "tags": ["kubernetes", "memory", "container", "leak"]
    },
    "API Rate Limit Storm": {
        "detection_confidence_range": (0.96, 0.99),
        "detection_time_range": (25, 45),
        "accuracy_range": (0.97, 0.99),
        "similar_incidents_range": (2, 4),
        "similarity_score_range": (0.87, 0.93),
        "pattern_confidence_range": (0.89, 0.94),
        "success_rate_range": (0.80, 0.90),
        "cost_savings_range": (3000, 5000),
        "resolution_time_range": (8, 15),
        "affected_users_range": (10000, 25000),
        "tags": ["api", "rate_limit", "throttling", "ddos"]
    },
    "Network Partition": {
        "detection_confidence_range": (0.98, 0.999),
        "detection_time_range": (20, 40),
        "accuracy_range": (0.98, 0.995),
        "similar_incidents_range": (1, 3),
        "similarity_score_range": (0.90, 0.97),
        "pattern_confidence_range": (0.93, 0.98),
        "success_rate_range": (0.75, 0.85),
        "cost_savings_range": (8000, 15000),
        "resolution_time_range": (20, 35),
        "affected_users_range": (50000, 100000),
        "tags": ["network", "partition", "connectivity", "failure"]
    },
    "Storage I/O Saturation": {
        "detection_confidence_range": (0.93, 0.98),
        "detection_time_range": (45, 70),
        "accuracy_range": (0.94, 0.98),
        "similar_incidents_range": (2, 5),
        "similarity_score_range": (0.86, 0.92),
        "pattern_confidence_range": (0.87, 0.93),
        "success_rate_range": (0.79, 0.87),
        "cost_savings_range": (5500, 8500),
        "resolution_time_range": (18, 28),
        "affected_users_range": (25000, 45000),
        "tags": ["storage", "disk", "io", "saturation"]
    }
}

def get_scenario_config(scenario_name: str) -> Dict[str, Any]:
    """Get configuration for a specific scenario with defaults"""
    return SCENARIO_CONFIGS.get(scenario_name, {
        "detection_confidence_range": (0.90, 0.98),
        "detection_time_range": (30, 60),
        "accuracy_range": (0.92, 0.98),
        "similar_incidents_range": (1, 3),
        "similarity_score_range": (0.85, 0.95),
        "pattern_confidence_range": (0.85, 0.95),
        "success_rate_range": (0.75, 0.90),
        "cost_savings_range": (4000, 8000),
        "resolution_time_range": (15, 30),
        "affected_users_range": (20000, 50000),
        "tags": ["unknown", "incident"]
    })

def simulate_arf_analysis(scenario_data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Simulate ARF analysis with scenario-specific metrics
    
    Args:
        scenario_data: Dictionary containing scenario information
        
    Returns:
        Dictionary with analysis results
    """
    scenario_name = scenario_data.get("name", "Unknown Scenario")
    config = get_scenario_config(scenario_name)
    
    # Generate scenario-specific values
    detection_confidence = random.uniform(*config["detection_confidence_range"])
    detection_time = random.randint(*config["detection_time_range"])
    accuracy = random.uniform(*config["accuracy_range"])
    
    return {
        "analysis_complete": True,
        "anomaly_detected": True,
        "severity": scenario_data.get("severity", "HIGH"),
        "confidence": round(detection_confidence, 3),  # Round to 3 decimals
        "detection_time_ms": detection_time * 1000,  # Convert to ms for display
        "detection_time_seconds": detection_time,
        "accuracy": round(accuracy, 3),
        "component": scenario_data.get("component", "unknown"),
        "scenario_specific": True,
        "scenario_name": scenario_name,
        "tags": config["tags"]
    }

def run_rag_similarity_search(scenario_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Simulate RAG similarity search with scenario-specific results
    
    Args:
        scenario_data: Dictionary containing scenario information
        
    Returns:
        List of similar incidents
    """
    scenario_name = scenario_data.get("name", "Unknown Scenario")
    config = get_scenario_config(scenario_name)
    
    similar_count = random.randint(*config["similar_incidents_range"])
    similar_incidents = []
    
    # Generate similar incidents based on scenario
    base_time = int(time.time())
    
    for i in range(similar_count):
        similarity_score = random.uniform(*config["similarity_score_range"])
        cost_savings = random.randint(*config["cost_savings_range"])
        resolution_time = random.randint(*config["resolution_time_range"])
        affected_users = random.randint(*config["affected_users_range"])
        
        # Different resolutions based on scenario type
        if "cache" in scenario_name.lower() or "redis" in scenario_name.lower():
            resolution = random.choice(["scale_out", "warm_cache", "memory_increase", "add_replicas"])
        elif "database" in scenario_name.lower():
            resolution = random.choice(["restart", "connection_pool_resize", "index_optimization", "vacuum"])
        elif "kubernetes" in scenario_name.lower():
            resolution = random.choice(["restart_pod", "memory_limit_increase", "node_drain", "resource_quota"])
        elif "api" in scenario_name.lower():
            resolution = random.choice(["circuit_breaker", "rate_limit_increase", "caching", "load_balancer"])
        elif "network" in scenario_name.lower():
            resolution = random.choice(["route_update", "failover", "bandwidth_increase", "redundancy"])
        elif "storage" in scenario_name.lower():
            resolution = random.choice(["io_optimization", "disk_upgrade", "cache_addition", "load_distribution"])
        else:
            resolution = random.choice(["investigate", "scale", "restart", "optimize"])
        
        similar_incidents.append({
            "incident_id": f"inc_{base_time - random.randint(1, 90)}_00{i}",
            "similarity_score": round(similarity_score, 3),
            "success": random.random() > 0.15,  # 85% success rate
            "resolution": resolution,
            "cost_savings": cost_savings,
            "detection_time": f"{random.randint(30, 60)}s",
            "resolution_time": f"{resolution_time}m",
            "pattern": f"{scenario_name.lower().replace(' ', '_')}_v{random.randint(1, 3)}",
            "affected_users": affected_users,
            "component_match": scenario_data.get("component", "unknown"),
            "rag_source": "production_memory_v3",
            "timestamp": f"2024-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}"
        })
    
    return similar_incidents

def calculate_pattern_confidence(scenario_data: Dict[str, Any], similar_incidents: List[Dict[str, Any]]) -> float:
    """
    Calculate pattern confidence based on similar incidents
    
    Args:
        scenario_data: Dictionary containing scenario information
        similar_incidents: List of similar incidents from RAG search
        
    Returns:
        Pattern confidence score (0-1)
    """
    scenario_name = scenario_data.get("name", "Unknown Scenario")
    config = get_scenario_config(scenario_name)
    
    if not similar_incidents:
        return random.uniform(*config["pattern_confidence_range"])
    
    # Calculate average similarity and success rate
    similarity_scores = [inc["similarity_score"] for inc in similar_incidents]
    success_rates = [1.0 if inc["success"] else 0.0 for inc in similar_incidents]
    
    avg_similarity = sum(similarity_scores) / len(similarity_scores)
    avg_success = sum(success_rates) / len(success_rates)
    
    # Weighted average: 60% similarity, 40% success rate
    confidence = (avg_similarity * 0.6) + (avg_success * 0.4)
    
    # Add some randomness but keep within scenario range
    min_conf, max_conf = config["pattern_confidence_range"]
    confidence = max(min_conf, min(max_conf, confidence))
    
    return round(confidence, 3)

def create_mock_healing_intent(scenario_data: Dict[str, Any], similar_incidents: List[Dict[str, Any]], confidence: float) -> Dict[str, Any]:
    """
    Create mock healing intent based on scenario and similar incidents
    
    Args:
        scenario_data: Dictionary containing scenario information
        similar_incidents: List of similar incidents from RAG search
        confidence: Pattern confidence score
        
    Returns:
        Healing intent dictionary
    """
    scenario_name = scenario_data.get("name", "Unknown Scenario")
    config = get_scenario_config(scenario_name)
    
    component = scenario_data.get("component", "unknown")
    
    # Determine action based on component and scenario
    if "cache" in component.lower() or "redis" in component.lower():
        action = "scale_out"
        parameters = {
            "nodes": f"{random.randint(2, 4)}→{random.randint(5, 8)}",
            "memory": f"{random.randint(8, 16)}GB→{random.randint(24, 64)}GB",
            "strategy": "gradual_scale",
            "region": "auto-select"
        }
    elif "database" in component.lower():
        action = "restart"
        parameters = {
            "connections": f"{random.randint(50, 100)}→{random.randint(150, 300)}",
            "timeout": f"{random.randint(30, 60)}s",
            "strategy": "rolling_restart",
            "maintenance_window": "immediate"
        }
    elif "kubernetes" in component.lower():
        action = "memory_limit_increase"
        parameters = {
            "memory": f"{random.randint(512, 1024)}Mi→{random.randint(2048, 4096)}Mi",
            "strategy": "pod_restart",
            "drain_timeout": f"{random.randint(5, 15)}m"
        }
    elif "api" in component.lower():
        action = "circuit_breaker"
        parameters = {
            "threshold": f"{random.randint(70, 85)}%",
            "window": f"{random.randint(3, 10)}m",
            "fallback": "cached_response",
            "retry_after": f"{random.randint(30, 120)}s"
        }
    elif "network" in component.lower():
        action = "failover"
        parameters = {
            "primary": "us-east-1",
            "secondary": "us-west-2",
            "timeout": f"{random.randint(10, 30)}s",
            "health_check": "enhanced"
        }
    elif "storage" in component.lower():
        action = "io_optimization"
        parameters = {
            "iops": f"{random.randint(1000, 3000)}→{random.randint(5000, 10000)}",
            "throughput": f"{random.randint(100, 250)}MB/s→{random.randint(500, 1000)}MB/s",
            "cache_size": f"{random.randint(8, 16)}GB→{random.randint(32, 64)}GB"
        }
    else:
        action = "investigate"
        parameters = {
            "priority": "high",
            "escalation": "tier2",
            "timeout": "30m"
        }
    
    # Calculate success rate from similar incidents
    if similar_incidents:
        success_count = sum(1 for inc in similar_incidents if inc["success"])
        success_rate = success_count / len(similar_incidents)
    else:
        success_rate = random.uniform(*config["success_rate_range"])
    
    # Calculate estimated impact
    if similar_incidents:
        avg_cost_savings = sum(inc["cost_savings"] for inc in similar_incidents) / len(similar_incidents)
        avg_resolution_time = sum(int(inc["resolution_time"].replace('m', '')) for inc in similar_incidents) / len(similar_incidents)
    else:
        avg_cost_savings = sum(config["cost_savings_range"]) / 2
        avg_resolution_time = sum(config["resolution_time_range"]) / 2
    
    return {
        "action": action,
        "component": component,
        "confidence": round(confidence, 3),
        "parameters": parameters,
        "source": "mock_analysis",
        "requires_enterprise": True,
        "advisory_only": True,
        "success_rate": round(success_rate, 3),
        "estimated_impact": {
            "cost_savings": int(avg_cost_savings),
            "resolution_time_minutes": int(avg_resolution_time),
            "users_protected": random.randint(*config["affected_users_range"]),
            "mttr_reduction": f"{random.randint(60, 80)}%"
        },
        "safety_checks": {
            "blast_radius": f"{random.randint(1, 3)} services",
            "business_hours": "compliant",
            "rollback_plan": "available",
            "approval_required": True,
            "risk_level": "medium" if confidence < 0.9 else "low"
        },
        "scenario_specific": True,
        "scenario_name": scenario_name
    }

def get_scenario_metrics(scenario_name: str) -> Dict[str, Any]:
    """
    Get dynamic metrics for a specific scenario
    
    Args:
        scenario_name: Name of the scenario
        
    Returns:
        Dictionary with scenario-specific metrics
    """
    config = get_scenario_config(scenario_name)
    
    # Generate dynamic values within ranges
    return {
        "detection_confidence": round(random.uniform(*config["detection_confidence_range"]), 3),
        "detection_time_seconds": random.randint(*config["detection_time_range"]),
        "accuracy": round(random.uniform(*config["accuracy_range"]), 3),
        "expected_similar_incidents": random.randint(*config["similar_incidents_range"]),
        "avg_similarity_score": round(random.uniform(*config["similarity_score_range"]), 3),
        "pattern_confidence": round(random.uniform(*config["pattern_confidence_range"]), 3),
        "success_rate": round(random.uniform(*config["success_rate_range"]), 3),
        "cost_savings_range": config["cost_savings_range"],
        "resolution_time_range": config["resolution_time_range"],
        "affected_users_range": config["affected_users_range"],
        "tags": config["tags"]
    }