import asyncio
from typing import Dict, List, Any
from dataclasses import dataclass
from monitoring_models import AgentSpecialization
from models import ReliabilityEvent, AnomalyResult

@dataclass
class AgentResult:
    specialization: AgentSpecialization
    confidence: float
    findings: Dict[str, Any]
    recommendations: List[str]
    processing_time: float

class BaseAgent:
    def __init__(self, specialization: AgentSpecialization):
        self.specialization = specialization
        self.performance_metrics = {
            'processed_events': 0,
            'successful_analyses': 0,
            'average_confidence': 0.0
        }
    
    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
        """Base analysis method to be implemented by specialized agents"""
        raise NotImplementedError

class AnomalyDetectionAgent(BaseAgent):
    def __init__(self):
        super().__init__(AgentSpecialization.DETECTIVE)
        self.adaptive_thresholds = {}
    
    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
        """Enhanced anomaly detection with pattern recognition"""
        start_time = asyncio.get_event_loop().time()
        
        # Multi-dimensional anomaly scoring
        anomaly_score = self._calculate_anomaly_score(event)
        pattern_match = self._detect_known_patterns(event)
        
        return AgentResult(
            specialization=self.specialization,
            confidence=anomaly_score,
            findings={
                'anomaly_score': anomaly_score,
                'detected_patterns': pattern_match,
                'affected_metrics': self._identify_affected_metrics(event),
                'severity_tier': self._classify_severity(anomaly_score)
            },
            recommendations=self._generate_detection_recommendations(event, anomaly_score),
            processing_time=asyncio.get_event_loop().time() - start_time
        )
    
    def _calculate_anomaly_score(self, event: ReliabilityEvent) -> float:
        """Calculate comprehensive anomaly score (0-1)"""
        scores = []
        
        # Latency anomaly (weighted 40%)
        if event.latency_p99 > 150:
            latency_score = min(1.0, (event.latency_p99 - 150) / 500)
            scores.append(0.4 * latency_score)
        
        # Error rate anomaly (weighted 30%)
        if event.error_rate > 0.05:
            error_score = min(1.0, event.error_rate / 0.3)
            scores.append(0.3 * error_score)
        
        # Resource anomaly (weighted 30%)
        resource_score = 0
        if event.cpu_util and event.cpu_util > 0.8:
            resource_score += 0.15 * min(1.0, (event.cpu_util - 0.8) / 0.2)
        if event.memory_util and event.memory_util > 0.8:
            resource_score += 0.15 * min(1.0, (event.memory_util - 0.8) / 0.2)
        scores.append(resource_score)
        
        return min(1.0, sum(scores))

class RootCauseAgent(BaseAgent):
    def __init__(self):
        super().__init__(AgentSpecialization.DIAGNOSTICIAN)
        self.causal_patterns = self._load_causal_patterns()
    
    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
        """AI-powered root cause analysis"""
        start_time = asyncio.get_event_loop().time()
        
        root_cause_analysis = self._perform_causal_analysis(event)
        
        return AgentResult(
            specialization=self.specialization,
            confidence=root_cause_analysis['confidence'],
            findings={
                'likely_root_causes': root_cause_analysis['causes'],
                'evidence_patterns': root_cause_analysis['evidence'],
                'dependency_analysis': self._analyze_dependencies(event),
                'timeline_correlation': self._check_temporal_patterns(event)
            },
            recommendations=root_cause_analysis['investigation_steps'],
            processing_time=asyncio.get_event_loop().time() - start_time
        )

class OrchestrationManager:
    def __init__(self):
        self.agents = {
            AgentSpecialization.DETECTIVE: AnomalyDetectionAgent(),
            AgentSpecialization.DIAGNOSTICIAN: RootCauseAgent(),
            # Add more agents as we build them
        }
        self.incident_history = []
    
    async def orchestrate_analysis(self, event: ReliabilityEvent) -> Dict[str, Any]:
        """Coordinate multiple agents for comprehensive analysis"""
        agent_tasks = {
            spec: agent.analyze(event)
            for spec, agent in self.agents.items()
        }
        
        # Parallel agent execution
        agent_results = {}
        for specialization, task in agent_tasks.items():
            try:
                result = await asyncio.wait_for(task, timeout=10.0)
                agent_results[specialization.value] = result
            except asyncio.TimeoutError:
                # Agent timeout - continue with others
                continue
        
        # Synthesize results
        return self._synthesize_agent_findings(event, agent_results)
    
    def _synthesize_agent_findings(self, event: ReliabilityEvent, agent_results: Dict) -> Dict[str, Any]:
        """Combine insights from all specialized agents"""
        detective_result = agent_results.get(AgentSpecialization.DETECTIVE.value)
        diagnostician_result = agent_results.get(AgentSpecialization.DIAGNOSTICIAN.value)
        
        if not detective_result:
            return {'error': 'No agent results available'}
        
        # Build comprehensive analysis
        synthesis = {
            'incident_summary': {
                'severity': detective_result.findings.get('severity_tier', 'UNKNOWN'),
                'anomaly_confidence': detective_result.confidence,
                'primary_metrics_affected': detective_result.findings.get('affected_metrics', [])
            },
            'root_cause_insights': diagnostician_result.findings if diagnostician_result else {},
            'recommended_actions': self._prioritize_actions(
                detective_result.recommendations,
                diagnostician_result.recommendations if diagnostician_result else []
            ),
            'business_context': self._add_business_context(event, detective_result.confidence),
            'agent_metadata': {
                'participating_agents': list(agent_results.keys()),
                'processing_times': {k: v.processing_time for k, v in agent_results.items()}
            }
        }
        
        return synthesis