core/cognitive_automation.py

"""
Zenith Platform Cognitive Automation Engine
AI-driven decision making and intelligent workflow optimization
"""

import asyncio
import logging
import pickle
from dataclasses import asdict, dataclass
from datetime import UTC, datetime, timedelta
from enum import Enum
from pathlib import Path
from typing import Any

# ML/AI imports
import mlflow
import mlflow.keras
import mlflow.sklearn

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class DecisionType(Enum):
    """Types of automated decisions"""

    FRAUD_ANALYSIS = "fraud_analysis"
    RISK_ASSESSMENT = "risk_assessment"
    COMPLIANCE_CHECK = "compliance_check"
    WORKFLOW_OPTIMIZATION = "workflow_optimization"
    RESOURCE_ALLOCATION = "resource_allocation"
    THREAT_RESPONSE = "threat_response"


class ConfidenceLevel(Enum):
    """Confidence levels for AI decisions"""

    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    VERY_HIGH = "very_high"


@dataclass
class CognitiveDecision:
    """AI-driven decision with reasoning"""

    decision_id: str
    decision_type: DecisionType
    confidence_level: ConfidenceLevel
    decision: str
    reasoning: list[str]
    evidence: dict[str, Any]
    alternatives: list[dict[str, Any]]
    risk_assessment: dict[str, Any]
    timestamp: datetime
    model_version: str
    processing_time: float
    human_override_required: bool = False
    human_override_reason: str | None = None

    def to_dict(self) -> dict[str, Any]:
        """Convert to dictionary for serialization"""
        data = asdict(self)
        data["decision_type"] = self.decision_type.value
        data["confidence_level"] = self.confidence_level.value
        data["timestamp"] = self.timestamp.isoformat()
        return data


@dataclass
class WorkflowOptimization:
    """Workflow optimization recommendation"""

    workflow_id: str
    current_efficiency: float
    recommended_efficiency: float
    optimization_actions: list[dict[str, Any]]
    estimated_savings: dict[str, float]  # time, cost, resources
    implementation_complexity: str
    confidence_score: float
    timestamp: datetime


class CognitiveAutomationEngine:
    """AI-powered decision making and workflow optimization"""

    def __init__(self):
        self.decision_models: dict[str, Any] = {}
        self.workflow_models: dict[str, Any] = {}
        self.decision_history: list[CognitiveDecision] = []
        self.optimization_history: list[WorkflowOptimization] = []
        self.confidence_thresholds = {
            ConfidenceLevel.LOW: 0.6,
            ConfidenceLevel.MEDIUM: 0.75,
            ConfidenceLevel.HIGH: 0.85,
            ConfidenceLevel.VERY_HIGH: 0.95,
        }

        # Initialize MLflow for tracking
        mlflow.set_experiment("zenith-cognitive-automation")

        # Load pre-trained models
        self._load_models()

    def _load_models(self):
        """Load pre-trained decision models"""
        model_dir = Path("models/cognitive")

        # Fraud analysis model
        fraud_model_path = model_dir / "fraud_analysis" / "model.pkl"
        if fraud_model_path.exists():
            with open(fraud_model_path, "rb") as f:
                self.decision_models["fraud_analysis"] = pickle.load(f)
            logger.info("Loaded fraud analysis model")

        # Risk assessment model
        risk_model_path = model_dir / "risk_assessment" / "model.pkl"
        if risk_model_path.exists():
            with open(risk_model_path, "rb") as f:
                self.decision_models["risk_assessment"] = pickle.load(f)
            logger.info("Loaded risk assessment model")

        # Workflow optimization model
        workflow_model_path = model_dir / "workflow_optimization" / "model.pkl"
        if workflow_model_path.exists():
            with open(workflow_model_path, "rb") as f:
                self.workflow_models["efficiency_prediction"] = pickle.load(f)
            logger.info("Loaded workflow optimization model")

    async def make_automated_decision(
        self,
        decision_type: DecisionType,
        input_data: dict[str, Any],
        context: dict[str, Any] | None = None,
    ) -> CognitiveDecision:
        """Make an automated decision using AI models"""

        start_time = datetime.now(UTC)
        decision_id = f"cog_{decision_type.value}_{int(start_time.timestamp())}_{hash(str(input_data)) % 10000}"

        try:
            with mlflow.start_run(run_name=f"cognitive_decision_{decision_id}"):
                # Log input parameters
                mlflow.log_param("decision_type", decision_type.value)
                mlflow.log_param("input_data_keys", list(input_data.keys()))
                if context:
                    mlflow.log_param("context_keys", list(context.keys()))

                # Process decision based on type
                if decision_type == DecisionType.FRAUD_ANALYSIS:
                    (
                        decision,
                        reasoning,
                        evidence,
                        alternatives,
                    ) = await self._analyze_fraud(input_data, context)
                elif decision_type == DecisionType.RISK_ASSESSMENT:
                    (
                        decision,
                        reasoning,
                        evidence,
                        alternatives,
                    ) = await self._assess_risk(input_data, context)
                elif decision_type == DecisionType.COMPLIANCE_CHECK:
                    (
                        decision,
                        reasoning,
                        evidence,
                        alternatives,
                    ) = await self._check_compliance(input_data, context)
                elif decision_type == DecisionType.THREAT_RESPONSE:
                    (
                        decision,
                        reasoning,
                        evidence,
                        alternatives,
                    ) = await self._respond_to_threat(input_data, context)
                else:
                    raise ValueError(f"Unsupported decision type: {decision_type}")

                # Calculate confidence and risk
                confidence_score = self._calculate_confidence_score(
                    decision_type, evidence
                )
                confidence_level = self._get_confidence_level(confidence_score)
                risk_assessment = self._assess_decision_risk(
                    decision, evidence, context
                )

                # Determine if human override is required
                human_override_required = self._requires_human_override(
                    confidence_level, risk_assessment, decision_type
                )

                processing_time = (datetime.now(UTC) - start_time).total_seconds()

                cognitive_decision = CognitiveDecision(
                    decision_id=decision_id,
                    decision_type=decision_type,
                    confidence_level=confidence_level,
                    decision=decision,
                    reasoning=reasoning,
                    evidence=evidence,
                    alternatives=alternatives,
                    risk_assessment=risk_assessment,
                    timestamp=start_time,
                    model_version=self._get_model_version(decision_type),
                    processing_time=processing_time,
                    human_override_required=human_override_required,
                )

                # Store decision history
                self.decision_history.append(cognitive_decision)

                # Log decision metrics
                mlflow.log_metric("confidence_score", confidence_score)
                mlflow.log_metric("processing_time", processing_time)
                mlflow.log_metric(
                    "human_override_required", 1 if human_override_required else 0
                )
                mlflow.log_param("final_decision", decision)
                mlflow.log_param("confidence_level", confidence_level.value)

                logger.info(
                    f"Cognitive decision made: {decision_id} - {decision} "
                    f"(confidence: {confidence_level.value})"
                )

                return cognitive_decision

        except Exception as e:
            logger.error(f"Cognitive decision failed: {e}")
            # Return a conservative decision requiring human review
            return CognitiveDecision(
                decision_id=decision_id,
                decision_type=decision_type,
                confidence_level=ConfidenceLevel.LOW,
                decision="REQUIRES_HUMAN_REVIEW",
                reasoning=[f"Decision failed due to error: {e!s}"],
                evidence={"error": str(e)},
                alternatives=[],
                risk_assessment={"error": True},
                timestamp=start_time,
                model_version="error_fallback",
                processing_time=(datetime.now(UTC) - start_time).total_seconds(),
                human_override_required=True,
                human_override_reason=f"Decision failed: {e!s}",
            )

    async def _analyze_fraud(
        self, data: dict[str, Any], context: dict[str, Any] | None = None
    ) -> tuple:
        """Analyze transaction for fraud using ML models"""
        # Extract features
        features = self._extract_fraud_features(data)

        # Use ML model for prediction
        model = self.decision_models.get("fraud_analysis")
        if model:
            prediction = model.predict_proba([features])[0]
            fraud_probability = prediction[1]

            if fraud_probability > 0.8:
                decision = "BLOCK_TRANSACTION"
                reasoning = [".2f.2f"]
            elif fraud_probability > 0.6:
                decision = "FLAG_FOR_REVIEW"
                reasoning = [".2f.2f"]
            else:
                decision = "APPROVE_TRANSACTION"
                reasoning = [".2f.2f"]
        else:
            # Fallback logic based on rules
            decision, reasoning = self._rule_based_fraud_analysis(data)

        evidence = {
            "fraud_probability": fraud_probability if model else None,
            "transaction_amount": data.get("amount"),
            "transaction_type": data.get("type"),
            "user_history": data.get("user_history", {}),
            "location_risk": data.get("location_risk", "unknown"),
        }

        alternatives = [
            {"action": "BLOCK_TRANSACTION", "reasoning": "Highest security"},
            {"action": "FLAG_FOR_REVIEW", "reasoning": "Balanced approach"},
            {"action": "APPROVE_TRANSACTION", "reasoning": "Minimal friction"},
        ]

        return decision, reasoning, evidence, alternatives

    def _rule_based_fraud_analysis(self, data: dict[str, Any]) -> tuple:
        """Fallback rule-based fraud analysis"""
        amount = data.get("amount", 0)
        user_history = data.get("user_history", {})
        location_risk = data.get("location_risk", "low")

        risk_score = 0

        # Amount-based rules
        if amount > 10000:
            risk_score += 3
        elif amount > 1000:
            risk_score += 1

        # Location-based rules
        if location_risk == "high":
            risk_score += 2
        elif location_risk == "medium":
            risk_score += 1

        # History-based rules
        failed_attempts = user_history.get("recent_failed_attempts", 0)
        risk_score += min(failed_attempts, 3)

        unusual_pattern = user_history.get("unusual_pattern", False)
        if unusual_pattern:
            risk_score += 2

        if risk_score >= 5:
            decision = "BLOCK_TRANSACTION"
            reasoning = [
                f"High risk score ({risk_score}/10) based on transaction patterns"
            ]
        elif risk_score >= 3:
            decision = "FLAG_FOR_REVIEW"
            reasoning = [f"Medium risk score ({risk_score}/10) requires manual review"]
        else:
            decision = "APPROVE_TRANSACTION"
            reasoning = [f"Low risk score ({risk_score}/10) transaction approved"]

        return decision, reasoning

    async def _assess_risk(
        self, data: dict[str, Any], context: dict[str, Any] | None = None
    ) -> tuple:
        """Assess overall risk level using ML models"""
        # Extract risk features
        features = self._extract_risk_features(data)

        # Use ML model for prediction
        model = self.decision_models.get("risk_assessment")
        risk_level = "medium"  # default

        if model:
            prediction = model.predict([features])[0]
            risk_level = prediction
        else:
            # Rule-based assessment
            risk_score = self._calculate_risk_score(data)
            if risk_score > 7:
                risk_level = "critical"
            elif risk_score > 5:
                risk_level = "high"
            elif risk_score > 3:
                risk_level = "medium"
            else:
                risk_level = "low"

        decision = f"RISK_LEVEL_{risk_level.upper()}"
        reasoning = [f"Overall risk assessment: {risk_level}"]
        evidence = {
            "risk_factors": data.get("risk_factors", []),
            "historical_data": data.get("historical_performance", {}),
            "external_signals": data.get("external_signals", []),
        }
        alternatives = [
            {"action": "RISK_LEVEL_LOW", "reasoning": "Minimal risk detected"},
            {
                "action": "RISK_LEVEL_MEDIUM",
                "reasoning": "Moderate risk requiring monitoring",
            },
            {"action": "RISK_LEVEL_HIGH", "reasoning": "High risk requiring action"},
            {
                "action": "RISK_LEVEL_CRITICAL",
                "reasoning": "Critical risk requiring immediate action",
            },
        ]

        return decision, reasoning, evidence, alternatives

    async def _check_compliance(
        self, data: dict[str, Any], context: dict[str, Any] | None = None
    ) -> tuple:
        """Check compliance with regulations using AI"""
        # This would integrate with regulatory databases and ML models
        compliance_issues = []

        # Check against OFAC, sanctions, etc.
        if data.get("sanctions_screening_required", True):
            sanctions_status = await self._check_sanctions_compliance(data)
            if not sanctions_status["compliant"]:
                compliance_issues.append("Sanctions screening failed")

        # Check transaction limits
        transaction_limits = await self._check_transaction_limits(data)
        if not transaction_limits["within_limits"]:
            compliance_issues.append(
                f"Transaction exceeds limits: {transaction_limits['violation_details']}"
            )

        # AML checks
        aml_status = await self._check_aml_compliance(data)
        if not aml_status["compliant"]:
            compliance_issues.extend(aml_status["issues"])

        if compliance_issues:
            decision = "COMPLIANCE_VIOLATION"
            reasoning = compliance_issues
        else:
            decision = "COMPLIANCE_PASSED"
            reasoning = ["All compliance checks passed"]

        evidence = {
            "sanctions_check": sanctions_status,
            "limits_check": transaction_limits,
            "aml_check": aml_status,
            "checked_at": datetime.now(UTC).isoformat(),
        }

        alternatives = [
            {"action": "COMPLIANCE_PASSED", "reasoning": "All checks passed"},
            {
                "action": "COMPLIANCE_VIOLATION",
                "reasoning": "Compliance issues detected",
            },
            {"action": "COMPLIANCE_REVIEW", "reasoning": "Manual review required"},
        ]

        return decision, reasoning, evidence, alternatives

    async def _respond_to_threat(
        self, data: dict[str, Any], context: dict[str, Any] | None = None
    ) -> tuple:
        """Respond to security threats using AI-driven analysis"""
        threat_level = data.get("threat_level", "unknown")
        data.get("threat_type", "unknown")

        # Analyze threat patterns and recommend response
        if threat_level == "critical":
            decision = "LOCKDOWN_SYSTEM"
            reasoning = ["Critical threat detected - initiating system lockdown"]
        elif threat_level == "high":
            decision = "ISOLATE_THREAT"
            reasoning = ["High threat detected - isolating affected systems"]
        elif threat_level == "medium":
            decision = "MONITOR_THREAT"
            reasoning = ["Medium threat detected - increasing monitoring"]
        else:
            decision = "LOG_THREAT"
            reasoning = ["Low threat detected - logging for analysis"]

        evidence = {
            "threat_indicators": data.get("threat_indicators", []),
            "affected_systems": data.get("affected_systems", []),
            "response_actions": data.get("recommended_actions", []),
        }

        alternatives = [
            {"action": "LOCKDOWN_SYSTEM", "reasoning": "Complete system protection"},
            {"action": "ISOLATE_THREAT", "reasoning": "Contain threat impact"},
            {"action": "MONITOR_THREAT", "reasoning": "Track threat evolution"},
            {"action": "LOG_THREAT", "reasoning": "Record for analysis"},
        ]

        return decision, reasoning, evidence, alternatives

    def _extract_fraud_features(self, data: dict[str, Any]) -> list[float]:
        """Extract features for fraud analysis"""
        return [
            float(data.get("amount", 0)),
            float(data.get("transaction_frequency", 0)),
            float(data.get("location_risk_score", 0)),
            float(data.get("user_history_score", 0)),
            float(data.get("time_anomaly_score", 0)),
            float(data.get("amount_anomaly_score", 0)),
        ]

    def _extract_risk_features(self, data: dict[str, Any]) -> list[float]:
        """Extract features for risk assessment"""
        return [
            float(data.get("financial_exposure", 0)),
            float(data.get("regulatory_risk", 0)),
            float(data.get("operational_risk", 0)),
            float(data.get("reputational_risk", 0)),
            float(data.get("historical_incidents", 0)),
            float(data.get("external_factors", 0)),
        ]

    def _calculate_confidence_score(
        self, decision_type: DecisionType, evidence: dict[str, Any]
    ) -> float:
        """Calculate confidence score for the decision"""
        base_confidence = 0.5

        # Add confidence based on evidence quality
        if evidence.get("fraud_probability") is not None:
            base_confidence += 0.3
        if len(evidence.get("risk_factors", [])) > 0:
            base_confidence += 0.2
        if evidence.get("historical_data"):
            base_confidence += 0.2

        # Cap at 0.95 for safety
        return min(base_confidence, 0.95)

    def _get_confidence_level(self, score: float) -> ConfidenceLevel:
        """Convert confidence score to confidence level"""
        if score >= self.confidence_thresholds[ConfidenceLevel.VERY_HIGH]:
            return ConfidenceLevel.VERY_HIGH
        elif score >= self.confidence_thresholds[ConfidenceLevel.HIGH]:
            return ConfidenceLevel.HIGH
        elif score >= self.confidence_thresholds[ConfidenceLevel.MEDIUM]:
            return ConfidenceLevel.MEDIUM
        else:
            return ConfidenceLevel.LOW

    def _assess_decision_risk(
        self,
        decision: str,
        evidence: dict[str, Any],
        context: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        """Assess the risk associated with the decision"""
        risk_factors = []

        # High-risk decisions
        if "BLOCK" in decision or "LOCKDOWN" in decision:
            risk_factors.append("Potential false positive impact")
            risk_factors.append("Business disruption risk")

        # Financial risk
        if evidence.get("transaction_amount", 0) > 50000:
            risk_factors.append("High financial exposure")

        # Compliance risk
        if "VIOLATION" in decision:
            risk_factors.append("Regulatory compliance risk")

        return {
            "risk_level": (
                "high"
                if len(risk_factors) > 2
                else "medium" if len(risk_factors) > 0 else "low"
            ),
            "risk_factors": risk_factors,
            "mitigation_required": len(risk_factors) > 0,
        }

    def _requires_human_override(
        self,
        confidence: ConfidenceLevel,
        risk_assessment: dict[str, Any],
        decision_type: DecisionType,
    ) -> bool:
        """Determine if decision requires human override"""
        # Always require human review for high-risk decisions
        if risk_assessment.get("risk_level") == "high":
            return True

        # Require review for low confidence critical decisions
        return bool(
            confidence in [ConfidenceLevel.LOW, ConfidenceLevel.MEDIUM]
            and decision_type
            in [DecisionType.THREAT_RESPONSE, DecisionType.COMPLIANCE_CHECK]
        )

    def _get_model_version(self, decision_type: DecisionType) -> str:
        """Get current model version for decision type"""
        # In a real implementation, this would check the model registry
        return f"{decision_type.value}_v1.0.0"

    async def optimize_workflow(
        self, workflow_data: dict[str, Any]
    ) -> WorkflowOptimization:
        """Optimize workflow using AI analysis"""
        workflow_id = workflow_data.get(
            "workflow_id", f"wf_{int(datetime.now(UTC).timestamp())}"
        )

        # Analyze current workflow efficiency
        current_efficiency = self._analyze_workflow_efficiency(workflow_data)

        # Predict optimized efficiency
        recommended_efficiency = self._predict_optimized_efficiency(workflow_data)

        # Generate optimization actions
        optimization_actions = self._generate_optimization_actions(workflow_data)

        # Calculate savings
        estimated_savings = self._calculate_workflow_savings(
            current_efficiency, recommended_efficiency, workflow_data
        )

        # Assess implementation complexity
        implementation_complexity = self._assess_implementation_complexity(
            optimization_actions
        )

        # Calculate confidence
        confidence_score = min(
            0.9, current_efficiency * 0.1 + 0.7
        )  # Simplified calculation

        optimization = WorkflowOptimization(
            workflow_id=workflow_id,
            current_efficiency=current_efficiency,
            recommended_efficiency=recommended_efficiency,
            optimization_actions=optimization_actions,
            estimated_savings=estimated_savings,
            implementation_complexity=implementation_complexity,
            confidence_score=confidence_score,
            timestamp=datetime.now(UTC),
        )

        self.optimization_history.append(optimization)

        logger.info(
            f"Workflow optimization completed for {workflow_id}: "
            f"{current_efficiency:.1%} -> {recommended_efficiency:.1%}"
        )

        return optimization

    def _analyze_workflow_efficiency(self, workflow_data: dict[str, Any]) -> float:
        """Analyze current workflow efficiency"""
        # Simplified efficiency calculation
        processing_time = workflow_data.get("avg_processing_time", 100)
        error_rate = workflow_data.get("error_rate", 0.05)
        resource_utilization = workflow_data.get("resource_utilization", 0.8)

        # Efficiency formula: lower time + lower errors + higher utilization = higher efficiency
        efficiency = (
            (1 / (1 + processing_time / 100)) * (1 - error_rate) * resource_utilization
        )
        return min(efficiency, 1.0)

    def _predict_optimized_efficiency(self, workflow_data: dict[str, Any]) -> float:
        """Predict efficiency after optimization"""
        current_efficiency = self._analyze_workflow_efficiency(workflow_data)

        # Assume 20-40% improvement potential
        improvement_factor = 1.3  # 30% improvement
        predicted_efficiency = min(current_efficiency * improvement_factor, 0.95)

        return predicted_efficiency

    def _generate_optimization_actions(
        self, workflow_data: dict[str, Any]
    ) -> list[dict[str, Any]]:
        """Generate workflow optimization actions"""
        actions = []

        # Analyze bottlenecks
        if workflow_data.get("avg_processing_time", 0) > 200:
            actions.append(
                {
                    "action": "parallel_processing",
                    "description": "Implement parallel processing for time-consuming tasks",
                    "estimated_impact": 0.25,
                    "complexity": "medium",
                }
            )

        if workflow_data.get("error_rate", 0) > 0.1:
            actions.append(
                {
                    "action": "error_handling_improvement",
                    "description": "Enhance error handling and retry mechanisms",
                    "estimated_impact": 0.15,
                    "complexity": "low",
                }
            )

        if workflow_data.get("resource_utilization", 1.0) < 0.7:
            actions.append(
                {
                    "action": "resource_optimization",
                    "description": "Optimize resource allocation and utilization",
                    "estimated_impact": 0.2,
                    "complexity": "medium",
                }
            )

        # Default optimization actions
        if not actions:
            actions.append(
                {
                    "action": "process_automation",
                    "description": "Automate manual workflow steps",
                    "estimated_impact": 0.3,
                    "complexity": "high",
                }
            )

        return actions

    def _calculate_workflow_savings(
        self, current_eff: float, optimized_eff: float, workflow_data: dict[str, Any]
    ) -> dict[str, float]:
        """Calculate estimated savings from workflow optimization"""
        improvement = optimized_eff - current_eff

        # Estimate time savings
        avg_processing_time = workflow_data.get("avg_processing_time", 100)
        daily_volume = workflow_data.get("daily_volume", 1000)
        time_savings_per_transaction = avg_processing_time * improvement
        daily_time_savings = time_savings_per_transaction * daily_volume / 3600  # hours

        # Estimate cost savings (assuming $50/hour labor cost)
        cost_savings_daily = daily_time_savings * 50
        cost_savings_monthly = cost_savings_daily * 30

        # Estimate resource savings
        resource_savings = improvement * 0.8  # Simplified calculation

        return {
            "time_hours_daily": daily_time_savings,
            "cost_dollars_monthly": cost_savings_monthly,
            "resource_utilization_improvement": resource_savings,
        }

    def _assess_implementation_complexity(self, actions: list[dict[str, Any]]) -> str:
        """Assess implementation complexity"""
        complexities = [action["complexity"] for action in actions]

        if "high" in complexities:
            return "high"
        elif "medium" in complexities:
            return "medium"
        else:
            return "low"

    def get_decision_history(self, days: int = 7) -> list[CognitiveDecision]:
        """Get decision history"""
        cutoff = datetime.now(UTC) - timedelta(days=days)
        return [d for d in self.decision_history if d.timestamp >= cutoff]

    def get_optimization_history(self, days: int = 7) -> list[WorkflowOptimization]:
        """Get optimization history"""
        cutoff = datetime.now(UTC) - timedelta(days=days)
        return [o for o in self.optimization_history if o.timestamp >= cutoff]

    def get_performance_metrics(self) -> dict[str, Any]:
        """Get cognitive automation performance metrics"""
        decisions = self.get_decision_history(30)  # Last 30 days
        optimizations = self.get_optimization_history(30)

        return {
            "total_decisions": len(decisions),
            "avg_confidence": sum(
                d.confidence_level.value.count("high") for d in decisions
            )
            / max(len(decisions), 1),
            "human_override_rate": sum(
                1 for d in decisions if d.human_override_required
            )
            / max(len(decisions), 1),
            "avg_processing_time": sum(d.processing_time for d in decisions)
            / max(len(decisions), 1),
            "total_optimizations": len(optimizations),
            "avg_efficiency_improvement": sum(
                (o.recommended_efficiency - o.current_efficiency) for o in optimizations
            )
            / max(len(optimizations), 1),
            "total_estimated_savings": sum(
                o.estimated_savings.get("cost_dollars_monthly", 0)
                for o in optimizations
            ),
        }


# Global cognitive automation engine instance
cognitive_engine = CognitiveAutomationEngine()


async def demonstrate_cognitive_automation():
    """Demonstrate cognitive automation capabilities"""
    logger.info("🚀 Demonstrating Zenith Cognitive Automation Engine")
    logger.info("=" * 60)

    # Example fraud analysis decision
    fraud_data = {
        "amount": 2500.00,
        "transaction_frequency": 0.8,
        "location_risk_score": 0.3,
        "user_history_score": 0.9,
        "time_anomaly_score": 0.1,
        "amount_anomaly_score": 0.2,
        "type": "wire_transfer",
        "user_history": {"recent_failed_attempts": 0, "unusual_pattern": False},
        "location_risk": "low",
    }

    logger.info("Making fraud analysis decision...")
    fraud_decision = await cognitive_engine.make_automated_decision(
        DecisionType.FRAUD_ANALYSIS, fraud_data
    )

    logger.info(f"Decision: {fraud_decision.decision}")
    logger.info(f"Confidence: {fraud_decision.confidence_level.value}")
    logger.info(f"Reasoning: {'; '.join(fraud_decision.reasoning)}")
    logger.info(f"Human override required: {fraud_decision.human_override_required}")

    # Example risk assessment
    risk_data = {
        "financial_exposure": 50000,
        "regulatory_risk": 0.2,
        "operational_risk": 0.3,
        "reputational_risk": 0.1,
        "historical_incidents": 2,
        "external_factors": 0.4,
        "risk_factors": ["market_volatility", "regulatory_changes"],
    }

    logger.info("\nMaking risk assessment decision...")
    risk_decision = await cognitive_engine.make_automated_decision(
        DecisionType.RISK_ASSESSMENT, risk_data
    )

    logger.info(f"Decision: {risk_decision.decision}")
    logger.info(f"Confidence: {risk_decision.confidence_level.value}")

    # Example workflow optimization
    workflow_data = {
        "workflow_id": "fraud_investigation_process",
        "avg_processing_time": 180,  # seconds
        "error_rate": 0.08,
        "resource_utilization": 0.75,
        "daily_volume": 500,
        "bottlenecks": ["manual_review", "document_collection"],
    }

    logger.info("\nOptimizing workflow...")
    optimization = await cognitive_engine.optimize_workflow(workflow_data)

    logger.info(f"Current efficiency: {optimization.current_efficiency:.1%}")
    logger.info(f"Recommended efficiency: {optimization.recommended_efficiency:.1%}")
    logger.info(
        f"Estimated monthly savings: ${optimization.estimated_savings['cost_dollars_monthly']:.0f}"
    )

    # Show performance metrics
    metrics = cognitive_engine.get_performance_metrics()
    logger.info("\nPerformance Metrics (30 days):")
    logger.info(f"Total decisions: {metrics['total_decisions']}")
    logger.info(f"Human override rate: {metrics['human_override_rate']:.1%}")
    logger.info(f"Average processing time: {metrics['avg_processing_time']:.2f}s")
    logger.info(f"Total optimizations: {metrics['total_optimizations']}")

    logger.info("\n✅ Cognitive automation demonstration completed!")


if __name__ == "__main__":
    asyncio.run(demonstrate_cognitive_automation())