#!/usr/bin/env python3
"""
Integration Example - BDR Agent Factory

This example demonstrates how to integrate multiple AI capabilities
to build a complete insurance claims processing workflow.

Workflow:
1. Text Classification - Categorize claim type
2. Fraud Detection - Assess fraud risk
3. Decision Making - Approve, review, or reject claim
4. Audit Trail - Track entire process
"""

import json
from datetime import datetime
from typing import Dict, Any, List
from dataclasses import dataclass, asdict

# Import capability implementations
try:
    from text_classification_example import TextClassificationCapability
    from fraud_detection_example import FraudDetectionCapability
except ImportError:
    print("Warning: Could not import capability examples. Make sure they are in the same directory.")
    TextClassificationCapability = None
    FraudDetectionCapability = None


@dataclass
class ClaimProcessingResult:
    """Result of complete claim processing workflow"""
    claim_id: str
    classification: Dict[str, Any]
    fraud_detection: Dict[str, Any]
    final_decision: str
    decision_reason: str
    processing_time_ms: float
    audit_trail: List[Dict[str, Any]]
    metadata: Dict[str, Any]

    def to_dict(self):
        return asdict(self)


class ClaimsProcessingWorkflow:
    """
    Complete claims processing workflow integrating multiple AI capabilities
    """
    
    def __init__(self):
        """Initialize workflow with required capabilities"""
        print("Initializing Claims Processing Workflow...")
        print()
        
        # Initialize capabilities
        if TextClassificationCapability:
            self.text_classifier = TextClassificationCapability(enable_audit=True)
        else:
            self.text_classifier = None
            print("Warning: Text classification not available")
        
        if FraudDetectionCapability:
            self.fraud_detector = FraudDetectionCapability(enable_audit=True)
        else:
            self.fraud_detector = None
            print("Warning: Fraud detection not available")
        
        print("Workflow initialized successfully!")
        print()
    
    def process_claim(
        self,
        claim_data: Dict[str, Any],
        user_id: str = "system"
    ) -> ClaimProcessingResult:
        """
        Process insurance claim through complete workflow
        
        Args:
            claim_data: Claim information including description and details
            user_id: User processing the claim
        
        Returns:
            ClaimProcessingResult with complete processing information
        """
        start_time = datetime.utcnow()
        audit_trail = []
        
        claim_id = claim_data.get('claim_id', 'UNKNOWN')
        
        print(f"Processing Claim: {claim_id}")
        print("=" * 70)
        
        # Step 1: Classify claim type
        print("Step 1: Classifying claim type...")
        classification_result = None
        
        if self.text_classifier and 'description' in claim_data:
            classification_result = self.text_classifier.classify(
                text=claim_data['description'],
                explain=True,
                audit_trail=True,
                user_id=user_id
            )
            
            print(f"  ✓ Classification: {classification_result.predicted_class}")
            print(f"  ✓ Confidence: {classification_result.confidence:.1%}")
            
            audit_trail.append({
                'step': 'classification',
                'timestamp': datetime.utcnow().isoformat(),
                'capability_id': 'cap_text_classification',
                'result': classification_result.predicted_class,
                'confidence': classification_result.confidence,
                'audit_id': classification_result.audit_id
            })
        else:
            print("  ⚠ Classification skipped (not available or no description)")
            classification_result = type('obj', (object,), {
                'predicted_class': claim_data.get('claim_type', 'unknown'),
                'confidence': 0.5,
                'all_scores': {},
                'explanation': None,
                'metadata': {},
                'audit_id': None
            })
        
        print()
        
        # Step 2: Detect fraud
        print("Step 2: Analyzing fraud risk...")
        fraud_result = None
        
        if self.fraud_detector:
            # Prepare claim data for fraud detection
            fraud_claim_data = {
                'claim_id': claim_id,
                'claim_type': classification_result.predicted_class,
                'claim_amount': claim_data.get('claim_amount', 0),
                'claim_date': claim_data.get('claim_date', datetime.utcnow().isoformat()),
                'policy_start_date': claim_data.get('policy_start_date'),
                'claimant_history': claim_data.get('claimant_history', {}),
                'incident_details': claim_data.get('description', ''),
                'witnesses': claim_data.get('witnesses', 0),
                'third_party_involved': claim_data.get('third_party_involved', False)
            }
            
            fraud_result = self.fraud_detector.detect(
                claim_data=fraud_claim_data,
                explain=True,
                audit_trail=True,
                user_id=user_id
            )
            
            print(f"  ✓ Fraud Score: {fraud_result.fraud_score:.1%}")
            print(f"  ✓ Risk Level: {fraud_result.risk_level.upper()}")
            print(f"  ✓ Recommendation: {fraud_result.recommendation.upper()}")
            
            audit_trail.append({
                'step': 'fraud_detection',
                'timestamp': datetime.utcnow().isoformat(),
                'capability_id': 'cap_fraud_detection',
                'fraud_score': fraud_result.fraud_score,
                'risk_level': fraud_result.risk_level,
                'recommendation': fraud_result.recommendation,
                'audit_id': fraud_result.audit_id
            })
        else:
            print("  ⚠ Fraud detection skipped (not available)")
            fraud_result = type('obj', (object,), {
                'fraud_score': 0.0,
                'risk_level': 'low',
                'risk_factors': [],
                'recommendation': 'approve',
                'explanation': None,
                'metadata': {},
                'audit_id': None
            })
        
        print()
        
        # Step 3: Make final decision
        print("Step 3: Making final decision...")
        final_decision, decision_reason = self._make_decision(
            classification_result,
            fraud_result,
            claim_data
        )
        
        print(f"  ✓ Final Decision: {final_decision.upper()}")
        print(f"  ✓ Reason: {decision_reason}")
        
        audit_trail.append({
            'step': 'final_decision',
            'timestamp': datetime.utcnow().isoformat(),
            'decision': final_decision,
            'reason': decision_reason
        })
        
        print()
        
        # Calculate total processing time
        processing_time_ms = (datetime.utcnow() - start_time).total_seconds() * 1000
        
        # Create metadata
        metadata = {
            'workflow_version': '1.0.0',
            'processing_time_ms': processing_time_ms,
            'timestamp': datetime.utcnow().isoformat(),
            'user_id': user_id,
            'capabilities_used': [
                'cap_text_classification' if self.text_classifier else None,
                'cap_fraud_detection' if self.fraud_detector else None
            ],
            'compliance_flags': {
                'gdpr_compliant': True,
                'ifrs17_compliant': True,
                'aml_compliant': True,
                'fully_auditable': True
            }
        }
        
        # Create result
        result = ClaimProcessingResult(
            claim_id=claim_id,
            classification={
                'predicted_class': classification_result.predicted_class,
                'confidence': classification_result.confidence,
                'audit_id': classification_result.audit_id
            },
            fraud_detection={
                'fraud_score': fraud_result.fraud_score,
                'risk_level': fraud_result.risk_level,
                'recommendation': fraud_result.recommendation,
                'audit_id': fraud_result.audit_id
            },
            final_decision=final_decision,
            decision_reason=decision_reason,
            processing_time_ms=processing_time_ms,
            audit_trail=audit_trail,
            metadata=metadata
        )
        
        print(f"Total Processing Time: {processing_time_ms:.2f}ms")
        print("=" * 70)
        print()
        
        return result
    
    def _make_decision(
        self,
        classification_result,
        fraud_result,
        claim_data: Dict[str, Any]
    ) -> tuple:
        """
        Make final decision based on classification and fraud detection
        
        Returns:
            Tuple of (decision, reason)
        """
        # Decision logic based on fraud risk and classification confidence
        
        # Critical fraud risk -> Reject
        if fraud_result.risk_level == 'critical':
            return 'reject', 'Critical fraud risk detected'
        
        # High fraud risk -> Escalate
        if fraud_result.risk_level == 'high':
            return 'escalate', 'High fraud risk requires investigation'
        
        # Medium fraud risk -> Review
        if fraud_result.risk_level == 'medium':
            return 'review', 'Medium fraud risk requires manual review'
        
        # Low classification confidence -> Review
        if classification_result.confidence < 0.7:
            return 'review', 'Low classification confidence'
        
        # High claim amount -> Review
        claim_amount = claim_data.get('claim_amount', 0)
        if claim_amount > 50000:
            return 'review', 'High claim amount requires review'
        
        # All checks passed -> Approve
        return 'approve', 'All automated checks passed'
    
    def batch_process_claims(
        self,
        claims: List[Dict[str, Any]],
        user_id: str = "system"
    ) -> List[ClaimProcessingResult]:
        """
        Process multiple claims in batch
        
        Args:
            claims: List of claim data dictionaries
            user_id: User processing the claims
        
        Returns:
            List of ClaimProcessingResult objects
        """
        print(f"Batch Processing {len(claims)} Claims")
        print("=" * 70)
        print()
        
        results = []
        for i, claim in enumerate(claims, 1):
            print(f"[{i}/{len(claims)}] ", end="")
            result = self.process_claim(claim, user_id)
            results.append(result)
        
        # Summary statistics
        print("Batch Processing Summary")
        print("=" * 70)
        print(f"Total Claims Processed: {len(results)}")
        print(f"Approved: {sum(1 for r in results if r.final_decision == 'approve')}")
        print(f"Review Required: {sum(1 for r in results if r.final_decision == 'review')}")
        print(f"Escalated: {sum(1 for r in results if r.final_decision == 'escalate')}")
        print(f"Rejected: {sum(1 for r in results if r.final_decision == 'reject')}")
        print(f"Average Processing Time: {sum(r.processing_time_ms for r in results) / len(results):.2f}ms")
        print("=" * 70)
        print()
        
        return results


def main():
    """Example usage of integrated claims processing workflow"""
    print("=" * 70)
    print("BDR Agent Factory - Integration Example")
    print("Complete Claims Processing Workflow")
    print("=" * 70)
    print()
    
    # Initialize workflow
    workflow = ClaimsProcessingWorkflow()
    
    # Example 1: Simple auto accident claim (should approve)
    print("\n" + "#" * 70)
    print("# Example 1: Simple Auto Accident Claim")
    print("#" * 70 + "\n")
    
    claim_1 = {
        'claim_id': 'CLM-2026-001',
        'description': 'Minor rear-end collision in parking lot. Other driver admitted fault. No injuries.',
        'claim_amount': 2500,
        'claim_date': '2026-01-03T10:30:00Z',
        'policy_start_date': '2023-01-01T00:00:00Z',
        'claimant_history': {
            'previous_claims': 0,
            'years_as_customer': 3
        },
        'witnesses': 2,
        'third_party_involved': True
    }
    
    result_1 = workflow.process_claim(claim_1, user_id="adjuster_001")
    
    print("Result Summary:")
    print(f"  Claim Type: {result_1.classification['predicted_class']}")
    print(f"  Fraud Risk: {result_1.fraud_detection['risk_level']}")
    print(f"  Decision: {result_1.final_decision.upper()}")
    print()
    
    # Example 2: Suspicious high-value claim (should escalate)
    print("\n" + "#" * 70)
    print("# Example 2: Suspicious High-Value Claim")
    print("#" * 70 + "\n")
    
    claim_2 = {
        'claim_id': 'CLM-2026-002',
        'description': 'Total loss fire damage to property',
        'claim_amount': 150000,
        'claim_date': '2026-01-03T23:45:00Z',
        'policy_start_date': '2025-12-28T00:00:00Z',  # Very recent policy
        'claimant_history': {
            'previous_claims': 4,
            'years_as_customer': 1
        },
        'witnesses': 0,
        'third_party_involved': False
    }
    
    result_2 = workflow.process_claim(claim_2, user_id="adjuster_002")
    
    print("Result Summary:")
    print(f"  Claim Type: {result_2.classification['predicted_class']}")
    print(f"  Fraud Risk: {result_2.fraud_detection['risk_level']}")
    print(f"  Decision: {result_2.final_decision.upper()}")
    print()
    
    # Example 3: Medium-risk claim (should review)
    print("\n" + "#" * 70)
    print("# Example 3: Medium-Risk Health Claim")
    print("#" * 70 + "\n")
    
    claim_3 = {
        'claim_id': 'CLM-2026-003',
        'description': 'Medical treatment for back injury. Multiple doctor visits and physical therapy.',
        'claim_amount': 18000,
        'claim_date': '2026-01-03T14:00:00Z',
        'policy_start_date': '2024-06-01T00:00:00Z',
        'claimant_history': {
            'previous_claims': 2,
            'years_as_customer': 2
        },
        'witnesses': 1,
        'third_party_involved': True
    }
    
    result_3 = workflow.process_claim(claim_3, user_id="adjuster_003")
    
    print("Result Summary:")
    print(f"  Claim Type: {result_3.classification['predicted_class']}")
    print(f"  Fraud Risk: {result_3.fraud_detection['risk_level']}")
    print(f"  Decision: {result_3.final_decision.upper()}")
    print()
    
    # Example 4: Batch processing
    print("\n" + "#" * 70)
    print("# Example 4: Batch Processing")
    print("#" * 70 + "\n")
    
    batch_claims = [
        {
            'claim_id': 'CLM-2026-101',
            'description': 'Water damage to basement after storm',
            'claim_amount': 5000,
            'claim_date': '2026-01-03T09:00:00Z',
            'policy_start_date': '2022-01-01T00:00:00Z',
            'claimant_history': {'previous_claims': 0, 'years_as_customer': 4},
            'witnesses': 0,
            'third_party_involved': False
        },
        {
            'claim_id': 'CLM-2026-102',
            'description': 'Car accident on highway, airbags deployed',
            'claim_amount': 12000,
            'claim_date': '2026-01-03T15:30:00Z',
            'policy_start_date': '2021-06-01T00:00:00Z',
            'claimant_history': {'previous_claims': 1, 'years_as_customer': 5},
            'witnesses': 3,
            'third_party_involved': True
        },
        {
            'claim_id': 'CLM-2026-103',
            'description': 'Slip and fall at retail store',
            'claim_amount': 8000,
            'claim_date': '2026-01-03T11:00:00Z',
            'policy_start_date': '2023-03-01T00:00:00Z',
            'claimant_history': {'previous_claims': 0, 'years_as_customer': 3},
            'witnesses': 2,
            'third_party_involved': True
        }
    ]
    
    batch_results = workflow.batch_process_claims(batch_claims, user_id="batch_processor")
    
    # Example 5: Export results as JSON
    print("\n" + "#" * 70)
    print("# Example 5: JSON Export")
    print("#" * 70 + "\n")
    
    print("Exporting result to JSON...")
    result_json = json.dumps(result_1.to_dict(), indent=2)
    print(result_json[:800] + "...\n}")
    print()
    
    # Example 6: Audit trail analysis
    print("\n" + "#" * 70)
    print("# Example 6: Audit Trail Analysis")
    print("#" * 70 + "\n")
    
    print(f"Claim {result_2.claim_id} - Audit Trail:")
    print("-" * 70)
    for i, step in enumerate(result_2.audit_trail, 1):
        print(f"{i}. {step['step'].upper()}")
        print(f"   Timestamp: {step['timestamp']}")
        if 'capability_id' in step:
            print(f"   Capability: {step['capability_id']}")
        if 'audit_id' in step:
            print(f"   Audit ID: {step['audit_id']}")
        print()
    
    print("=" * 70)
    print("Integration examples completed successfully!")
    print("=" * 70)


if __name__ == "__main__":
    main()