# src/tools/quality_tools.py
from typing import Dict, List, Optional, Any
from typing_extensions import TypedDict  # If using TypedDict
from langchain_core.tools import tool
from datetime import datetime, timedelta
from ..utils.logger import setup_logger

logger = setup_logger(__name__)

class QualityTools:
    @tool
    def analyze_patient_satisfaction(
        self,
        satisfaction_scores: List[float],
        feedback_comments: List[str],
        department: Optional[str] = None
    ) -> Dict:
        """Analyze patient satisfaction scores and feedback"""
        try:
            analysis = {
                "metrics": {
                    "average_score": sum(satisfaction_scores) / len(satisfaction_scores),
                    "total_responses": len(satisfaction_scores),
                    "score_distribution": {},
                    "trend": "stable"
                },
                "feedback_analysis": {
                    "positive_themes": [],
                    "negative_themes": [],
                    "improvement_areas": []
                },
                "recommendations": []
            }
            
            # Analyze score distribution
            for score in satisfaction_scores:
                category = int(score)
                analysis["metrics"]["score_distribution"][category] = \
                    analysis["metrics"]["score_distribution"].get(category, 0) + 1
            
            # Basic sentiment analysis of feedback
            positive_keywords = ["great", "excellent", "good", "satisfied", "helpful"]
            negative_keywords = ["poor", "bad", "slow", "unhappy", "dissatisfied"]
            
            for comment in feedback_comments:
                comment_lower = comment.lower()
                
                # Analyze positive feedback
                for keyword in positive_keywords:
                    if keyword in comment_lower:
                        analysis["feedback_analysis"]["positive_themes"].append(keyword)
                
                # Analyze negative feedback
                for keyword in negative_keywords:
                    if keyword in comment_lower:
                        analysis["feedback_analysis"]["negative_themes"].append(keyword)
            
            # Generate recommendations
            if analysis["metrics"]["average_score"] < 7.0:
                analysis["recommendations"].append("Implement immediate satisfaction improvement plan")
            
            return analysis
            
        except Exception as e:
            logger.error(f"Error analyzing patient satisfaction: {str(e)}")
            raise

    @tool
    def monitor_clinical_outcomes(
        self,
        outcomes_data: List[Dict],
        benchmark_metrics: Dict[str, float]
    ) -> Dict:
        """Monitor and analyze clinical outcomes against benchmarks"""
        try:
            analysis = {
                "outcome_metrics": {},
                "benchmark_comparison": {},
                "critical_deviations": [],
                "success_areas": []
            }
            
            # Analyze outcomes by category
            for outcome in outcomes_data:
                category = outcome["category"]
                if category not in analysis["outcome_metrics"]:
                    analysis["outcome_metrics"][category] = {
                        "success_rate": 0,
                        "complication_rate": 0,
                        "readmission_rate": 0,
                        "total_cases": 0
                    }
                
                # Update metrics
                metrics = analysis["outcome_metrics"][category]
                metrics["total_cases"] += 1
                metrics["success_rate"] = (metrics["success_rate"] * (metrics["total_cases"] - 1) + 
                                         outcome["success"]) / metrics["total_cases"]
                
                # Compare with benchmarks
                if category in benchmark_metrics:
                    benchmark = benchmark_metrics[category]
                    deviation = metrics["success_rate"] - benchmark
                    
                    if deviation < -0.1:  # More than 10% below benchmark
                        analysis["critical_deviations"].append({
                            "category": category,
                            "deviation": deviation,
                            "current_rate": metrics["success_rate"],
                            "benchmark": benchmark
                        })
                    elif deviation > 0.05:  # More than 5% above benchmark
                        analysis["success_areas"].append({
                            "category": category,
                            "improvement": deviation,
                            "current_rate": metrics["success_rate"]
                        })
            
            return analysis
            
        except Exception as e:
            logger.error(f"Error monitoring clinical outcomes: {str(e)}")
            raise

    @tool
    def track_compliance_metrics(
        self,
        compliance_data: List[Dict],
        audit_period: str
    ) -> Dict:
        """Track and analyze compliance with medical standards and regulations"""
        try:
            analysis = {
                "compliance_rate": 0,
                "violations": [],
                "risk_areas": [],
                "audit_summary": {
                    "period": audit_period,
                    "total_checks": len(compliance_data),
                    "passed_checks": 0,
                    "failed_checks": 0
                }
            }
            
            # Analyze compliance checks
            for check in compliance_data:
                if check["compliant"]:
                    analysis["audit_summary"]["passed_checks"] += 1
                else:
                    analysis["audit_summary"]["failed_checks"] += 1
                    analysis["violations"].append({
                        "standard": check["standard"],
                        "severity": check["severity"],
                        "date": check["date"]
                    })
                
                # Identify risk areas
                if check["severity"] == "high" or check.get("repeat_violation", False):
                    analysis["risk_areas"].append({
                        "area": check["standard"],
                        "risk_level": "high",
                        "recommendations": ["Immediate action required", 
                                          "Staff training needed"]
                    })
            
            # Calculate overall compliance rate
            total_checks = analysis["audit_summary"]["total_checks"]
            if total_checks > 0:
                analysis["compliance_rate"] = (analysis["audit_summary"]["passed_checks"] / 
                                             total_checks * 100)
            
            return analysis
            
        except Exception as e:
            logger.error(f"Error tracking compliance metrics: {str(e)}")
            raise# quality_tools implementation