backend/production_logging.py

"""
Production Logging Infrastructure
Structured logging with medical-specific fields and compliance features

Features:
- JSON-structured logging for machine parsing
- Medical-specific log fields (PHI anonymization, confidence scores)
- Log levels with appropriate categorization
- Security event logging
- Compliance-ready log retention
- Centralized log aggregation support

Author: MiniMax Agent
Date: 2025-10-29
Version: 1.0.0
"""

import logging
import json
import hashlib
from typing import Dict, Any, Optional
from datetime import datetime
from enum import Enum
import traceback


class LogLevel(Enum):
    """Standard log levels"""
    DEBUG = "DEBUG"
    INFO = "INFO"
    WARNING = "WARNING"
    ERROR = "ERROR"
    CRITICAL = "CRITICAL"


class EventCategory(Enum):
    """Event categories for medical AI platform"""
    AUTHENTICATION = "authentication"
    AUTHORIZATION = "authorization"
    PHI_ACCESS = "phi_access"
    MODEL_INFERENCE = "model_inference"
    DATA_PROCESSING = "data_processing"
    SYSTEM_EVENT = "system_event"
    SECURITY_EVENT = "security_event"
    COMPLIANCE_EVENT = "compliance_event"
    PERFORMANCE_EVENT = "performance_event"
    ERROR_EVENT = "error_event"


class MedicalLogger:
    """
    Medical-grade structured logger with compliance features
    Implements HIPAA-compliant logging with PHI protection
    """
    
    def __init__(
        self,
        service_name: str,
        environment: str = "production"
    ):
        self.service_name = service_name
        self.environment = environment
        self.logger = logging.getLogger(service_name)
        self.logger.setLevel(logging.DEBUG)
        
        # Setup JSON formatter
        self._setup_json_handler()
        
        # Track logging statistics
        self.log_counts = {level.value: 0 for level in LogLevel}
    
    def _setup_json_handler(self):
        """Setup JSON-formatted log handler"""
        handler = logging.StreamHandler()
        handler.setLevel(logging.DEBUG)
        
        # Custom formatter for JSON output
        formatter = logging.Formatter(
            '{"timestamp": "%(asctime)s", "level": "%(levelname)s", '
            '"service": "%(name)s", "message": "%(message)s"}'
        )
        handler.setFormatter(formatter)
        
        self.logger.addHandler(handler)
    
    def _anonymize_phi(self, data: Any) -> Any:
        """Anonymize PHI in log data"""
        if isinstance(data, dict):
            anonymized = {}
            phi_fields = ["patient_id", "patient_name", "ssn", "mrn", "email", "phone"]
            
            for key, value in data.items():
                if any(phi_field in key.lower() for phi_field in phi_fields):
                    # Hash PHI fields
                    if isinstance(value, str):
                        anonymized[key] = hashlib.sha256(value.encode()).hexdigest()[:16]
                    else:
                        anonymized[key] = "[REDACTED]"
                elif isinstance(value, (dict, list)):
                    anonymized[key] = self._anonymize_phi(value)
                else:
                    anonymized[key] = value
            
            return anonymized
        
        elif isinstance(data, list):
            return [self._anonymize_phi(item) for item in data]
        
        return data
    
    def _create_log_entry(
        self,
        level: LogLevel,
        message: str,
        category: EventCategory,
        details: Optional[Dict[str, Any]] = None,
        user_id: Optional[str] = None,
        document_id: Optional[str] = None,
        model_id: Optional[str] = None,
        confidence: Optional[float] = None,
        anonymize: bool = True
    ) -> Dict[str, Any]:
        """Create structured log entry"""
        
        log_entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "level": level.value,
            "service": self.service_name,
            "environment": self.environment,
            "category": category.value,
            "message": message
        }
        
        # Add optional fields
        if user_id:
            log_entry["user_id"] = user_id
        
        if document_id:
            log_entry["document_id"] = document_id
        
        if model_id:
            log_entry["model_id"] = model_id
        
        if confidence is not None:
            log_entry["confidence"] = confidence
        
        if details:
            # Anonymize PHI if requested
            if anonymize:
                details = self._anonymize_phi(details)
            log_entry["details"] = details
        
        return log_entry
    
    def log(
        self,
        level: LogLevel,
        message: str,
        category: EventCategory = EventCategory.SYSTEM_EVENT,
        **kwargs
    ):
        """Generic log method"""
        log_entry = self._create_log_entry(level, message, category, **kwargs)
        
        # Increment counter
        self.log_counts[level.value] += 1
        
        # Log at appropriate level
        if level == LogLevel.DEBUG:
            self.logger.debug(json.dumps(log_entry))
        elif level == LogLevel.INFO:
            self.logger.info(json.dumps(log_entry))
        elif level == LogLevel.WARNING:
            self.logger.warning(json.dumps(log_entry))
        elif level == LogLevel.ERROR:
            self.logger.error(json.dumps(log_entry))
        elif level == LogLevel.CRITICAL:
            self.logger.critical(json.dumps(log_entry))
    
    def info(self, message: str, category: EventCategory = EventCategory.SYSTEM_EVENT, **kwargs):
        """Log info message"""
        self.log(LogLevel.INFO, message, category, **kwargs)
    
    def warning(self, message: str, category: EventCategory = EventCategory.SYSTEM_EVENT, **kwargs):
        """Log warning message"""
        self.log(LogLevel.WARNING, message, category, **kwargs)
    
    def error(self, message: str, category: EventCategory = EventCategory.ERROR_EVENT, **kwargs):
        """Log error message"""
        self.log(LogLevel.ERROR, message, category, **kwargs)
    
    def critical(self, message: str, category: EventCategory = EventCategory.ERROR_EVENT, **kwargs):
        """Log critical message"""
        self.log(LogLevel.CRITICAL, message, category, **kwargs)
    
    def debug(self, message: str, category: EventCategory = EventCategory.SYSTEM_EVENT, **kwargs):
        """Log debug message"""
        self.log(LogLevel.DEBUG, message, category, **kwargs)
    
    def log_authentication(
        self,
        user_id: str,
        success: bool,
        ip_address: str,
        details: Optional[Dict[str, Any]] = None
    ):
        """Log authentication event"""
        message = f"Authentication {'successful' if success else 'failed'} for user {user_id}"
        
        self.log(
            LogLevel.INFO if success else LogLevel.WARNING,
            message,
            EventCategory.AUTHENTICATION,
            user_id=user_id,
            details={
                "ip_address": ip_address,
                "success": success,
                **(details or {})
            }
        )
    
    def log_phi_access(
        self,
        user_id: str,
        document_id: str,
        action: str,
        ip_address: str,
        details: Optional[Dict[str, Any]] = None
    ):
        """Log PHI access event (HIPAA requirement)"""
        message = f"PHI access: {action} on document {document_id} by user {user_id}"
        
        self.log(
            LogLevel.INFO,
            message,
            EventCategory.PHI_ACCESS,
            user_id=user_id,
            document_id=document_id,
            details={
                "action": action,
                "ip_address": ip_address,
                **(details or {})
            },
            anonymize=False  # PHI access logs must be complete
        )
    
    def log_model_inference(
        self,
        model_id: str,
        document_id: str,
        confidence: float,
        duration_seconds: float,
        success: bool,
        details: Optional[Dict[str, Any]] = None
    ):
        """Log model inference event"""
        message = f"Model inference: {model_id} on {document_id} ({'success' if success else 'failed'})"
        
        self.log(
            LogLevel.INFO,
            message,
            EventCategory.MODEL_INFERENCE,
            document_id=document_id,
            model_id=model_id,
            confidence=confidence,
            details={
                "duration_seconds": duration_seconds,
                "success": success,
                **(details or {})
            }
        )
    
    def log_security_event(
        self,
        event_type: str,
        severity: str,
        user_id: Optional[str] = None,
        ip_address: Optional[str] = None,
        details: Optional[Dict[str, Any]] = None
    ):
        """Log security event"""
        message = f"Security event: {event_type} (severity: {severity})"
        
        level = LogLevel.CRITICAL if severity == "high" else LogLevel.WARNING
        
        self.log(
            level,
            message,
            EventCategory.SECURITY_EVENT,
            user_id=user_id,
            details={
                "event_type": event_type,
                "severity": severity,
                "ip_address": ip_address,
                **(details or {})
            }
        )
    
    def log_exception(
        self,
        exception: Exception,
        context: str,
        user_id: Optional[str] = None,
        document_id: Optional[str] = None
    ):
        """Log exception with stack trace"""
        message = f"Exception in {context}: {str(exception)}"
        
        self.log(
            LogLevel.ERROR,
            message,
            EventCategory.ERROR_EVENT,
            user_id=user_id,
            document_id=document_id,
            details={
                "exception_type": type(exception).__name__,
                "exception_message": str(exception),
                "stack_trace": traceback.format_exc(),
                "context": context
            }
        )
    
    def get_log_statistics(self) -> Dict[str, int]:
        """Get logging statistics"""
        return dict(self.log_counts)


# Global logger instance
_medical_logger = None


def get_medical_logger(service_name: str = "medical_ai_platform") -> MedicalLogger:
    """Get singleton medical logger instance"""
    global _medical_logger
    if _medical_logger is None:
        _medical_logger = MedicalLogger(service_name)
    return _medical_logger