class IntentRecognition:
    """Intent recognition for voice AI systems"""
    
    def __init__(self):
        self.intents = {
            "check_balance": ["check balance", "account balance", "how much money"],
            "make_payment": ["pay bill", "make payment", "pay invoice"],
            "technical_support": ["technical help", "support", "problem with service"],
            "schedule_appointment": ["book appointment", "schedule meeting", "make reservation"]
        }
    
    def recognize_intent(self, user_input: str) -> dict:
        """Recognize user intent from input text"""
        user_input = user_input.lower()
        
        for intent, patterns in self.intents.items():
            for pattern in patterns:
                if pattern in user_input:
                    return {
                        "intent": intent,
                        "confidence": 0.85,
                        "matched_pattern": pattern
                    }
        
        return {
            "intent": "unknown",
            "confidence": 0.0,
            "matched_pattern": None
        }

import re
from dataclasses import dataclass
from typing import List, Optional

@dataclass
class Entity:
    entity_type: str
    value: str
    confidence: float
    start_pos: int
    end_pos: int

class EntityExtractor:
    """Extract entities from customer input"""
    
    def __init__(self):
        self.entity_patterns = {
            "order_number": r"\b\d{5,10}\b",
            "phone_number": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",
            "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
            "amount": r"\$\d+(?:\.\d{2})?",
            "date": r"\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b"
        }
    
    def extract_entities(self, text: str) -> List[Entity]:
        """Extract entities from text"""
        entities = []
        
        for entity_type, pattern in self.entity_patterns.items():
            matches = re.finditer(pattern, text)
            for match in matches:
                entity = Entity(
                    entity_type=entity_type,
                    value=match.group(),
                    confidence=0.9,
                    start_pos=match.start(),
                    end_pos=match.end()
                )
                entities.append(entity)
        
        return entities

from enum import Enum
from typing import Dict, Any

class ConversationState(Enum):
    GREETING = "greeting"
    INTENT_COLLECTION = "intent_collection"
    ENTITY_COLLECTION = "entity_collection"
    CONFIRMATION = "confirmation"
    RESOLUTION = "resolution"
    CLOSING = "closing"

class ConversationManager:
    """Manage multi-turn conversations"""
    
    def __init__(self):
        self.conversation_context = {}
        self.current_state = ConversationState.GREETING
        self.required_entities = []
        self.collected_entities = {}
    
    def process_user_input(self, user_input: str, call_id: str) -> dict:
        """Process user input and determine next action"""
        
        # Update conversation context
        if call_id not in self.conversation_context:
            self.conversation_context[call_id] = {
                "state": self.current_state,
                "entities": {},
                "intent": None,
                "turn_count": 0
            }
        
        context = self.conversation_context[call_id]
        context["turn_count"] += 1
        
        # Recognize intent and extract entities
        intent_result = IntentRecognition().recognize_intent(user_input)
        entities = EntityExtractor().extract_entities(user_input)
        
        # Update context
        if intent_result["intent"] != "unknown":
            context["intent"] = intent_result["intent"]
        
        for entity in entities:
            context["entities"][entity.entity_type] = entity.value
        
        # Determine next action based on state
        return self._determine_next_action(context, intent_result, entities)

import json
from typing import Dict, Any

class LLMIntentClassifier:
    """Use LLMs for advanced intent classification"""
    
    def __init__(self):
        self.system_prompt = """
        You are a customer service AI assistant. Classify the customer's intent from their message.
        Available intents: check_balance, make_payment, technical_support, schedule_appointment, general_inquiry
        
        Return a JSON response with:
        - intent: the classified intent
        - confidence: confidence score (0-1)
        - reasoning: brief explanation
        - entities: any relevant information extracted
        """
    
    def classify_intent(self, user_input: str) -> Dict[str, Any]:
        """Classify intent using LLM"""
        
        # Simulate LLM response (in real implementation, call actual LLM API)
        prompt = f"{self.system_prompt}\n\nCustomer message: {user_input}"
        
        # Simulated LLM response
        response = self._simulate_llm_response(user_input)
        
        try:
            return json.loads(response)
        except json.JSONDecodeError:
            return {
                "intent": "unknown",
                "confidence": 0.0,
                "reasoning": "Failed to parse LLM response",
                "entities": {}
            }

class FallbackHandler:
    """Handle low-confidence scenarios and errors"""
    
    def __init__(self):
        self.confidence_threshold = 0.7
        self.max_retries = 3
        self.fallback_responses = {
            "low_confidence": [
                "I didn't quite catch that. Could you please repeat?",
                "I'm not sure I understood. Can you rephrase that?",
                "Let me make sure I understand correctly..."
            ],
            "no_intent": [
                "I'm here to help with account inquiries, payments, and technical support. What can I assist you with?",
                "You can ask me about your balance, make payments, or get technical support. How can I help?"
            ],
            "escalation": [
                "Let me connect you with a customer service representative who can better assist you.",
                "I'll transfer you to a human agent who can help with your specific needs."
            ]
        }
    
    def handle_low_confidence(self, confidence: float, retry_count: int) -> dict:
        """Handle low confidence scenarios"""
        
        if confidence < self.confidence_threshold:
            if retry_count < self.max_retries:
                return {
                    "action": "reprompt",
                    "message": self.fallback_responses["low_confidence"][retry_count % len(self.fallback_responses["low_confidence"])],
                    "should_escalate": False
                }
            else:
                return {
                    "action": "escalate",
                    "message": self.fallback_responses["escalation"][0],
                    "should_escalate": True
                }
        
        return {
            "action": "continue",
            "message": None,
            "should_escalate": False
        }

import time
from datetime import datetime
from typing import Dict, List

class NLPMetrics:
    """Track NLP performance metrics"""
    
    def __init__(self):
        self.metrics = {
            "intent_accuracy": [],
            "entity_extraction_accuracy": [],
            "response_time": [],
            "confidence_scores": [],
            "fallback_rate": 0,
            "escalation_rate": 0,
            "total_interactions": 0
        }
    
    def record_intent_recognition(self, predicted_intent: str, actual_intent: str, confidence: float, response_time: float):
        """Record intent recognition metrics"""
        accuracy = 1.0 if predicted_intent == actual_intent else 0.0
        
        self.metrics["intent_accuracy"].append(accuracy)
        self.metrics["confidence_scores"].append(confidence)
        self.metrics["response_time"].append(response_time)
        self.metrics["total_interactions"] += 1
    
    def get_performance_summary(self) -> Dict[str, float]:
        """Get performance summary"""
        total_interactions = self.metrics["total_interactions"]
        
        return {
            "avg_intent_accuracy": sum(self.metrics["intent_accuracy"]) / len(self.metrics["intent_accuracy"]) if self.metrics["intent_accuracy"] else 0.0,
            "avg_entity_accuracy": sum(self.metrics["entity_extraction_accuracy"]) / len(self.metrics["entity_extraction_accuracy"]) if self.metrics["entity_extraction_accuracy"] else 0.0,
            "avg_response_time": sum(self.metrics["response_time"]) / len(self.metrics["response_time"]) if self.metrics["response_time"] else 0.0,
            "avg_confidence": sum(self.metrics["confidence_scores"]) / len(self.metrics["confidence_scores"]) if self.metrics["confidence_scores"] else 0.0,
            "fallback_rate": self.metrics["fallback_rate"] / total_interactions if total_interactions > 0 else 0.0,
            "escalation_rate": self.metrics["escalation_rate"] / total_interactions if total_interactions > 0 else 0.0,
            "total_interactions": total_interactions
        }

from flask import Flask, request, Response
from twilio.twiml.voice_response import VoiceResponse

app = Flask(__name__)

@app.route("/voice", methods=["POST"])
def voice():
    resp = VoiceResponse()
    resp.say("Hello! This is an AI-powered call center using Twilio.", voice="Polly.Joanna")
    return Response(str(resp), mimetype="application/xml")

if __name__ == "__main__":
    app.run(port=5000)

from flask import Flask, request, Response
from twilio.twiml.voice_response import VoiceResponse, Gather
import requests

app = Flask(__name__)

@app.route("/voice", methods=["POST"])
def voice():
    resp = VoiceResponse()
    
    # Initial greeting
    resp.say("Welcome to our AI assistant. How can I help you today?", voice="Polly.Joanna")
    
    # Gather customer input
    gather = Gather(input='speech', action='/process_speech', method='POST')
    gather.say("Please tell me what you need help with.", voice="Polly.Joanna")
    resp.append(gather)
    
    return Response(str(resp), mimetype="application/xml")

@app.route("/process_speech", methods=["POST"])
def process_speech():
    resp = VoiceResponse()
    
    # Get speech input from Twilio
    speech_result = request.values.get('SpeechResult', '')
    confidence = request.values.get('Confidence', 0)
    
    # Process with NLP (simplified)
    if 'balance' in speech_result.lower():
        resp.say("I can help you check your balance. Please provide your account number.", voice="Polly.Joanna")
    elif 'password' in speech_result.lower():
        resp.say("I understand you need password help. Let me connect you with an agent.", voice="Polly.Joanna")
    else:
        resp.say("I didn't understand that. Let me connect you with a human agent.", voice="Polly.Joanna")
    
    return Response(str(resp), mimetype="application/xml")

{
  "StartAction": {
    "Type": "Message",
    "Parameters": {
      "Text": "Hello! How can I help you today?",
      "SSML": "<speak>Hello! How can I help you today?</speak>"
    }
  },
  "States": {
    "GetCustomerIntent": {
      "Type": "GetCustomerInput",
      "Parameters": {
        "BotName": "CustomerServiceBot",
        "BotAlias": "PROD",
        "LocaleId": "en_US"
      },
      "Transitions": {
        "Success": "ProcessIntent",
        "Error": "FallbackToAgent"
      }
    },
    "ProcessIntent": {
      "Type": "InvokeLambdaFunction",
      "Parameters": {
        "FunctionArn": "arn:aws:lambda:us-east-1:123456789012:function:process-intent"
      }
    }
  }
}

// Genesys Flow Script
const flow = {
  name: "AI-Powered Customer Service",
  version: "1.0",
  startState: "greeting",
  states: {
    greeting: {
      name: "Greeting",
      type: "message",
      properties: {
        message: "Welcome to our AI-powered customer service. How can I help you?"
      },
      transitions: {
        next: "getIntent"
      }
    },
    getIntent: {
      name: "Get Customer Intent",
      type: "aiIntent",
      properties: {
        aiEngine: "genesys-ai",
        confidenceThreshold: 0.7
      },
      transitions: {
        highConfidence: "processIntent",
        lowConfidence: "escalateToAgent"
      }
    },
    processIntent: {
      name: "Process Intent",
      type: "action",
      properties: {
        action: "processCustomerRequest"
      }
    }
  }
};

class CallMonitor:
    def __init__(self):
        self.metrics = {
            'active_calls': 0,
            'avg_latency': 0,
            'success_rate': 0,
            'error_count': 0
        }
    
    def track_call_metrics(self, call_id, metrics):
        """Track real-time call performance metrics"""
        self.metrics['active_calls'] += 1
        self.metrics['avg_latency'] = (
            (self.metrics['avg_latency'] + metrics['latency']) / 2
        )
        
        if metrics['success']:
            self.metrics['success_rate'] += 1
        else:
            self.metrics['error_count'] += 1

<speak>
  Your balance is <break time="400ms"/> $120.50.
</speak>

from flask import Flask, request, Response
from twilio.twiml.voice_response import VoiceResponse

app = Flask(__name__)

@app.route("/voice", methods=["POST"])
def voice():
    resp = VoiceResponse()
    resp.say("Welcome to ShopEasy. How can I assist you today?", voice="Polly.Joanna")
    # Here you would integrate NLP and backend logic
    return Response(str(resp), mimetype="application/xml")

if __name__ == "__main__":
    app.run(port=5000)

def classify_intent(utterance: str) -> Dict:
    """Classify customer intent from utterance"""
    utterance_lower = utterance.lower()
    
    if any(word in utterance_lower for word in ["track", "order", "status"]):
        return {"intent": "CheckOrderStatus", "confidence": 0.95}
    elif any(word in utterance_lower for word in ["book", "appointment", "schedule"]):
        return {"intent": "BookAppointment", "confidence": 0.92}
    elif any(word in utterance_lower for word in ["pay", "payment", "bill"]):
        return {"intent": "MakePayment", "confidence": 0.89}
    else:
        return {"intent": "Unknown", "confidence": 0.45}

def extract_entities(utterance: str) -> Dict:
    """Extract entities from customer utterance"""
    entities = {}
    
    # Extract order numbers
    order_pattern = r'\b(\d{5,})\b'
    orders = re.findall(order_pattern, utterance)
    if orders:
        entities["order_number"] = orders[0]
    
    # Extract doctor names
    doctor_pattern = r'Dr\.\s+(\w+)'
    doctors = re.findall(doctor_pattern, utterance)
    if doctors:
        entities["doctor_name"] = doctors[0]
    
    # Extract amounts
    amount_pattern = r'\$(\d+(?:\.\d{2})?)'
    amounts = re.findall(amount_pattern, utterance)
    if amounts:
        entities["amount"] = float(amounts[0])
    
    return entities

def generate_ssml_response(text: str, add_pauses: bool = True) -> str:
    """Generate SSML with natural pacing"""
    ssml = text
    
    if add_pauses:
        # Add pauses for natural pacing
        ssml = re.sub(r'([.!?])\s+', r'\1 <break time="300ms"/> ', ssml)
        
        # Add pauses before important information
        ssml = re.sub(r'(\$[\d,]+\.?\d*)', r'<break time="400ms"/> \1', ssml)
    
    return f'<speak>{ssml}</speak>'

from flask import Flask, request
from twilio.twiml.voice_response import VoiceResponse

app = Flask(__name__)

@app.route("/webhook", methods=["POST"])
def handle_call():
    resp = VoiceResponse()
    
    # Get customer input
    speech_result = request.values.get('SpeechResult', '')
    
    # Process with NLP
    intent = classify_intent(speech_result)
    
    if intent["intent"] == "CheckOrderStatus":
        resp.say("Please provide your order number.", voice="Polly.Joanna")
        resp.gather(input="speech", action="/process_order", method="POST")
    else:
        resp.say("I didn't understand. Please try again.", voice="Polly.Joanna")
        resp.gather(input="speech", action="/webhook", method="POST")
    
    return str(resp)

{
  "Type": "GetCustomerInput",
  "Parameters": {
    "Text": "Welcome to our service. How can I help you today?",
    "TimeoutSeconds": 10,
    "MaxDigits": 0,
    "TextToSpeechParameters": {
      "VoiceId": "Joanna",
      "Engine": "neural"
    }
  },
  "NextAction": "ProcessIntent"
}

def handle_low_confidence(intent: Dict, utterance: str) -> str:
    """Handle cases where intent confidence is low"""
    if intent["confidence"] < 0.7:
        return f"I think you said '{utterance}', but I'm not completely sure. " \
               f"Could you please clarify what you need help with?"
    return None

def escalate_to_human(reason: str) -> str:
    """Escalate call to human agent"""
    return f"I understand this is important. Let me connect you with a " \
           f"specialist who can better assist you. Please hold."

def retry_prompt(attempt: int, max_attempts: int = 3) -> str:
    """Generate retry prompt with increasing clarity"""
    if attempt == 1:
        return "I didn't catch that. Could you please repeat?"
    elif attempt == 2:
        return "I'm still having trouble understanding. You can say things like " \
               "'check my order', 'make a payment', or 'speak to an agent'."
    else:
        return "Let me connect you with a human agent who can help."

def test_intent_classification():
    """Test intent classification accuracy"""
    test_cases = [
        ("I want to track my order", "CheckOrderStatus"),
        ("I need to pay my bill", "MakePayment"),
        ("Book an appointment", "BookAppointment")
    ]
    
    for utterance, expected_intent in test_cases:
        result = classify_intent(utterance)
        assert result["intent"] == expected_intent

{
  "timestamp": "2025-01-24T10:15:22Z",
  "session_id": "abcd-1234-5678-efgh",
  "call_id": "CA1234567890abcdef",
  "user_id": "user_12345",
  "phone_number": "+15551234567",
  "event_type": "call_start",
  "component": "ivr_gateway",
  "latency_ms": 180,
  "status": "success",
  "metadata": {
    "intent_detected": "CheckBalance",
    "ivr_node": "BalanceMenu",
    "confidence_score": 0.92
  }
}

# ASR Accuracy Calculation
def calculate_asr_accuracy(recognized_text, actual_text):
    """Calculate Word Error Rate (WER)"""
    recognized_words = recognized_text.lower().split()
    actual_words = actual_text.lower().split()
    
    # Calculate Levenshtein distance
    distance = levenshtein_distance(recognized_words, actual_words)
    wer = distance / len(actual_words)
    accuracy = 1 - wer
    
    return accuracy

# First Call Resolution Rate
def calculate_fcr_rate(total_calls, resolved_calls):
    """Calculate First Call Resolution rate"""
    fcr_rate = (resolved_calls / total_calls) * 100
    return fcr_rate

# Average Handling Time
def calculate_aht(call_durations):
    """Calculate Average Handling Time"""
    total_duration = sum(call_durations)
    aht = total_duration / len(call_durations)
    return aht

alerts:
  - name: "High TTS Latency"
    condition: "tts_latency_ms > 1000"
    severity: "critical"
    notification: ["slack", "pagerduty"]
    
  - name: "High Error Rate"
    condition: "error_rate > 0.02"
    severity: "warning"
    notification: ["slack"]
    
  - name: "Low ASR Accuracy"
    condition: "asr_accuracy < 0.85"
    severity: "warning"
    notification: ["email", "slack"]
    
  - name: "System Down"
    condition: "uptime < 0.99"
    severity: "critical"
    notification: ["pagerduty", "phone"]

# Example trace correlation
def handle_voice_request(request):
    trace_id = generate_trace_id()
    
    # Log with trace correlation
    logger.info("Voice request received", extra={
        "trace_id": trace_id,
        "session_id": request.session_id,
        "call_id": request.call_id
    })
    
    # Process through different services
    with tracer.start_span("stt_processing", trace_id=trace_id):
        text = process_speech(request.audio)
    
    with tracer.start_span("intent_detection", trace_id=trace_id):
        intent = detect_intent(text)
    
    with tracer.start_span("tts_generation", trace_id=trace_id):
        response = generate_speech(intent.response)
    
    return response

# Example anomaly detection
def detect_voice_anomaly(audio_features):
    """Detect anomalies in voice patterns"""
    model = load_anomaly_detection_model()
    
    # Extract features
    features = extract_audio_features(audio_features)
    
    # Predict anomaly score
    anomaly_score = model.predict(features)
    
    if anomaly_score > ANOMALY_THRESHOLD:
        logger.warning("Voice anomaly detected", extra={
            "anomaly_score": anomaly_score,
            "features": features
        })
        
        # Trigger appropriate response
        escalate_call()
    
    return anomaly_score

import logging
import json
from datetime import datetime
from typing import Dict, Any

class VoiceSystemLogger:
    """Structured logger for voice AI systems"""
    
    def __init__(self, service_name: str):
        self.service_name = service_name
        self.logger = logging.getLogger(service_name)
        
    def log_call_event(self, event_type: str, session_id: str, 
                      call_id: str, metadata: Dict[str, Any]):
        """Log call-related events"""
        log_entry = {
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "service": self.service_name,
            "event_type": event_type,
            "session_id": session_id,
            "call_id": call_id,
            "metadata": metadata
        }
        
        self.logger.info(json.dumps(log_entry))
    
    def log_performance_metric(self, metric_name: str, value: float, 
                             session_id: str, metadata: Dict[str, Any] = None):
        """Log performance metrics"""
        log_entry = {
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "service": self.service_name,
            "metric_name": metric_name,
            "value": value,
            "session_id": session_id,
            "metadata": metadata or {}
        }
        
        self.logger.info(json.dumps(log_entry))

import dash
from dash import dcc, html
import plotly.graph_objs as go
from datetime import datetime, timedelta

def create_monitoring_dashboard():
    """Create real-time monitoring dashboard"""
    app = dash.Dash(__name__)
    
    app.layout = html.Div([
        html.H1("Voice AI System Monitor"),
        
        # System Health
        html.Div([
            html.H2("System Health"),
            dcc.Graph(id="system-health"),
            dcc.Interval(id="health-interval", interval=30000)  # 30 seconds
        ]),
        
        # Performance Metrics
        html.Div([
            html.H2("Performance Metrics"),
            dcc.Graph(id="performance-metrics"),
            dcc.Interval(id="performance-interval", interval=60000)  # 1 minute
        ]),
        
        # Call Volume
        html.Div([
            html.H2("Call Volume"),
            dcc.Graph(id="call-volume"),
            dcc.Interval(id="volume-interval", interval=300000)  # 5 minutes
        ])
    ])
    
    return app

import librosa
import numpy as np

class EmotionDetector:
    """Advanced emotion detection from voice"""
    
    def extract_audio_features(self, audio_data: np.ndarray, sample_rate: int) -> Dict[str, float]:
        """Extract features for emotion analysis"""
        features = {}
        
        # Pitch features
        pitches, magnitudes = librosa.piptrack(y=audio_data, sr=sample_rate)
        pitch_values = pitches[magnitudes > np.percentile(magnitudes, 85)]
        features['pitch_mean'] = np.mean(pitch_values) if len(pitch_values) > 0 else 0
        features['pitch_std'] = np.std(pitch_values) if len(pitch_values) > 0 else 0
        
        # Energy features
        features['energy_mean'] = np.mean(librosa.feature.rms(y=audio_data))
        features['energy_std'] = np.std(librosa.feature.rms(y=audio_data))
        
        # Spectral features
        mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=13)
        features['mfcc_mean'] = np.mean(mfccs)
        features['mfcc_std'] = np.std(mfccs)
        
        return features
    
    def detect_emotion(self, audio_features: Dict[str, float]) -> Dict[str, float]:
        """Detect emotions from audio features"""
        emotions = {
            'happiness': 0.0, 'sadness': 0.0, 'anger': 0.0,
            'fear': 0.0, 'surprise': 0.0, 'disgust': 0.0, 'neutral': 0.0
        }
        
        # Rule-based emotion detection
        pitch_mean = audio_features.get('pitch_mean', 0)
        energy_mean = audio_features.get('energy_mean', 0)
        
        if pitch_mean > 200 and energy_mean > 0.1:
            emotions['happiness'] = 0.8
        elif pitch_mean < 150 and energy_mean < 0.05:
            emotions['sadness'] = 0.7
        elif energy_mean > 0.15:
            emotions['anger'] = 0.6
        else:
            emotions['neutral'] = 0.6
        
        return emotions

class EmotionAwareIVR:
    """IVR system with emotion detection and adaptive responses"""
    
    def __init__(self):
        self.emotion_detector = EmotionDetector()
        self.response_templates = {
            'happiness': {
                'greeting': "I'm glad you're having a great day! How can I help you?",
                'confirmation': "Excellent! I'll get that sorted for you right away.",
                'closing': "It's been a pleasure helping you today. Have a wonderful day!"
            },
            'sadness': {
                'greeting': "I understand this might be a difficult time. I'm here to help.",
                'confirmation': "I'll make sure to handle this carefully for you.",
                'closing': "I hope I've been able to help. Please don't hesitate to call back."
            },
            'anger': {
                'greeting': "I can see you're frustrated, and I want to help resolve this quickly.",
                'confirmation': "I understand this is important to you. Let me escalate this immediately.",
                'closing': "I appreciate your patience. We're working to resolve this for you."
            }
        }
    
    def process_customer_input(self, audio_data: np.ndarray, sample_rate: int, 
                             text_content: str) -> Dict[str, Any]:
        """Process customer input with emotion detection"""
        
        # Extract audio features and detect emotions
        audio_features = self.emotion_detector.extract_audio_features(audio_data, sample_rate)
        emotions = self.emotion_detector.detect_emotion(audio_features)
        
        # Get dominant emotion
        dominant_emotion = max(emotions.items(), key=lambda x: x[1])
        
        # Generate appropriate response
        response = self._generate_emotion_aware_response(dominant_emotion[0], text_content)
        
        # Determine if escalation is needed
        escalation_needed = emotions.get('anger', 0) > 0.7 or emotions.get('fear', 0) > 0.6
        
        return {
            'text_response': response,
            'detected_emotion': dominant_emotion[0],
            'emotion_confidence': dominant_emotion[1],
            'all_emotions': emotions,
            'escalation_needed': escalation_needed
        }

from sklearn.mixture import GaussianMixture
import numpy as np

class VoiceBiometricSystem:
    """Voice biometric system for speaker identification and verification"""
    
    def __init__(self):
        self.speaker_models = {}
        self.speaker_profiles = {}
        self.verification_threshold = 0.7
    
    def enroll_speaker(self, speaker_id: str, audio_samples: List[np.ndarray], 
                      sample_rate: int, metadata: Dict[str, Any] = None):
        """Enroll a new speaker in the system"""
        
        # Extract features from all samples
        all_features = []
        for audio in audio_samples:
            features = self._extract_speaker_features(audio, sample_rate)
            all_features.extend(features)
        
        # Train Gaussian Mixture Model
        gmm = GaussianMixture(n_components=16, covariance_type='diag', random_state=42)
        gmm.fit(all_features)
        
        # Store model and metadata
        self.speaker_models[speaker_id] = gmm
        self.speaker_profiles[speaker_id] = {
            'enrollment_date': datetime.now(),
            'sample_count': len(audio_samples),
            'metadata': metadata or {}
        }
    
    def verify_speaker(self, claimed_speaker_id: str, audio_data: np.ndarray, 
                      sample_rate: int) -> Dict[str, Any]:
        """Verify if the audio matches the claimed speaker"""
        
        if claimed_speaker_id not in self.speaker_models:
            return {'verified': False, 'confidence': 0.0, 'error': 'Speaker not enrolled'}
        
        # Extract features and get score
        features = self._extract_speaker_features(audio_data, sample_rate)
        model = self.speaker_models[claimed_speaker_id]
        score = model.score(features)
        
        # Normalize score and make decision
        normalized_score = min(1.0, max(0.0, (score + 100) / 200))
        verified = normalized_score >= self.verification_threshold
        
        return {
            'verified': verified,
            'confidence': normalized_score,
            'raw_score': score,
            'threshold': self.verification_threshold
        }
    
    def _extract_speaker_features(self, audio_data: np.ndarray, sample_rate: int) -> np.ndarray:
        """Extract speaker-specific features"""
        import librosa
        
        # Extract MFCCs with deltas
        mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=20)
        delta_mfccs = librosa.feature.delta(mfccs)
        delta2_mfccs = librosa.feature.delta(mfccs, order=2)
        
        # Combine features
        features = np.vstack([mfccs, delta_mfccs, delta2_mfccs])
        return features.T

from langdetect import detect
from googletrans import Translator

class MultilingualVoiceAI:
    """Multilingual voice AI system with language detection and translation"""
    
    def __init__(self):
        self.translator = Translator()
        self.supported_languages = {
            'en': 'English', 'es': 'Spanish', 'fr': 'French',
            'de': 'German', 'it': 'Italian', 'pt': 'Portuguese',
            'ja': 'Japanese', 'ko': 'Korean', 'zh': 'Chinese', 'ar': 'Arabic'
        }
    
    def detect_language(self, text: str) -> str:
        """Detect the language of text"""
        try:
            detected_lang = detect(text)
            return detected_lang
        except:
            return 'en'  # Default to English
    
    def translate_text(self, text: str, target_language: str, 
                      source_language: str = 'auto') -> str:
        """Translate text to target language"""
        try:
            translation = self.translator.translate(
                text, dest=target_language, src=source_language
            )
            return translation.text
        except:
            return text
    
    def process_multilingual_input(self, text: str, preferred_language: str = 'en') -> Dict[str, Any]:
        """Process input in multiple languages"""
        
        # Detect language
        detected_language = self.detect_language(text)
        
        # Translate to preferred language if different
        translated_text = text
        if detected_language != preferred_language:
            translated_text = self.translate_text(text, preferred_language, detected_language)
        
        return {
            'original_text': text,
            'translated_text': translated_text,
            'detected_language': detected_language,
            'preferred_language': preferred_language,
            'language_name': self.supported_languages.get(detected_language, 'Unknown')
        }

class CulturalAdaptation:
    """Cultural adaptation for global voice AI"""
    
    def __init__(self):
        self.cultural_profiles = {
            'en-US': {
                'formality': 'casual',
                'greeting_style': 'direct',
                'time_format': '12h',
                'currency': 'USD'
            },
            'ja-JP': {
                'formality': 'formal',
                'greeting_style': 'respectful',
                'time_format': '24h',
                'currency': 'JPY'
            },
            'es-ES': {
                'formality': 'semi-formal',
                'greeting_style': 'warm',
                'time_format': '24h',
                'currency': 'EUR'
            }
        }
    
    def adapt_response(self, response: str, culture_code: str) -> str:
        """Adapt response for cultural preferences"""
        
        profile = self.cultural_profiles.get(culture_code, self.cultural_profiles['en-US'])
        
        # Apply cultural adaptations
        if profile['formality'] == 'formal':
            response = f"申し訳ございませんが、{response}"
        elif profile['greeting_style'] == 'warm':
            response = f"¡Hola! {response}"
        
        return response

class ConversationalContext:
    """Advanced conversational context management"""
    
    def __init__(self, session_id: str):
        self.session_id = session_id
        self.conversation_history = []
        self.context_variables = {}
        self.user_preferences = {}
        self.context_window = 10
    
    def add_interaction(self, user_input: str, system_response: str, 
                       metadata: Dict[str, Any] = None):
        """Add interaction to conversation history"""
        
        interaction = {
            'timestamp': datetime.now(),
            'user_input': user_input,
            'system_response': system_response,
            'metadata': metadata or {}
        }
        
        self.conversation_history.append(interaction)
        
        # Maintain context window
        if len(self.conversation_history) > self.context_window:
            self.conversation_history.pop(0)
    
    def extract_context_variables(self, user_input: str) -> Dict[str, Any]:
        """Extract context variables from user input"""
        
        entities = self._extract_entities(user_input)
        preferences = self._extract_preferences(user_input)
        
        # Update context variables
        self.context_variables.update(entities)
        self.user_preferences.update(preferences)
        
        return {
            'entities': entities,
            'preferences': preferences,
            'current_context': self.context_variables.copy()
        }
    
    def _extract_entities(self, text: str) -> Dict[str, Any]:
        """Extract entities from text"""
        entities = {}
        
        if 'my name is' in text.lower():
            name_start = text.lower().find('my name is') + 10
            name_end = text.find('.', name_start)
            if name_end == -1:
                name_end = len(text)
            entities['name'] = text[name_start:name_end].strip()
        
        return entities
    
    def _extract_preferences(self, text: str) -> Dict[str, Any]:
        """Extract user preferences from text"""
        preferences = {}
        
        if any(lang in text.lower() for lang in ['spanish', 'español']):
            preferences['language'] = 'es'
        elif any(lang in text.lower() for lang in ['french', 'français']):
            preferences['language'] = 'fr'
        
        return preferences

class PredictiveIntentSystem:
    """Predictive intent recognition and proactive assistance"""
    
    def __init__(self):
        self.intent_patterns = {
            'check_balance': ['balance', 'account', 'money', 'funds'],
            'transfer_money': ['transfer', 'send', 'move', 'pay'],
            'reset_password': ['password', 'reset', 'forgot', 'login'],
            'schedule_appointment': ['appointment', 'schedule', 'book', 'meeting'],
            'technical_support': ['help', 'problem', 'issue', 'support', 'broken']
        }
        
        self.intent_sequences = {
            'check_balance': ['transfer_money', 'schedule_appointment'],
            'transfer_money': ['check_balance', 'technical_support'],
            'reset_password': ['technical_support', 'check_balance']
        }
    
    def predict_next_intent(self, current_intent: str, 
                          conversation_history: List[Dict]) -> List[str]:
        """Predict likely next intents based on current context"""
        
        # Get common next intents
        common_next = self.intent_sequences.get(current_intent, [])
        
        # Analyze conversation patterns
        pattern_based = self._analyze_conversation_patterns(conversation_history)
        
        # Combine predictions
        all_predictions = common_next + pattern_based
        
        return list(set(all_predictions))
    
    def generate_proactive_suggestions(self, predicted_intents: List[str]) -> List[str]:
        """Generate proactive suggestions based on predicted intents"""
        
        suggestions = []
        
        for intent in predicted_intents:
            if intent == 'transfer_money':
                suggestions.append("Would you like to transfer money to another account?")
            elif intent == 'schedule_appointment':
                suggestions.append("I can help you schedule an appointment. What day works best?")
            elif intent == 'technical_support':
                suggestions.append("If you're having technical issues, I can connect you with support.")
            elif intent == 'check_balance':
                suggestions.append("Would you like to check your account balance?")
        
        return suggestions[:2]  # Limit to 2 suggestions
    
    def _analyze_conversation_patterns(self, history: List[Dict]) -> List[str]:
        """Analyze conversation patterns to predict next intent"""
        
        recent_topics = []
        for interaction in history[-3:]:  # Last 3 interactions
            user_input = interaction.get('user_input', '').lower()
            
            if any(word in user_input for word in ['money', 'transfer', 'send']):
                recent_topics.append('transfer_money')
            elif any(word in user_input for word in ['balance', 'account']):
                recent_topics.append('check_balance')
            elif any(word in user_input for word in ['password', 'login']):
                recent_topics.append('reset_password')
        
        if recent_topics:
            from collections import Counter
            topic_counts = Counter(recent_topics)
            return [topic for topic, count in topic_counts.most_common(2)]
        
        return []

class AdvancedVoiceAISystem:
    """Complete advanced voice AI system integration"""
    
    def __init__(self):
        self.emotion_detector = EmotionAwareIVR()
        self.biometric_system = VoiceBiometricSystem()
        self.multilingual_system = MultilingualVoiceAI()
        self.context_manager = ConversationalContext("session_1")
        self.predictive_system = PredictiveIntentSystem()
        
        self.current_session = None
    
    def process_voice_input(self, audio_data: bytes, sample_rate: int, 
                          session_id: str, user_id: Optional[str] = None) -> Dict[str, Any]:
        """Process voice input with all advanced features"""
        
        # Initialize session if needed
        if not self.current_session or self.current_session.session_id != session_id:
            self.current_session = ConversationalContext(session_id)
        
        # Convert audio to numpy array
        audio_np = self._bytes_to_numpy(audio_data, sample_rate)
        
        # 1. Language detection and translation
        language_result = self.multilingual_system.process_multilingual_input(
            "sample text", preferred_language='en'
        )
        
        # 2. Emotion detection
        emotion_result = self.emotion_detector.process_customer_input(
            audio_np, sample_rate, language_result['translated_text']
        )
        
        # 3. Speaker identification/verification
        if user_id:
            biometric_result = self.biometric_system.verify_speaker(
                user_id, audio_np, sample_rate
            )
        else:
            biometric_result = {'verified': False, 'confidence': 0.0}
        
        # 4. Context analysis
        context_result = self.current_session.extract_context_variables(
            language_result['translated_text']
        )
        
        # 5. Intent prediction
        current_intent = self._detect_intent(language_result['translated_text'])
        predicted_intents = self.predictive_system.predict_next_intent(
            current_intent, self.current_session.get_recent_context()
        )
        
        # 6. Generate comprehensive response
        response = self._generate_advanced_response(
            language_result, emotion_result, biometric_result, 
            context_result, predicted_intents
        )
        
        return {
            'text_response': response['text_response'],
            'emotion_detected': emotion_result['detected_emotion'],
            'language_detected': language_result['detected_language'],
            'speaker_verified': biometric_result.get('verified', False),
            'escalation_needed': emotion_result['escalation_needed'],
            'predicted_intents': predicted_intents,
            'context_variables': context_result['current_context']
        }
    
    def _generate_advanced_response(self, language_result: Dict, emotion_result: Dict,
                                  biometric_result: Dict, context_result: Dict,
                                  predicted_intents: List[str]) -> Dict[str, Any]:
        """Generate advanced response using all available information"""
        
        # Base response based on intent and emotion
        base_response = emotion_result['text_response']
        
        # Add personalization if speaker is verified
        if biometric_result.get('verified', False):
            base_response = f"Hello {context_result.get('entities', {}).get('name', 'there')}, {base_response}"
        
        # Add proactive suggestions
        suggestions = self.predictive_system.generate_proactive_suggestions(predicted_intents)
        
        if suggestions:
            base_response += f" {suggestions[0]}"
        
        return {
            'text_response': base_response,
            'suggestions': suggestions,
            'emotion_adapted': True
        }
    
    def _detect_intent(self, text: str) -> str:
        """Detect intent from text"""
        text_lower = text.lower()
        
        for intent, keywords in self.predictive_system.intent_patterns.items():
            if any(keyword in text_lower for keyword in keywords):
                return intent
        
        return 'general_inquiry'
    
    def _bytes_to_numpy(self, audio_bytes: bytes, sample_rate: int) -> np.ndarray:
        """Convert audio bytes to numpy array"""
        import struct
        
        # Convert bytes to 16-bit integers
        audio_int = struct.unpack(f'<{len(audio_bytes)//2}h', audio_bytes)
        
        # Convert to float and normalize
        audio_np = np.array(audio_int, dtype=np.float32) / 32768.0
        
        return audio_np

class VoiceSecurityThreats:
    """Common security threats in voice AI systems"""
    
    def __init__(self):
        self.threat_categories = {
            "interception": {
                "description": "Unauthorized access to voice data",
                "mitigation": ["End-to-end encryption", "Secure transmission protocols"]
            },
            "spoofing": {
                "description": "Voice impersonation attacks",
                "mitigation": ["Voice biometrics", "Liveness detection", "MFA"]
            },
            "fraud": {
                "description": "Exploitation of voice systems",
                "mitigation": ["Rate limiting", "Behavioral analysis", "Fraud detection"]
            }
        }
    
    def assess_threat_level(self, system_type: str, data_sensitivity: str) -> Dict[str, str]:
        """Assess threat level for different system types"""
        
        if system_type in ["banking", "healthcare", "government"]:
            return {"level": "high", "recommendations": self.threat_categories}
        elif system_type in ["ecommerce", "utilities", "insurance"]:
            return {"level": "medium", "recommendations": self.threat_categories}
        else:
            return {"level": "low", "recommendations": self.threat_categories}

from cryptography.fernet import Fernet
import re

class VoiceEncryption:
    """Voice data encryption and secure transmission"""
    
    def __init__(self):
        self.encryption_key = Fernet.generate_key()
        self.cipher_suite = Fernet(self.encryption_key)
    
    def encrypt_voice_data(self, audio_data: bytes) -> bytes:
        """Encrypt voice audio data"""
        return self.cipher_suite.encrypt(audio_data)
    
    def decrypt_voice_data(self, encrypted_data: bytes) -> bytes:
        """Decrypt voice audio data"""
        return self.cipher_suite.decrypt(encrypted_data)
    
    def mask_sensitive_data(self, text: str) -> str:
        """Mask sensitive information in voice transcripts"""
        
        # Mask credit card numbers
        text = re.sub(r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b', '[CARD_NUMBER]', text)
        
        # Mask SSN
        text = re.sub(r'\b\d{3}[\s-]?\d{2}[\s-]?\d{4}\b', '[SSN]', text)
        
        # Mask phone numbers
        text = re.sub(r'\b\d{3}[\s-]?\d{3}[\s-]?\d{4}\b', '[PHONE]', text)
        
        return text

import hashlib
import secrets
import time
from typing import Dict, List, Optional

class VoiceIAM:
    """Identity and Access Management for voice systems"""
    
    def __init__(self):
        self.users = {}
        self.api_keys = {}
        self.session_tokens = {}
    
    def create_user(self, username: str, password: str, role: str = "user") -> Dict[str, str]:
        """Create a new user with secure password hashing"""
        
        # Generate salt and hash password
        salt = secrets.token_hex(16)
        password_hash = hashlib.pbkdf2_hmac(
            'sha256', 
            password.encode('utf-8'), 
            salt.encode('utf-8'), 
            100000
        ).hex()
        
        user_id = secrets.token_hex(16)
        
        self.users[user_id] = {
            "username": username,
            "password_hash": password_hash,
            "salt": salt,
            "role": role,
            "created_at": time.time(),
            "mfa_enabled": False
        }
        
        return {"user_id": user_id, "status": "created"}
    
    def authenticate_user(self, username: str, password: str, mfa_code: Optional[str] = None) -> Dict[str, Any]:
        """Authenticate user with MFA support"""
        
        # Find user by username
        user_id = None
        for uid, user_data in self.users.items():
            if user_data["username"] == username:
                user_id = uid
                break
        
        if not user_id:
            return {"authenticated": False, "error": "User not found"}
        
        user = self.users[user_id]
        
        # Verify password
        password_hash = hashlib.pbkdf2_hmac(
            'sha256', 
            password.encode('utf-8'), 
            user["salt"].encode('utf-8'), 
            100000
        ).hex()
        
        if password_hash != user["password_hash"]:
            return {"authenticated": False, "error": "Invalid password"}
        
        # Check MFA if enabled
        if user["mfa_enabled"] and not mfa_code:
            return {"authenticated": False, "error": "MFA code required"}
        
        # Generate session token
        session_token = secrets.token_hex(32)
        self.session_tokens[session_token] = {
            "user_id": user_id,
            "created_at": time.time(),
            "expires_at": time.time() + 3600  # 1 hour
        }
        
        return {
            "authenticated": True,
            "user_id": user_id,
            "role": user["role"],
            "session_token": session_token
        }

from datetime import datetime, timedelta
from typing import Dict, List, Optional

class GDPRCompliance:
    """GDPR compliance management for voice systems"""
    
    def __init__(self):
        self.consent_records = {}
        self.retention_policies = {
            "voice_recordings": 30,  # days
            "transcripts": 90,       # days
            "user_profiles": 365,    # days
        }
    
    def record_consent(self, user_id: str, consent_type: str, 
                      consent_given: bool) -> str:
        """Record user consent for data processing"""
        
        consent_id = f"consent_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{user_id}"
        
        self.consent_records[consent_id] = {
            "user_id": user_id,
            "consent_type": consent_type,
            "consent_given": consent_given,
            "timestamp": datetime.now()
        }
        
        return consent_id
    
    def check_consent(self, user_id: str, consent_type: str) -> bool:
        """Check if user has given consent for specific processing"""
        
        # Find most recent consent for this user and type
        latest_consent = None
        latest_timestamp = None
        
        for consent_id, consent_data in self.consent_records.items():
            if (consent_data["user_id"] == user_id and 
                consent_data["consent_type"] == consent_type):
                
                if latest_timestamp is None or consent_data["timestamp"] > latest_timestamp:
                    latest_consent = consent_data
                    latest_timestamp = consent_data["timestamp"]
        
        if latest_consent is None:
            return False
        
        return latest_consent["consent_given"]
    
    def process_data_subject_request(self, user_id: str, request_type: str) -> Dict[str, Any]:
        """Process GDPR data subject requests"""
        
        if request_type == "access":
            return {
                "request_type": "access",
                "user_id": user_id,
                "data": self._get_user_personal_data(user_id),
                "timestamp": datetime.now()
            }
        elif request_type == "deletion":
            return {
                "request_type": "deletion",
                "user_id": user_id,
                "status": "deletion_scheduled",
                "completion_date": datetime.now() + timedelta(days=30)
            }
        else:
            return {"error": "Unknown request type"}
    
    def _get_user_personal_data(self, user_id: str) -> Dict[str, Any]:
        """Get user's personal data"""
        return {
            "name": "John Doe",
            "email": "john.doe@example.com",
            "phone": "+1234567890",
            "voice_profile": "voice_profile_hash"
        }

class HIPAACompliance:
    """HIPAA compliance for healthcare voice applications"""
    
    def __init__(self):
        self.phi_records = {}  # Protected Health Information
        self.access_logs = {}
    
    def handle_phi_data(self, patient_id: str, data_type: str, 
                       data_content: str, user_id: str) -> Dict[str, Any]:
        """Handle Protected Health Information with HIPAA compliance"""
        
        # Log access
        access_id = self._log_access(patient_id, user_id, data_type)
        
        # Encrypt PHI data
        encrypted_data = self._encrypt_phi_data(data_content)
        
        # Store with audit trail
        self.phi_records[access_id] = {
            "patient_id": patient_id,
            "data_type": data_type,
            "encrypted_data": encrypted_data,
            "user_id": user_id,
            "timestamp": datetime.now(),
            "purpose": "treatment"
        }
        
        return {
            "access_id": access_id,
            "status": "phi_handled",
            "compliance_verified": True
        }
    
    def _log_access(self, patient_id: str, user_id: str, data_type: str) -> str:
        """Log access to PHI"""
        
        access_id = f"access_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{user_id}"
        
        self.access_logs[access_id] = {
            "patient_id": patient_id,
            "user_id": user_id,
            "data_type": data_type,
            "timestamp": datetime.now(),
            "action": "access"
        }
        
        return access_id
    
    def _encrypt_phi_data(self, data: str) -> str:
        """Encrypt PHI data"""
        return f"encrypted_{hash(data)}"

class VoiceAuditSystem:
    """Comprehensive audit system for voice applications"""
    
    def __init__(self):
        self.audit_logs = []
        self.audit_config = {
            "retention_days": 2555,  # 7 years
            "sensitive_fields": ["password", "ssn", "credit_card", "api_key"]
        }
    
    def log_audit_event(self, event_type: str, user_id: str, 
                       action: str, details: Dict[str, Any], 
                       severity: str = "INFO") -> str:
        """Log audit event with comprehensive details"""
        
        audit_id = f"audit_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}"
        
        audit_entry = {
            "audit_id": audit_id,
            "timestamp": datetime.now(),
            "event_type": event_type,
            "user_id": user_id,
            "action": action,
            "details": self._sanitize_details(details),
            "severity": severity
        }
        
        self.audit_logs.append(audit_entry)
        
        return audit_id
    
    def _sanitize_details(self, details: Dict[str, Any]) -> Dict[str, Any]:
        """Remove sensitive information from audit details"""
        
        sanitized = details.copy()
        
        for field in self.audit_config["sensitive_fields"]:
            if field in sanitized:
                sanitized[field] = "[REDACTED]"
        
        return sanitized
    
    def generate_audit_report(self, start_date: datetime, end_date: datetime) -> Dict[str, Any]:
        """Generate comprehensive audit report"""
        
        period_logs = [
            log for log in self.audit_logs
            if start_date <= log["timestamp"] <= end_date
        ]
        
        # Analyze by event type
        event_counts = {}
        for log in period_logs:
            event_type = log["event_type"]
            event_counts[event_type] = event_counts.get(event_type, 0) + 1
        
        return {
            "report_period": f"{start_date} to {end_date}",
            "total_events": len(period_logs),
            "event_type_breakdown": event_counts,
            "unique_users": len(set(log["user_id"] for log in period_logs)),
            "compliance_status": "compliant"
        }

class ResponsibleAI:
    """Responsible AI practices for voice applications"""
    
    def __init__(self):
        self.ai_ethics_guidelines = {
            "transparency": ["disclose_ai_usage", "explain_ai_decisions"],
            "fairness": ["bias_detection", "equal_treatment"],
            "privacy": ["data_minimization", "consent_management"],
            "accountability": ["decision_logging", "human_oversight"]
        }
        
        self.decision_logs = []
    
    def disclose_ai_usage(self, interaction_type: str) -> str:
        """Generate AI disclosure message"""
        
        disclosures = {
            "greeting": "Hello, I'm an AI assistant. How can I help you today?",
            "confirmation": "I'm an AI system processing your request.",
            "escalation": "I'm connecting you with a human agent who can better assist you.",
            "closing": "Thank you for using our AI-powered service."
        }
        
        return disclosures.get(interaction_type, "I'm an AI assistant.")
    
    def log_ai_decision(self, decision_type: str, input_data: str, 
                       output_data: str, confidence: float, 
                       user_id: str) -> str:
        """Log AI decision for transparency and accountability"""
        
        decision_id = f"decision_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}"
        
        decision_log = {
            "decision_id": decision_id,
            "timestamp": datetime.now(),
            "decision_type": decision_type,
            "input_data": self._sanitize_input(input_data),
            "output_data": output_data,
            "confidence": confidence,
            "user_id": user_id,
            "model_version": "voice_ai_v1.2"
        }
        
        self.decision_logs.append(decision_log)
        
        return decision_id
    
    def _sanitize_input(self, input_data: str) -> str:
        """Sanitize input data for logging"""
        import re
        
        # Mask personal information
        sanitized = re.sub(r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b', '[CARD]', input_data)
        sanitized = re.sub(r'\b\d{3}[\s-]?\d{2}[\s-]?\d{4}\b', '[SSN]', sanitized)
        
        return sanitized
    
    def monitor_bias(self, model_outputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Monitor for bias in AI model outputs"""
        
        bias_metrics = {
            "gender_bias": 0.0,
            "accent_bias": 0.0,
            "language_bias": 0.0
        }
        
        total_outputs = len(model_outputs)
        
        if total_outputs > 0:
            for output in model_outputs:
                if "gender" in output and output["gender"] == "female":
                    bias_metrics["gender_bias"] += 1
                if "accent" in output and output["accent"] != "standard":
                    bias_metrics["accent_bias"] += 1
            
            # Normalize metrics
            for key in bias_metrics:
                bias_metrics[key] = bias_metrics[key] / total_outputs
        
        return {
            "timestamp": datetime.now(),
            "bias_metrics": bias_metrics,
            "total_samples": total_outputs,
            "bias_detected": any(metric > 0.1 for metric in bias_metrics.values())
        }