┌─────────────────────────────────────────────────────────────┐
│                 Orchestrator Agent                          │
│           (Coordinates all AI processes)                    │
└─────────────────┬───────────────────────────────────────────┘
                  │
    ┌─────────────┼─────────────┐
    │             │             │
    ▼             ▼             ▼
┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐
│Sentiment│  │ Skill   │  │Cultural │  │Body Lang│  │Worker   │
│Analysis │  │Assessment│  │Fit      │  │Analysis │  │Agent    │
│Agent    │  │Agent     │  │Agent    │  │Agent    │  │         │
└─────────┘  └─────────┘  └─────────┘  └─────────┘  └─────────┘

class OrchestratorAgent:
    def __init__(self):
        self.sentiment_agent = EnhancedSentimentAgent()
        self.skill_agent = SkillAssessmentAgent()
        self.cultural_agent = CulturalFitAgent()
        self.worker_agent = WorkerAgent()
        
    async def process_interview(self, interview_session):
        """Process complete interview through AI pipeline."""
        # Start parallel processing
        tasks = [
            self.sentiment_agent.analyze_session(interview_session),
            self.skill_agent.assess_skills(interview_session),
            self.cultural_agent.evaluate_fit(interview_session)
        ]
        
        # Wait for all analyses to complete
        results = await asyncio.gather(*tasks)
        
        # Aggregate and validate results
        final_score = self.calculate_weighted_score(results)
        recommendations = self.generate_recommendations(results)
        
        return {
            'overall_score': final_score,
            'detailed_analysis': results,
            'recommendations': recommendations
        }

class EnhancedSentimentAgent:
    def __init__(self):
        self.base_analyzer = pipeline("sentiment-analysis", 
                                    model="cardiffnlp/twitter-roberta-base-sentiment-latest")
        self.emotion_analyzer = pipeline("text-classification",
                                       model="j-hartmann/emotion-english-distilroberta-base")
        self.context_analyzer = ContextualAnalyzer()
        
    def analyze_sentiment_with_context(self, text, context_factors):
        # Base sentiment analysis
        base_sentiment = self.base_analyzer(text)
        
        # Emotional intelligence analysis
        emotions = self.emotion_analyzer(text)
        
        # Context-aware enhancement
        context_scores = self.analyze_context_factors(text, context_factors)
        
        # Weighted combination for final score
        enhanced_sentiment = self.calculate_enhanced_score(
            base_sentiment, emotions, context_scores
        )
        
        return enhanced_sentiment

class ContextualAnalyzer:
    def analyze_context_factors(self, text, job_context):
        return {
            'value_alignment': self.assess_value_alignment(text, job_context.company_values),
            'requirement_addressing': self.check_requirement_coverage(text, job_context.requirements),
            'response_relevance': self.calculate_relevance_score(text, job_context.question),
            'confidence_indicators': self.detect_confidence_markers(text),
            'enthusiasm_level': self.measure_enthusiasm(text),
            'authenticity_score': self.assess_authenticity(text)
        }
    
    def assess_value_alignment(self, text, company_values):
        """Measures alignment with company values."""
        alignment_indicators = {
            'innovation': ['creative', 'innovative', 'new approach', 'problem-solving'],
            'collaboration': ['team', 'together', 'collective', 'partnership'],
            'integrity': ['honest', 'transparent', 'ethical', 'responsibility'],
            'excellence': ['quality', 'best', 'exceed', 'outstanding']
        }
        
        scores = {}
        for value, indicators in alignment_indicators.items():
            score = sum(1 for indicator in indicators if indicator in text.lower())
            scores[value] = min(score / len(indicators), 1.0)
            
        return scores

class RealTimeSentimentTracker:
    def __init__(self, websocket_connection):
        self.connection = websocket_connection
        self.sentiment_history = []
        
    async def track_live_sentiment(self, audio_stream):
        """Process live audio for real-time sentiment analysis."""
        async for audio_chunk in audio_stream:
            # Convert audio to text
            text = await self.transcribe_audio(audio_chunk)
            
            # Analyze sentiment
            sentiment = self.sentiment_agent.analyze(text)
            
            # Track sentiment over time
            self.sentiment_history.append({
                'timestamp': datetime.now(),
                'sentiment': sentiment,
                'text': text
            })
            
            # Send real-time updates
            await self.connection.send_json({
                'type': 'sentiment_update',
                'data': sentiment
            })

class SkillAssessmentAgent:
    def __init__(self):
        self.skill_extractors = {
            'technical': TechnicalSkillExtractor(),
            'communication': CommunicationSkillExtractor(),
            'leadership': LeadershipSkillExtractor(),
            'problem_solving': ProblemSolvingExtractor()
        }
        
    async def assess_skills(self, interview_session):
        """Comprehensive skill assessment."""
        job_requirements = interview_session.job.requirements
        transcript = interview_session.get_transcript()
        
        # Extract job-specific skills
        required_skills = self.parse_job_requirements(job_requirements)
        
        # Assess each skill category
        skill_scores = {}
        for category, extractor in self.skill_extractors.items():
            scores = await extractor.extract_and_score(
                transcript, required_skills
            )
            skill_scores[category] = scores
            
        # Calculate weighted overall score
        overall_score = self.calculate_weighted_skill_score(
            skill_scores, required_skills
        )
        
        return {
            'overall_score': overall_score,
            'skill_breakdown': skill_scores,
            'improvement_areas': self.identify_gaps(skill_scores, required_skills)
        }

class TechnicalSkillExtractor:
    def __init__(self):
        self.skill_patterns = {
            'programming_languages': {
                'python': ['python', 'django', 'flask', 'pandas', 'numpy'],
                'javascript': ['javascript', 'js', 'node', 'react', 'vue', 'angular'],
                'java': ['java', 'spring', 'maven', 'gradle'],
                'sql': ['sql', 'database', 'query', 'select', 'join']
            },
            'frameworks': {
                'web_frameworks': ['django', 'flask', 'express', 'spring boot'],
                'frontend_frameworks': ['react', 'angular', 'vue', 'svelte'],
                'mobile_frameworks': ['react native', 'flutter', 'ionic']
            },
            'concepts': {
                'algorithms': ['algorithm', 'complexity', 'big o', 'optimization'],
                'design_patterns': ['singleton', 'factory', 'observer', 'mvc'],
                'architecture': ['microservices', 'rest', 'api', 'scalability']
            }
        }
        
    async def extract_and_score(self, transcript, required_skills):
        """Extract and score technical skills from transcript."""
        skill_mentions = self.identify_skill_mentions(transcript)
        depth_analysis = self.analyze_skill_depth(transcript, skill_mentions)
        practical_experience = self.assess_practical_experience(transcript)
        
        scores = {}
        for skill in required_skills:
            base_score = self.calculate_base_score(skill, skill_mentions)
            depth_bonus = depth_analysis.get(skill, 0)
            experience_bonus = practical_experience.get(skill, 0)
            
            final_score = min(base_score + depth_bonus + experience_bonus, 10.0)
            scores[skill] = final_score
            
        return scores

class CommunicationSkillExtractor:
    def __init__(self):
        self.language_model = pipeline("text-classification", 
                                     model="microsoft/DialoGPT-medium")
        
    async def extract_and_score(self, transcript, context):
        """Analyze communication effectiveness."""
        analysis = {
            'clarity': self.assess_clarity(transcript),
            'structure': self.analyze_response_structure(transcript),
            'engagement': self.measure_engagement_level(transcript),
            'listening_skills': self.evaluate_listening(transcript, context),
            'persuasiveness': self.assess_persuasiveness(transcript)
        }
        
        # Calculate overall communication score
        weights = {
            'clarity': 0.3,
            'structure': 0.2,
            'engagement': 0.2,
            'listening_skills': 0.15,
            'persuasiveness': 0.15
        }
        
        overall_score = sum(
            analysis[skill] * weight 
            for skill, weight in weights.items()
        )
        
        return {
            'overall_score': overall_score,
            'breakdown': analysis
        }
        
    def assess_clarity(self, transcript):
        """Measure clarity of communication."""
        # Analyze sentence structure, vocabulary complexity, filler words
        sentences = self.split_into_sentences(transcript)
        
        clarity_factors = {
            'avg_sentence_length': self.calculate_avg_sentence_length(sentences),
            'vocabulary_diversity': self.calculate_vocabulary_diversity(transcript),
            'filler_word_ratio': self.calculate_filler_ratio(transcript),
            'grammatical_correctness': self.assess_grammar(transcript)
        }
        
        # Weighted scoring
        clarity_score = (
            (1 - min(clarity_factors['filler_word_ratio'] / 0.1, 1)) * 0.3 +
            min(clarity_factors['vocabulary_diversity'] / 100, 1) * 0.3 +
            clarity_factors['grammatical_correctness'] * 0.4
        ) * 10
        
        return clarity_score

class CulturalFitAgent:
    def __init__(self):
        self.value_analyzer = ValueAlignmentAnalyzer()
        self.behavior_predictor = BehaviorPredictionModel()
        
    async def evaluate_fit(self, interview_session):
        """Comprehensive cultural fit evaluation."""
        company = interview_session.job.company
        candidate_responses = interview_session.get_responses()
        
        # Analyze value alignment
        value_scores = await self.value_analyzer.analyze_alignment(
            candidate_responses, company.values
        )
        
        # Predict cultural behaviors
        behavior_prediction = await self.behavior_predictor.predict_fit(
            candidate_responses, company.culture_profile
        )
        
        # Assess team compatibility
        team_fit = await self.assess_team_compatibility(
            candidate_responses, company.team_dynamics
        )
        
        overall_fit_score = self.calculate_cultural_fit_score(
            value_scores, behavior_prediction, team_fit
        )
        
        return {
            'overall_score': overall_fit_score,
            'value_alignment': value_scores,
            'behavior_prediction': behavior_prediction,
            'team_compatibility': team_fit,
            'recommendations': self.generate_fit_recommendations(overall_fit_score)
        }

class BehaviorPredictionModel:
    def __init__(self):
        self.personality_model = pipeline("text-classification",
                                        model="personality-prediction-model")
        
    async def predict_fit(self, responses, culture_profile):
        """Predict cultural fit based on behavioral patterns."""
        # Extract personality traits
        personality_traits = self.extract_personality_traits(responses)
        
        # Analyze work style preferences
        work_style = self.analyze_work_style(responses)
        
        # Assess leadership potential
        leadership_style = self.assess_leadership_style(responses)
        
        # Calculate fit with company culture
        culture_match = self.calculate_culture_match(
            personality_traits, work_style, leadership_style, culture_profile
        )
        
        return {
            'personality_match': culture_match['personality'],
            'work_style_match': culture_match['work_style'],
            'leadership_match': culture_match['leadership'],
            'overall_prediction': culture_match['overall']
        }

class STARAnalysisAgent:
    def __init__(self):
        self.component_detector = STARComponentDetector()
        
    def analyze_star_response(self, response_text, question_context):
        """Analyze response using STAR methodology."""
        components = {
            'situation': self.component_detector.detect_situation(response_text),
            'task': self.component_detector.detect_task(response_text),
            'action': self.component_detector.detect_action(response_text),
            'result': self.component_detector.detect_result(response_text)
        }
        
        # Evaluate completeness and quality
        completeness_score = self.calculate_completeness(components)
        quality_score = self.assess_quality(components, question_context)
        
        # Generate feedback and follow-up questions
        feedback = self.generate_star_feedback(components)
        follow_up = self.generate_follow_up_questions(components)
        
        return {
            'components': components,
            'completeness_score': completeness_score,
            'quality_score': quality_score,
            'feedback': feedback,
            'follow_up': follow_up
        }

class BodyLanguageAnalyzer:
    def __init__(self):
        self.face_detector = FaceDetectionModel()
        self.emotion_classifier = EmotionClassificationModel()
        self.gesture_recognizer = GestureRecognitionModel()
        
    async def analyze_video_stream(self, video_stream):
        """Analyze body language and facial expressions."""
        analysis_results = []
        
        async for frame in video_stream:
            # Detect faces and extract features
            faces = self.face_detector.detect_faces(frame)
            
            for face in faces:
                # Analyze facial expressions
                emotions = self.emotion_classifier.classify_emotions(face)
                
                # Detect micro-expressions
                micro_expressions = self.detect_micro_expressions(face)
                
                # Analyze eye contact and attention
                attention_metrics = self.analyze_attention(face)
                
                analysis_results.append({
                    'timestamp': datetime.now(),
                    'emotions': emotions,
                    'micro_expressions': micro_expressions,
                    'attention': attention_metrics
                })
        
        # Aggregate results over time
        behavioral_summary = self.aggregate_behavioral_data(analysis_results)
        
        return behavioral_summary

class SpeechAnalysisEngine:
    def __init__(self):
        self.transcriber = AWS_Transcribe()
        self.audio_analyzer = AudioFeatureExtractor()
        
    async def analyze_audio(self, audio_stream):
        """Comprehensive audio analysis."""
        # Real-time transcription
        transcript = await self.transcriber.transcribe_stream(audio_stream)
        
        # Extract audio features
        audio_features = self.audio_analyzer.extract_features(audio_stream)
        
        # Analyze speech patterns
        speech_analysis = {
            'pace': self.calculate_speech_pace(audio_features),
            'tone_variation': self.analyze_tone_variation(audio_features),
            'confidence_indicators': self.detect_confidence_in_voice(audio_features),
            'filler_words': self.count_filler_words(transcript),
            'clarity': self.assess_speech_clarity(audio_features)
        }
        
        return {
            'transcript': transcript,
            'speech_metrics': speech_analysis,
            'overall_communication_score': self.calculate_communication_score(speech_analysis)
        }

class FillerWordsAgent:
    def __init__(self):
        self.filler_patterns = [
            'um', 'uh', 'like', 'you know', 'actually', 'basically',
            'literally', 'so', 'well', 'I mean', 'kind of', 'sort of'
        ]
        
    def analyze_filler_usage(self, transcript):
        """Analyze and track filler word usage."""
        words = transcript.lower().split()
        total_words = len(words)
        
        filler_counts = {}
        total_fillers = 0
        
        for filler in self.filler_patterns:
            count = transcript.lower().count(filler)
            filler_counts[filler] = count
            total_fillers += count
        
        filler_ratio = total_fillers / total_words if total_words > 0 else 0
        
        # Generate improvement suggestions
        suggestions = self.generate_filler_reduction_tips(filler_counts)
        
        return {
            'filler_ratio': filler_ratio,
            'filler_breakdown': filler_counts,
            'total_fillers': total_fillers,
            'total_words': total_words,
            'improvement_suggestions': suggestions
        }

class RecommendationsAgent:
    def __init__(self):
        self.skill_gap_analyzer = SkillGapAnalyzer()
        self.resource_matcher = LearningResourceMatcher()
        
    async def generate_learning_path(self, interview_results):
        """Generate personalized learning recommendations."""
        # Identify skill gaps
        skill_gaps = self.skill_gap_analyzer.identify_gaps(
            interview_results['skill_scores'],
            interview_results['job_requirements']
        )
        
        # Prioritize gaps based on job importance
        prioritized_gaps = self.prioritize_skill_gaps(
            skill_gaps, interview_results['job_focus']
        )
        
        # Match learning resources
        learning_resources = []
        for gap in prioritized_gaps:
            resources = await self.resource_matcher.find_resources(
                skill=gap['skill'],
                current_level=gap['current_score'],
                target_level=gap['target_score'],
                learning_style=interview_results.get('learning_style', 'mixed')
            )
            learning_resources.extend(resources)
        
        # Create structured learning path
        learning_path = self.create_learning_path(learning_resources, prioritized_gaps)
        
        return {
            'skill_gaps': prioritized_gaps,
            'learning_path': learning_path,
            'estimated_time': self.calculate_learning_time(learning_path),
            'success_probability': self.predict_success_rate(learning_path, interview_results)
        }

class KnowledgeGapAnalyzer:
    def analyze_knowledge_gaps(self, interview_session):
        """Identify specific knowledge gaps and learning opportunities."""
        transcript = interview_session.get_transcript()
        job_requirements = interview_session.job.requirements
        
        # Parse technical concepts from job requirements
        required_concepts = self.extract_technical_concepts(job_requirements)
        
        # Analyze candidate's knowledge demonstration
        demonstrated_knowledge = self.extract_demonstrated_knowledge(transcript)
        
        # Identify gaps
        knowledge_gaps = []
        for concept in required_concepts:
            if concept not in demonstrated_knowledge:
                gap_severity = self.assess_gap_severity(concept, job_requirements)
                knowledge_gaps.append({
                    'concept': concept,
                    'severity': gap_severity,
                    'learning_resources': self.suggest_learning_resources(concept),
                    'estimated_learning_time': self.estimate_learning_time(concept)
                })
        
        return knowledge_gaps

class ModelTrainingPipeline:
    def __init__(self):
        self.model_registry = ModelRegistry()
        self.data_collector = TrainingDataCollector()
        
    async def retrain_models(self):
        """Continuously improve AI models with new data."""
        # Collect new training data
        training_data = await self.data_collector.collect_recent_data()
        
        # Validate data quality
        validated_data = self.validate_training_data(training_data)
        
        # Retrain models
        for model_name, model in self.model_registry.get_models():
            if self.should_retrain(model, validated_data):
                new_model = await self.retrain_model(model, validated_data)
                
                # Validate model performance
                if self.validate_model_performance(new_model, model):
                    self.model_registry.update_model(model_name, new_model)
                    
    def validate_model_performance(self, new_model, current_model):
        """Validate that new model performs better than current."""
        test_data = self.get_test_dataset()
        
        new_accuracy = self.calculate_accuracy(new_model, test_data)
        current_accuracy = self.calculate_accuracy(current_model, test_data)
        
        return new_accuracy > current_accuracy * 1.02  # 2% improvement threshold

class SentimentExperimentManager:
    def __init__(self):
        self.control_model = StandardSentimentModel()
        self.treatment_model = EnhancedSentimentModel()
        
    def assign_experiment_group(self, interview_session):
        """Assign interview to control or treatment group."""
        # Deterministic assignment based on session ID
        group = 'treatment' if hash(interview_session.session_id) % 2 == 0 else 'control'
        
        return group
        
    async def run_experiment(self, interview_session):
        """Run A/B test comparing sentiment models."""
        group = self.assign_experiment_group(interview_session)
        
        if group == 'treatment':
            sentiment_score = await self.treatment_model.analyze(interview_session)
        else:
            sentiment_score = await self.control_model.analyze(interview_session)
            
        # Record experiment results
        SentimentExperiment.record_experiment(
            interview_session=interview_session,
            experiment_group=group,
            sentiment_score=sentiment_score
        )
        
        return sentiment_score

class AIPerformanceOptimizer:
    def __init__(self):
        self.cache = RedisCache()
        self.async_processor = AsyncProcessor()
        
    async def optimize_processing(self, interview_session):
        """Optimize AI processing for speed and accuracy."""
        # Check cache for similar interviews
        cache_key = self.generate_cache_key(interview_session)
        cached_result = await self.cache.get(cache_key)
        
        if cached_result and self.is_cache_valid(cached_result):
            return cached_result
            
        # Process with optimized pipeline
        results = await self.process_with_pipeline(interview_session)
        
        # Cache results for future use
        await self.cache.set(cache_key, results, ttl=3600)
        
        return results
        
    async def process_with_pipeline(self, interview_session):
        """Process interview through optimized AI pipeline."""
        # Parallel processing of independent analyses
        sentiment_task = self.async_processor.submit(
            self.sentiment_agent.analyze, interview_session
        )
        skill_task = self.async_processor.submit(
            self.skill_agent.assess, interview_session
        )
        cultural_task = self.async_processor.submit(
            self.cultural_agent.evaluate, interview_session
        )
        
        # Wait for all tasks to complete
        sentiment_result = await sentiment_task
        skill_result = await skill_task
        cultural_result = await cultural_task
        
        # Aggregate results
        return self.aggregate_results([sentiment_result, skill_result, cultural_result])

class AIErrorHandler:
    def __init__(self):
        self.fallback_models = {
            'sentiment': BasicSentimentModel(),
            'skills': RuleBasedSkillExtractor(),
            'cultural_fit': SimpleCompatibilityMatcher()
        }
        
    async def safe_ai_processing(self, interview_session):
        """Process interview with error handling and fallbacks."""
        results = {}
        
        for component, agent in self.ai_agents.items():
            try:
                result = await agent.process(interview_session)
                results[component] = result
                
            except AIProcessingError as e:
                logger.warning(f"AI component {component} failed: {e}")
                
                # Use fallback model
                fallback_agent = self.fallback_models.get(component)
                if fallback_agent:
                    result = await fallback_agent.process(interview_session)
                    results[component] = result
                    results[f'{component}_fallback_used'] = True
                else:
                    results[component] = self.get_default_result(component)
                    
            except Exception as e:
                logger.error(f"Unexpected error in {component}: {e}")
                results[component] = self.get_default_result(component)
                
        return results

class AIMonitoringSystem:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        
    def track_ai_performance(self, component, processing_time, accuracy_score):
        """Track AI component performance metrics."""
        self.metrics_collector.record_metric(
            metric_name=f'ai.{component}.processing_time',
            value=processing_time,
            tags=['component:' + component]
        )
        
        self.metrics_collector.record_metric(
            metric_name=f'ai.{component}.accuracy',
            value=accuracy_score,
            tags=['component:' + component]
        )
        
    def generate_ai_health_report(self):
        """Generate comprehensive AI system health report."""
        return {
            'component_health': self.check_component_health(),
            'performance_metrics': self.get_performance_metrics(),
            'error_rates': self.calculate_error_rates(),
            'recommendations': self.generate_optimization_recommendations()
        }