# Conceptual framework for AI emoji generation (inspired by Genmoji)
class AIEmojiGenerator:
    def __init__(self, model_path, style_guidelines):
        self.text_encoder = self.load_text_encoder(model_path)
        self.image_generator = self.load_image_generator(model_path)
        self.style_guidelines = style_guidelines
        self.consistency_checker = ConsistencyChecker(style_guidelines)
    
    async def generate_emoji(self, text_description, user_context=None):
        """Generate custom emoji from text description"""
        
        # Parse and understand the text description
        parsed_request = await self.parse_description(text_description)
        
        # Extract key visual elements
        visual_elements = self.extract_visual_elements(parsed_request)
        
        # Generate base emoji design
        base_design = await self.image_generator.generate(
            prompt=self.construct_generation_prompt(visual_elements),
            style_constraints=self.style_guidelines,
            user_preferences=user_context
        )
        
        # Apply consistency and quality checks
        refined_design = await self.refine_design(base_design, visual_elements)
        
        # Ensure platform compatibility
        final_emoji = await self.optimize_for_platforms(refined_design)
        
        return {
            'emoji': final_emoji,
            'metadata': self.generate_metadata(text_description, visual_elements),
            'alternatives': await self.generate_variations(final_emoji, 3),
            'usage_suggestions': self.suggest_usage_contexts(visual_elements)
        }
    
    def construct_generation_prompt(self, visual_elements):
        """Create optimized prompt for emoji generation model"""
        base_prompt = f"Generate a custom emoji featuring: {visual_elements['primary_subject']}"
        
        if visual_elements['emotions']:
            base_prompt += f" expressing {visual_elements['emotions']}"
        
        if visual_elements['actions']:
            base_prompt += f" performing {visual_elements['actions']}"
        
        if visual_elements['objects']:
            base_prompt += f" with {visual_elements['objects']}"
        
        # Add style constraints
        base_prompt += f". Style: {self.style_guidelines['aesthetic']}, "
        base_prompt += f"Color palette: {self.style_guidelines['colors']}, "
        base_prompt += f"Resolution: {self.style_guidelines['resolution']}"
        
        return base_prompt
    
    async def refine_design(self, base_design, visual_elements):
        """Apply AI-powered refinement to ensure quality and consistency"""
        refinement_iterations = 0
        current_design = base_design
        
        while refinement_iterations < 3:
            quality_score = await self.assess_design_quality(current_design)
            consistency_score = self.consistency_checker.evaluate(current_design)
            
            if quality_score > 0.85 and consistency_score > 0.9:
                break
                
            # Apply targeted improvements
            improvement_suggestions = self.identify_improvements(
                current_design, quality_score, consistency_score
            )
            
            current_design = await self.apply_improvements(
                current_design, improvement_suggestions
            )
            
            refinement_iterations += 1
        
        return current_design

# Diffusion-based emoji generation system
import torch
from diffusers import StableDiffusionPipeline
from transformers import CLIPTextModel, CLIPTokenizer

class EmojiDiffusionGenerator:
    def __init__(self, model_id="stable-diffusion-v1-5"):
        self.pipeline = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            use_safetensors=True
        )
        
        # Specialized emoji fine-tuning
        self.emoji_lora = self.load_emoji_specific_weights()
        self.pipeline.unet.load_attn_procs(self.emoji_lora)
        
        # Style transfer components
        self.style_encoder = self.load_style_encoder()
        self.emoji_postprocessor = EmojiPostProcessor()
    
    async def generate_custom_emoji(self, description, style_reference=None):
        """Generate emoji using diffusion models with style control"""
        
        # Construct optimized prompt for emoji generation
        emoji_prompt = self.optimize_prompt_for_emoji(description)
        
        # Apply negative prompting to avoid unwanted elements
        negative_prompt = self.construct_negative_prompt()
        
        # Generate multiple candidates
        candidates = []
        for i in range(4):  # Generate 4 candidates
            image = self.pipeline(
                prompt=emoji_prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=50,
                guidance_scale=7.5,
                width=512,
                height=512,
                generator=torch.Generator().manual_seed(42 + i)
            ).images[0]
            
            # Post-process for emoji format
            processed_emoji = await self.emoji_postprocessor.process(
                image,
                target_size=(128, 128),
                background_removal=True,
                emoji_styling=True
            )
            
            candidates.append(processed_emoji)
        
        # Select best candidate using quality metrics
        best_emoji = await self.select_best_candidate(candidates, description)
        
        return {
            'primary_emoji': best_emoji,
            'alternatives': candidates,
            'generation_metadata': {
                'prompt': emoji_prompt,
                'model_version': self.pipeline.scheduler.config._class_name,
                'style_applied': style_reference is not None
            }
        }
    
    def optimize_prompt_for_emoji(self, description):
        """Optimize natural language description for emoji generation"""
        # Add emoji-specific style markers
        optimized = f"cute emoji of {description}, "
        optimized += "simple design, clean lines, vibrant colors, "
        optimized += "white background, centered composition, "
        optimized += "digital art style, high quality"
        
        return optimized
    
    def construct_negative_prompt(self):
        """Define elements to avoid in emoji generation"""
        return (
            "realistic, photograph, complex background, "
            "dark colors, low quality, blurry, distorted, "
            "multiple subjects, text, watermark"
        )

// Multimodal emoji generation system
class MultimodalEmojiAI {
    constructor() {
        this.textProcessor = new TextUnderstandingEngine();
        this.visualProcessor = new VisualAnalysisEngine();
        this.emotionAnalyzer = new EmotionDetectionEngine();
        this.contextProcessor = new ContextAnalysisEngine();
        this.generationEngine = new UnifiedGenerationEngine();
    }
    
    async generateFromMultipleInputs(inputs) {
        const processedInputs = {
            text: inputs.description ? 
                await this.textProcessor.analyze(inputs.description) : null,
            
            visual_reference: inputs.referenceImage ? 
                await this.visualProcessor.extractFeatures(inputs.referenceImage) : null,
            
            emotional_context: inputs.emotionalState ? 
                await this.emotionAnalyzer.processEmotion(inputs.emotionalState) : null,
            
            usage_context: inputs.intendedUse ? 
                await this.contextProcessor.analyzeContext(inputs.intendedUse) : null,
                
            user_preferences: inputs.userProfile ? 
                this.extractUserPreferences(inputs.userProfile) : null
        };
        
        // Synthesize all inputs into unified generation parameters
        const generationParameters = await this.synthesizeInputs(processedInputs);
        
        // Generate emoji with multimodal guidance
        const generatedEmoji = await this.generationEngine.generate(generationParameters);
        
        return {
            emoji: generatedEmoji,
            input_analysis: processedInputs,
            generation_reasoning: this.explainGenerationDecisions(generationParameters),
            confidence_scores: this.calculateConfidenceMetrics(processedInputs)
        };
    }
    
    async synthesizeInputs(processedInputs) {
        """Combine multiple input modalities into coherent generation parameters"""
        const synthesis = {
            primary_concepts: [],
            style_guidance: {},
            emotional_tone: {},
            contextual_constraints: {}
        };
        
        // Extract primary concepts from text
        if (processedInputs.text) {
            synthesis.primary_concepts.push(...processedInputs.text.keyEntities);
        }
        
        // Incorporate visual style from reference image
        if (processedInputs.visual_reference) {
            synthesis.style_guidance = {
                color_palette: processedInputs.visual_reference.dominantColors,
                composition_style: processedInputs.visual_reference.compositionType,
                artistic_style: processedInputs.visual_reference.styleClassification
            };
        }
        
        // Apply emotional context
        if (processedInputs.emotional_context) {
            synthesis.emotional_tone = {
                primary_emotion: processedInputs.emotional_context.dominantEmotion,
                intensity: processedInputs.emotional_context.intensity,
                secondary_emotions: processedInputs.emotional_context.secondaryEmotions
            };
        }
        
        // Add contextual constraints
        if (processedInputs.usage_context) {
            synthesis.contextual_constraints = {
                platform_requirements: processedInputs.usage_context.platformSpecs,
                audience_appropriateness: processedInputs.usage_context.audienceLevel,
                cultural_considerations: processedInputs.usage_context.culturalFactors
            };
        }
        
        return synthesis;
    }
}

# Analytics framework for AI emoji generation systems
class AIEmojiAnalytics {
    def __init__(self):
        self.metrics = {
            'generation_success_rate': 0.0,
            'user_satisfaction_score': 0.0,
            'average_generation_time': 0.0,
            'style_consistency_score': 0.0,
            'usage_retention_rate': 0.0
        }
    
    def track_generation_attempt(self, request, result, user_feedback):
        """Track individual emoji generation attempts"""
        generation_data = {
            'timestamp': datetime.now(),
            'user_id': request.user_id,
            'description': request.description,
            'generation_time': result.processing_time,
            'success': result.status == 'success',
            'quality_score': result.quality_assessment,
            'user_accepted': user_feedback.accepted,
            'satisfaction_rating': user_feedback.rating
        }
        
        self.store_generation_data(generation_data)
        self.update_performance_metrics(generation_data)
        
        return generation_data
    
    def analyze_usage_patterns(self, time_period='30_days'):
        """Analyze how AI-generated emojis are being used"""
        usage_data = self.query_usage_data(time_period)
        
        patterns = {
            'popular_generation_types': self.identify_popular_types(usage_data),
            'peak_generation_times': self.analyze_temporal_patterns(usage_data),
            'user_learning_curves': self.track_user_improvement(usage_data),
            'failure_pattern_analysis': self.analyze_failures(usage_data)
        }
        
        return patterns

# Collaborative filtering system for emoji recommendations
import numpy as np
from sklearn.decomposition import NMF
from sklearn.metrics.pairwise import cosine_similarity

class EmojiCollaborativeFiltering:
    def __init__(self, min_interactions=5):
        self.min_interactions = min_interactions
        self.user_emoji_matrix = None
        self.emoji_features = None
        self.user_features = None
        self.model = NMF(n_components=50, random_state=42)
    
    def build_user_emoji_matrix(self, interaction_data):
        """Build matrix of user-emoji interactions"""
        # Create user-emoji interaction matrix
        users = sorted(interaction_data['user_id'].unique())
        emojis = sorted(interaction_data['emoji_id'].unique())
        
        matrix = np.zeros((len(users), len(emojis)))
        
        for _, row in interaction_data.iterrows():
            user_idx = users.index(row['user_id'])
            emoji_idx = emojis.index(row['emoji_id'])
            matrix[user_idx, emoji_idx] = row['interaction_weight']
        
        self.user_emoji_matrix = matrix
        self.users = users
        self.emojis = emojis
        
        return matrix
    
    def train_model(self, interaction_data):
        """Train collaborative filtering model"""
        self.build_user_emoji_matrix(interaction_data)
        
        # Apply non-negative matrix factorization
        self.user_features = self.model.fit_transform(self.user_emoji_matrix)
        self.emoji_features = self.model.components_
        
        return self.model
    
    def recommend_emojis(self, user_id, context, n_recommendations=10):
        """Generate personalized emoji recommendations"""
        if user_id not in self.users:
            return self.recommend_for_new_user(context, n_recommendations)
        
        user_idx = self.users.index(user_id)
        user_vector = self.user_features[user_idx]
        
        # Calculate emoji scores for this user
        emoji_scores = np.dot(user_vector, self.emoji_features)
        
        # Apply contextual filtering
        contextual_scores = self.apply_contextual_filters(
            emoji_scores, context, user_id
        )
        
        # Get top recommendations (excluding already used emojis)
        user_used_emojis = set(np.where(self.user_emoji_matrix[user_idx] > 0)[0])
        
        recommendations = []
        for emoji_idx in np.argsort(contextual_scores)[::-1]:
            if emoji_idx not in user_used_emojis:
                recommendations.append({
                    'emoji_id': self.emojis[emoji_idx],
                    'score': contextual_scores[emoji_idx],
                    'reasoning': self.explain_recommendation(user_id, emoji_idx)
                })
                
            if len(recommendations) >= n_recommendations:
                break
        
        return recommendations
    
    def apply_contextual_filters(self, base_scores, context, user_id):
        """Apply contextual information to refine recommendations"""
        contextual_scores = base_scores.copy()
        
        # Time-based adjustments
        if context.get('time_of_day'):
            time_multiplier = self.get_time_based_multiplier(context['time_of_day'])
            contextual_scores *= time_multiplier
        
        # Platform-specific adjustments
        if context.get('platform'):
            platform_weights = self.get_platform_weights(context['platform'])
            contextual_scores *= platform_weights
        
        # Conversation context adjustments
        if context.get('conversation_sentiment'):
            sentiment_weights = self.get_sentiment_weights(context['conversation_sentiment'])
            contextual_scores *= sentiment_weights
        
        # Social group context
        if context.get('group_context'):
            group_weights = self.get_group_weights(context['group_context'], user_id)
            contextual_scores *= group_weights
        
        return contextual_scores

# Deep learning model for contextual emoji suggestion
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModel

class ContextualEmojiSuggestionModel:
    def __init__(self, model_name="bert-base-uncased"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.text_encoder = TFAutoModel.from_pretrained(model_name)
        self.emoji_classifier = self.build_emoji_classifier()
        self.context_analyzer = ContextAnalyzer()
        
    def build_emoji_classifier(self):
        """Build neural network for emoji classification"""
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(512, activation='relu', input_shape=(768,)),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(256, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dense(1000, activation='softmax')  # 1000 emoji classes
        ])
        
        model.compile(
            optimizer='adam',
            loss='categorical_crossentropy',
            metrics=['accuracy', 'top_k_categorical_accuracy']
        )
        
        return model
    
    async def suggest_emojis_for_message(self, message_text, conversation_history, user_profile):
        """Suggest emojis based on message content and context"""
        
        # Encode message text
        message_encoding = await self.encode_text(message_text)
        
        # Analyze conversation context
        context_features = await self.context_analyzer.analyze_conversation(
            conversation_history
        )
        
        # Extract user preference features
        user_features = self.extract_user_features(user_profile)
        
        # Combine all features
        combined_features = tf.concat([
            message_encoding,
            context_features,
            user_features
        ], axis=1)
        
        # Get emoji predictions
        emoji_probabilities = self.emoji_classifier.predict(combined_features)
        
        # Post-process predictions with business rules
        filtered_suggestions = await self.apply_suggestion_filters(
            emoji_probabilities,
            message_text,
            conversation_history,
            user_profile
        )
        
        return filtered_suggestions
    
    async def encode_text(self, text):
        """Encode text using transformer model"""
        tokens = self.tokenizer(
            text,
            return_tensors='tf',
            truncation=True,
            padding=True,
            max_length=512
        )
        
        outputs = self.text_encoder(tokens)
        
        # Use [CLS] token representation
        return outputs.last_hidden_state[:, 0, :]
    
    async def apply_suggestion_filters(self, predictions, message_text, history, profile):
        """Apply business logic and filters to raw predictions"""
        filtered_suggestions = []
        
        # Get top-k predictions
        top_k_indices = tf.nn.top_k(predictions, k=20).indices.numpy()[0]
        top_k_scores = tf.nn.top_k(predictions, k=20).values.numpy()[0]
        
        for idx, score in zip(top_k_indices, top_k_scores):
            emoji_id = self.idx_to_emoji_id(idx)
            
            # Apply contextual appropriateness filter
            if await self.is_contextually_appropriate(emoji_id, message_text, history):
                # Apply user preference weighting
                user_weight = self.get_user_preference_weight(emoji_id, profile)
                adjusted_score = score * user_weight
                
                # Apply diversity filter (avoid suggesting too similar emojis)
                if self.is_sufficiently_diverse(emoji_id, filtered_suggestions):
                    filtered_suggestions.append({
                        'emoji_id': emoji_id,
                        'confidence_score': adjusted_score,
                        'reasoning': self.generate_suggestion_reasoning(
                            emoji_id, message_text, adjusted_score
                        )
                    })
        
        # Sort by adjusted scores and return top suggestions
        return sorted(filtered_suggestions, key=lambda x: x['confidence_score'], reverse=True)[:5]

# Reinforcement learning system for emoji recommendation optimization
import numpy as np
from collections import defaultdict

class EmojiReinforcementLearner:
    def __init__(self, learning_rate=0.1, exploration_rate=0.1):
        self.learning_rate = learning_rate
        self.exploration_rate = exploration_rate
        self.q_table = defaultdict(lambda: defaultdict(float))
        self.state_encoder = StateEncoder()
        self.action_space = self.load_emoji_action_space()
        
    def encode_state(self, context):
        """Encode current context into state representation"""
        return self.state_encoder.encode({
            'message_sentiment': context.get('sentiment', 'neutral'),
            'conversation_type': context.get('type', 'casual'),
            'time_of_day': context.get('time_bucket', 'daytime'),
            'user_mood': context.get('user_mood', 'normal'),
            'platform': context.get('platform', 'generic'),
            'group_size': context.get('group_size', 'individual')
        })
    
    def select_action(self, state, available_emojis):
        """Select emoji action using epsilon-greedy strategy"""
        state_key = self.state_encoder.state_to_key(state)
        
        if np.random.random() < self.exploration_rate:
            # Exploration: select random emoji
            return np.random.choice(available_emojis)
        else:
            # Exploitation: select best known emoji for this state
            emoji_values = {
                emoji: self.q_table[state_key][emoji] 
                for emoji in available_emojis
            }
            return max(emoji_values, key=emoji_values.get)
    
    def update_q_value(self, state, action, reward, next_state):
        """Update Q-value based on user feedback"""
        state_key = self.state_encoder.state_to_key(state)
        next_state_key = self.state_encoder.state_to_key(next_state)
        
        # Calculate next state max value
        next_max_value = max(
            self.q_table[next_state_key].values(),
            default=0.0
        )
        
        # Update Q-value using temporal difference learning
        current_q = self.q_table[state_key][action]
        new_q = current_q + self.learning_rate * (
            reward + 0.9 * next_max_value - current_q
        )
        
        self.q_table[state_key][action] = new_q
    
    def process_user_feedback(self, interaction_data):
        """Process user feedback to calculate reward"""
        feedback_type = interaction_data['feedback_type']
        
        # Define reward structure
        rewards = {
            'used_immediately': 1.0,      # User used suggested emoji right away
            'used_later': 0.5,            # User used emoji later in conversation
            'dismissed': -0.1,            # User dismissed suggestion
            'replaced': -0.3,             # User chose different emoji instead
            'reported_inappropriate': -1.0  # User reported as inappropriate
        }
        
        base_reward = rewards.get(feedback_type, 0.0)
        
        # Apply contextual modifiers
        if interaction_data.get('conversation_continued'):
            base_reward *= 1.2  # Bonus for maintaining conversation flow
        
        if interaction_data.get('positive_response_received'):
            base_reward *= 1.1  # Bonus for positive recipient response
        
        return base_reward
    
    def get_personalized_suggestions(self, user_context, n_suggestions=5):
        """Generate personalized suggestions using learned preferences"""
        state = self.encode_state(user_context)
        state_key = self.state_encoder.state_to_key(state)
        
        # Get Q-values for all available emojis in this state
        emoji_scores = dict(self.q_table[state_key])
        
        # Add exploration bonus for rarely suggested emojis
        for emoji in self.action_space:
            if emoji not in emoji_scores:
                emoji_scores[emoji] = 0.0
            
            # Bonus for emoji diversity
            usage_frequency = self.get_emoji_usage_frequency(emoji, user_context['user_id'])
            diversity_bonus = max(0.1 - usage_frequency, 0)
            emoji_scores[emoji] += diversity_bonus
        
        # Sort and return top suggestions
        top_suggestions = sorted(
            emoji_scores.items(),
            key=lambda x: x[1],
            reverse=True
        )[:n_suggestions]
        
        return [
            {
                'emoji': emoji,
                'confidence': score,
                'learning_confidence': self.calculate_learning_confidence(state_key, emoji)
            }
            for emoji, score in top_suggestions
        ]

# Trend analysis and prediction system for emojis
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import numpy as np

class EmojiTrendPredictor:
    def __init__(self):
        self.trend_model = RandomForestRegressor(n_estimators=100)
        self.scaler = StandardScaler()
        self.feature_extractors = [
            TemporalFeatureExtractor(),
            SocialFeatureExtractor(),
            CulturalEventExtractor(),
            SemanticFeatureExtractor()
        ]
    
    def extract_trend_features(self, emoji_usage_data, external_data):
        """Extract features for trend prediction"""
        features = {}
        
        # Temporal features
        temporal_features = self.feature_extractors[0].extract(emoji_usage_data)
        features.update(temporal_features)
        
        # Social context features
        social_features = self.feature_extractors[1].extract(
            emoji_usage_data, external_data.get('social_media', {})
        )
        features.update(social_features)
        
        # Cultural event features
        cultural_features = self.feature_extractors[2].extract(
            external_data.get('events', {}), emoji_usage_data
        )
        features.update(cultural_features)
        
        # Semantic similarity features
        semantic_features = self.feature_extractors[3].extract(emoji_usage_data)
        features.update(semantic_features)
        
        return features
    
    def train_trend_model(self, historical_data):
        """Train model to predict emoji usage trends"""
        training_features = []
        training_targets = []
        
        for date, day_data in historical_data.items():
            features = self.extract_trend_features(
                day_data['usage'],
                day_data['external_context']
            )
            
            # Prepare feature vector
            feature_vector = [
                features['daily_usage_count'],
                features['unique_user_count'],
                features['conversation_penetration'],
                features['sentiment_association'],
                features['cultural_event_correlation'],
                features['seasonal_adjustment'],
                features['social_media_mentions'],
                features['semantic_cluster_activity']
            ]
            
            training_features.append(feature_vector)
            
            # Target: usage growth rate for next week
            next_week_usage = day_data.get('future_usage', 0)
            current_usage = features['daily_usage_count']
            growth_rate = (next_week_usage - current_usage) / current_usage if current_usage > 0 else 0
            training_targets.append(growth_rate)
        
        # Normalize features and train model
        X_train = self.scaler.fit_transform(training_features)
        y_train = np.array(training_targets)
        
        self.trend_model.fit(X_train, y_train)
        
        return self.trend_model
    
    def predict_emoji_trends(self, current_data, prediction_horizon='7_days'):
        """Predict emoji usage trends for specified time horizon"""
        predictions = {}
        
        for emoji_id, usage_data in current_data.items():
            features = self.extract_trend_features(
                usage_data,
                current_data.get('external_context', {})
            )
            
            feature_vector = np.array([[
                features['daily_usage_count'],
                features['unique_user_count'],
                features['conversation_penetration'],
                features['sentiment_association'],
                features['cultural_event_correlation'],
                features['seasonal_adjustment'],
                features['social_media_mentions'],
                features['semantic_cluster_activity']
            ]])
            
            # Normalize and predict
            X_normalized = self.scaler.transform(feature_vector)
            predicted_growth = self.trend_model.predict(X_normalized)[0]
            
            # Calculate confidence interval
            confidence = self.calculate_prediction_confidence(
                feature_vector, usage_data
            )
            
            predictions[emoji_id] = {
                'predicted_growth_rate': predicted_growth,
                'confidence_score': confidence,
                'trend_classification': self.classify_trend(predicted_growth),
                'contributing_factors': self.identify_trend_drivers(features)
            }
        
        return predictions
    
    def classify_trend(self, growth_rate):
        """Classify trend based on predicted growth rate"""
        if growth_rate > 0.5:
            return 'rapidly_growing'
        elif growth_rate > 0.1:
            return 'growing'
        elif growth_rate > -0.1:
            return 'stable'
        elif growth_rate > -0.3:
            return 'declining'
        else:
            return 'rapidly_declining'

// AR emoji integration framework
class ARCustomEmojiSystem {
    constructor(arEngine) {
        this.arEngine = arEngine;
        this.spatialTracker = new SpatialTrackingSystem();
        this.emojiRenderer = new ThreeDEmojiRenderer();
        this.gestureRecognizer = new GestureRecognitionEngine();
        this.contextMapper = new SpatialContextMapper();
    }
    
    async initializeARSession(userPreferences) {
        """Initialize AR emoji session with user customization"""
        const arSession = await this.arEngine.startSession({
            tracking: ['world', 'face', 'hand'],
            rendering: 'high_quality',
            interaction_modes: ['gesture', 'voice', 'gaze']
        });
        
        // Load user's custom emoji library
        const customEmojis = await this.loadUserEmojiLibrary(userPreferences.userId);
        
        // Initialize spatial emoji placement system
        await this.spatialTracker.calibrateSpace(arSession);
        
        return {
            session: arSession,
            available_emojis: customEmojis,
            interaction_modes: this.getAvailableInteractions(arSession)
        };
    }
    
    async placeEmojiInSpace(emoji, spatialPosition, duration = 5000) {
        """Place custom emoji in 3D space with realistic physics"""
        const emojiObject = await this.emojiRenderer.create3DEmoji({
            design: emoji.design,
            size: this.calculateOptimalSize(spatialPosition),
            physics: {
                gravity: true,
                collision: true,
                interaction: true
            },
            animation: emoji.animations || []
        });
        
        // Apply spatial anchoring
        const anchor = await this.spatialTracker.createAnchor(spatialPosition);
        emojiObject.attachToAnchor(anchor);
        
        // Add interactive behaviors
        emojiObject.onInteraction = (interactionType, data) => {
            this.handleEmojiInteraction(emoji, interactionType, data);
        };
        
        // Schedule automatic removal
        setTimeout(() => {
            this.removeEmojiFromSpace(emojiObject);
        }, duration);
        
        return emojiObject;
    }
    
    async recognizeEmojiGesture(gestureData) {
        """Recognize hand gestures for emoji creation and manipulation"""
        const recognitionResult = await this.gestureRecognizer.analyze(gestureData);
        
        if (recognitionResult.confidence > 0.8) {
            switch (recognitionResult.gesture_type) {
                case 'create_emoji':
                    return await this.handleEmojiCreationGesture(recognitionResult);
                    
                case 'modify_emoji':
                    return await this.handleEmojiModificationGesture(recognitionResult);
                    
                case 'send_emoji':
                    return await this.handleEmojiSendGesture(recognitionResult);
                    
                default:
                    return { action: 'unknown_gesture', confidence: recognitionResult.confidence };
            }
        }
        
        return { action: 'no_gesture_recognized' };
    }
}

# Conceptual BCI emoji control system
class BCIEmojiInterface:
    def __init__(self, bci_device, neural_decoder):
        self.bci_device = bci_device
        self.neural_decoder = neural_decoder
        self.emotion_classifier = NeuralEmotionClassifier()
        self.intent_decoder = IntentDecodingEngine()
        self.emoji_synthesizer = NeuralEmojiSynthesizer()
        
    async def initialize_neural_connection(self, user_calibration_data):
        """Initialize BCI connection with user-specific neural patterns"""
        await self.bci_device.connect()
        
        # Calibrate neural patterns for emoji-related thoughts
        calibration_results = await self.calibrate_emoji_patterns(
            user_calibration_data
        )
        
        # Train personal neural decoder
        self.neural_decoder.personalize(calibration_results)
        
        return {
            'connection_status': 'established',
            'calibration_accuracy': calibration_results.accuracy,
            'supported_commands': self.get_supported_neural_commands()
        }
    
    async def decode_emoji_intent(self, neural_signal_window):
        """Decode user's emoji intention from neural signals"""
        # Extract relevant neural features
        neural_features = await self.extract_neural_features(neural_signal_window)
        
        # Decode emotional state
        emotional_state = await self.emotion_classifier.classify(neural_features)
        
        # Decode communication intent
        communication_intent = await self.intent_decoder.decode(neural_features)
        
        # Synthesize appropriate emoji
        emoji_parameters = {
            'emotion': emotional_state,
            'intent': communication_intent,
            'intensity': neural_features.emotional_intensity,
            'context': await self.get_current_context()
        }
        
        generated_emoji = await self.emoji_synthesizer.generate(emoji_parameters)
        
        return {
            'emoji': generated_emoji,
            'confidence': min(emotional_state.confidence, communication_intent.confidence),
            'neural_state': {
                'emotion': emotional_state,
                'intent': communication_intent,
                'arousal_level': neural_features.arousal
            }
        }
    
    async def enable_thought_to_emoji_translation(self, continuous_mode=True):
        """Enable real-time translation of thoughts to emoji suggestions"""
        if continuous_mode:
            # Set up continuous neural monitoring
            self.bci_device.start_continuous_monitoring(
                callback=self.process_neural_stream,
                sample_rate=250  # Hz
            )
        
        # Real-time processing pipeline
        async def process_neural_stream(neural_data):
            # Buffer neural data for analysis
            self.neural_buffer.append(neural_data)
            
            # Process when sufficient data is available
            if len(self.neural_buffer) >= self.minimum_analysis_window:
                emoji_suggestions = await self.decode_emoji_intent(self.neural_buffer)
                
                # Send suggestions to communication interface
                await self.send_emoji_suggestions(emoji_suggestions)
                
                # Clear buffer for next analysis window
                self.neural_buffer.clear()

// Personal visual language evolution tracker
class PersonalEmojiLanguageEvolution {
    constructor(userId) {
        this.userId = userId;
        this.languageModel = new PersonalLanguageModel();
        this.semanticAnalyzer = new SemanticEvolutionAnalyzer();
        this.culturalInfluenceTracker = new CulturalInfluenceTracker();
    }
    
    trackLanguageEvolution(timespan = '1_year') {
        const evolutionMetrics = {
            vocabulary_growth: this.measureVocabularyGrowth(timespan),
            semantic_shift: this.analyzeSemanticShift(timespan),
            cultural_adaptation: this.trackCulturalAdaptation(timespan),
            personal_innovation: this.measureInnovation(timespan)
        };
        
        return {
            user_id: this.userId,
            evolution_summary: evolutionMetrics,
            predicted_trends: this.predictPersonalTrends(evolutionMetrics),
            language_uniqueness_score: this.calculateUniqueness(evolutionMetrics)
        };
    }
    
    measureVocabularyGrowth(timespan) {
        // Track expansion of personal emoji vocabulary
        const historical_usage = this.getHistoricalUsage(timespan);
        
        return {
            new_emojis_created: historical_usage.filter(e => e.user_created).length,
            new_emojis_adopted: historical_usage.filter(e => e.adopted_from_others).length,
            usage_frequency_evolution: this.analyzeFrequencyChanges(historical_usage),
            semantic_category_expansion: this.measureCategoryGrowth(historical_usage)
        };
    }
    
    predictPersonalTrends(evolutionMetrics) {
        // Predict future direction of personal emoji language
        const trends = this.languageModel.predictTrends(evolutionMetrics);
        
        return {
            emerging_themes: trends.themes,
            likely_adoptions: trends.adoptions,
            innovation_probability: trends.innovation_score,
            influence_potential: trends.influence_on_others
        };
    }
}

# Cross-cultural emoji translation system
class CulturalEmojiTranslator:
    def __init__(self):
        self.cultural_databases = self.load_cultural_databases()
        self.translation_models = self.load_translation_models()
        self.context_analyzer = CrossCulturalContextAnalyzer()
        self.sensitivity_checker = CulturalSensitivityChecker()
        
    async def translate_emoji_message(self, emoji_message, source_culture, target_culture):
        """Translate emoji message between cultures"""
        
        # Analyze cultural context of source message
        source_analysis = await self.analyze_cultural_context(
            emoji_message, source_culture
        )
        
        # Check for potential cultural sensitivity issues
        sensitivity_issues = await self.sensitivity_checker.check(
            emoji_message, source_culture, target_culture
        )
        
        # Perform cultural translation
        if sensitivity_issues:
            translated_message = await self.handle_sensitive_translation(
                emoji_message, source_analysis, target_culture, sensitivity_issues
            )
        else:
            translated_message = await self.direct_translation(
                emoji_message, source_analysis, target_culture
            )
        
        # Provide cultural explanation when needed
        cultural_explanation = await self.generate_cultural_explanation(
            emoji_message, translated_message, source_culture, target_culture
        )
        
        return {
            'original_message': emoji_message,
            'translated_message': translated_message,
            'cultural_notes': cultural_explanation,
            'confidence_score': self.calculate_translation_confidence(
                source_analysis, translated_message
            ),
            'alternative_translations': await self.generate_alternatives(
                emoji_message, source_analysis, target_culture
            )
        }
    
    async def handle_sensitive_translation(self, message, analysis, target_culture, issues):
        """Handle culturally sensitive emoji translations"""
        
        safe_alternatives = []
        
        for issue in issues:
            # Find culturally appropriate alternatives
            alternatives = await self.find_safe_alternatives(
                issue.problematic_emoji,
                target_culture,
                analysis.intended_meaning
            )
            
            safe_alternatives.extend(alternatives)
        
        # Reconstruct message with safe alternatives
        safe_message = await self.reconstruct_with_alternatives(
            message, safe_alternatives
        )
        
        return {
            'translated_message': safe_message,
            'cultural_adaptations': [
                {
                    'original_emoji': issue.problematic_emoji,
                    'replacement': alternative,
                    'reason': issue.sensitivity_reason
                }
                for issue, alternative in zip(issues, safe_alternatives)
            ]
        }

// Ethical AI framework for emoji generation
class EthicalEmojiAI {
    constructor() {
        this.ethicsGuidelines = new EthicsFramework({
            privacy_protection: true,
            bias_mitigation: true,
            cultural_respect: true,
            user_autonomy: true,
            transparency: true
        });
        
        this.biasDetector = new BiasDetectionSystem();
        this.privacyProtector = new PrivacyProtectionEngine();
        this.transparencyEngine = new AITransparencyEngine();
    }
    
    async ethicalReview(emojiGenerationRequest, userProfile) {
        const ethicalAssessment = {
            privacy_score: await this.assessPrivacyImpact(emojiGenerationRequest, userProfile),
            bias_score: await this.detectPotentialBias(emojiGenerationRequest),
            cultural_sensitivity_score: await this.assessCulturalSensitivity(emojiGenerationRequest),
            user_autonomy_score: await this.evaluateUserAutonomy(emojiGenerationRequest),
            transparency_score: await this.evaluateTransparency(emojiGenerationRequest)
        };
        
        const overallEthicalScore = this.calculateOverallScore(ethicalAssessment);
        
        return {
            ethical_clearance: overallEthicalScore > 0.7,
            assessment_details: ethicalAssessment,
            recommended_modifications: await this.generateEthicalRecommendations(ethicalAssessment),
            user_disclosure: await this.generateUserDisclosure(ethicalAssessment)
        };
    }
    
    async implementEthicalSafeguards(emojiSystem) {
        // Implement comprehensive ethical safeguards
        const safeguards = {
            data_minimization: await this.implementDataMinimization(emojiSystem),
            bias_mitigation: await this.implementBiasMitigation(emojiSystem),
            user_control: await this.implementUserControl(emojiSystem),
            transparency: await this.implementTransparency(emojiSystem),
            cultural_respect: await this.implementCulturalRespect(emojiSystem)
        };
        
        return safeguards;
    }
}