Smart Text Completion

class SmartCompletionSystem:
    """
    Gmail Compose-style smart text completion system
    Real-time, context-aware suggestions with sub-100ms latency
    """
    
    def __init__(self, config):
        self.config = config
        self.models = self.load_completion_models()
        self.context_analyzer = self.initialize_context_analyzer()
        self.suggestion_cache = self.setup_caching()
        self.user_preferences = self.load_user_preferences()
        
    def load_completion_models(self):
        """Load different models for different completion tasks"""
        models = {
            'short_phrase': self.load_lightweight_model('gpt2-medium'),
            'sentence': self.load_distilled_model('t5-small'),
            'paragraph': self.load_specialized_model('completion-gpt'),
            'email_specific': self.load_domain_model('email-completion')
        }
        
        # Model selection strategy
        self.model_selector = ModelSelector(models)
        return models
    
    def initialize_context_analyzer(self):
        """Analyze context for better completions"""
        class ContextAnalyzer:
            def __init__(self):
                self.intent_classifier = self.load_intent_model()
                self.topic_extractor = self.load_topic_model()
                self.formality_detector = self.load_formality_model()
                
            def analyze_context(self, text, metadata):
                """Extract context features for completion"""
                context = {
                    'intent': self.classify_intent(text),
                    'topic': self.extract_topic(text),
                    'formality': self.detect_formality(text),
                    'position': self.analyze_cursor_position(text, metadata['cursor']),
                    'email_type': self.classify_email_type(metadata),
                    'recipient_context': self.analyze_recipients(metadata.get('recipients', []))
                }
                
                return context
            
            def classify_intent(self, text):
                """Classify writing intent"""
                # Request, inform, persuade, thank, apologize, etc.
                intent_features = self.extract_intent_features(text)
                return self.intent_classifier.predict(intent_features)
            
            def extract_topic(self, text):
                """Extract main topics"""
                # Business, personal, technical, sales, support, etc.
                embeddings = self.get_text_embeddings(text)
                topics = self.topic_extractor.transform(embeddings)
                return topics
            
            def detect_formality(self, text):
                """Detect formality level"""
                # Formal, semi-formal, casual
                features = {
                    'contraction_count': len(re.findall(r"w+'[a-z]", text)),
                    'formal_words': self.count_formal_vocabulary(text),
                    'sentence_length': np.mean([len(s.split()) for s in text.split('.')]),
                    'greeting_type': self.classify_greeting(text)
                }
                
                return self.formality_detector.predict([list(features.values())])[0]
            
            def analyze_cursor_position(self, text, cursor_pos):
                """Analyze where user is typing"""
                return {
                    'sentence_start': self.is_sentence_start(text, cursor_pos),
                    'paragraph_start': self.is_paragraph_start(text, cursor_pos),
                    'mid_sentence': self.is_mid_sentence(text, cursor_pos),
                    'after_punctuation': self.is_after_punctuation(text, cursor_pos)
                }
        
        return ContextAnalyzer()
    
    async def generate_suggestions(self, text, cursor_position, metadata):
        """Main completion generation pipeline"""
        # Step 1: Analyze context
        context = self.context_analyzer.analyze_context(text, {
            'cursor': cursor_position,
            'recipients': metadata.get('recipients', []),
            'subject': metadata.get('subject', ''),
            'thread_context': metadata.get('thread_context', '')
        })
        
        # Step 2: Select appropriate model
        model_type = self.select_completion_model(context, text, cursor_position)
        model = self.models[model_type]
        
        # Step 3: Prepare input with context
        model_input = self.prepare_model_input(text, context, cursor_position)
        
        # Step 4: Generate multiple candidates
        candidates = await self.generate_candidates(model, model_input, context)
        
        # Step 5: Rank and filter suggestions
        ranked_suggestions = self.rank_suggestions(candidates, context, text)
        
        # Step 6: Post-process for UI
        final_suggestions = self.post_process_suggestions(ranked_suggestions)
        
        return final_suggestions
    
    def select_completion_model(self, context, text, cursor_pos):
        """Select best model based on context"""
        # Estimate completion length needed
        remaining_sentence = self.estimate_remaining_sentence(text, cursor_pos)
        
        if remaining_sentence < 5:  # Short phrase
            return 'short_phrase'
        elif remaining_sentence < 20:  # Full sentence
            return 'sentence'
        elif context['position']['paragraph_start']:  # New paragraph
            return 'paragraph'
        else:  # Email-specific completion
            return 'email_specific'
    
    async def generate_candidates(self, model, model_input, context, num_candidates=10):
        """Generate multiple completion candidates"""
        candidates = []
        
        # Primary generation
        primary_outputs = await model.generate(
            model_input,
            num_return_sequences=num_candidates,
            temperature=0.7,
            max_length=50,
            do_sample=True,
            top_p=0.9
        )
        
        for output in primary_outputs:
            candidate = {
                'text': output.generated_text,
                'confidence': output.sequence_score,
                'model_type': 'primary',
                'tokens': output.tokens
            }
            candidates.append(candidate)
        
        # Template-based suggestions for common patterns
        template_suggestions = self.generate_template_suggestions(context)
        candidates.extend(template_suggestions)
        
        # Personal writing style adaptation
        if self.user_preferences.get('personalization_enabled'):
            personal_suggestions = await self.generate_personalized_suggestions(
                model_input, context
            )
            candidates.extend(personal_suggestions)
        
        return candidates
    
    def rank_suggestions(self, candidates, context, original_text):
        """Rank suggestions by relevance and quality"""
        scored_candidates = []
        
        for candidate in candidates:
            score = self.calculate_suggestion_score(candidate, context, original_text)
            scored_candidates.append((candidate, score))
        
        # Sort by score descending
        scored_candidates.sort(key=lambda x: x[1], reverse=True)
        
        return [candidate for candidate, score in scored_candidates[:5]]
    
    def calculate_suggestion_score(self, candidate, context, original_text):
        """Multi-factor scoring for suggestions"""
        score = 0.0
        
        # Model confidence
        score += candidate['confidence'] * 0.3
        
        # Context relevance
        relevance = self.calculate_context_relevance(candidate['text'], context)
        score += relevance * 0.25
        
        # Fluency and grammar
        fluency = self.assess_fluency(candidate['text'])
        score += fluency * 0.2
        
        # Personalization match
        if self.user_preferences.get('personalization_enabled'):
            personal_match = self.calculate_personal_match(candidate['text'])
            score += personal_match * 0.15
        
        # Diversity bonus (avoid repetitive suggestions)
        diversity = self.calculate_diversity_bonus(candidate['text'], original_text)
        score += diversity * 0.1
        
        return score