utils/nlp_query_enhancer.py

"""

NLP Query Enhancer

Advanced query processing using spaCy and NLTK for better search results

"""

import spacy
import nltk
from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sentence_transformers import SentenceTransformer
import numpy as np
from typing import List, Dict, Set, Tuple, Any
import re
import logging
from collections import defaultdict, Counter
import asyncio
import aiohttp

logger = logging.getLogger(__name__)

class NLPQueryEnhancer:
    """Enhanced query processing using advanced NLP techniques"""
    
    def __init__(self):
        """Initialize NLP models and resources"""
        self.nlp = None
        self.sentence_model = None
        self.lemmatizer = None
        self.stop_words = None
        self._initialized = False
        
        # Industry-specific term mappings
        self.industry_ontology = {
            'technology': {
                'ai': ['artificial intelligence', 'machine learning', 'deep learning', 'neural networks'],
                'app': ['application', 'software', 'mobile app', 'web app', 'platform'],
                'api': ['application programming interface', 'web service', 'endpoint'],
                'saas': ['software as a service', 'cloud software', 'subscription software'],
                'iot': ['internet of things', 'connected devices', 'smart devices'],
                'blockchain': ['distributed ledger', 'cryptocurrency', 'smart contracts']
            },
            'business': {
                'startup': ['new business', 'entrepreneur', 'venture', 'company'],
                'revenue': ['income', 'earnings', 'sales', 'profit'],
                'customer': ['client', 'user', 'consumer', 'buyer'],
                'market': ['industry', 'sector', 'segment', 'niche'],
                'competition': ['competitor', 'rival', 'alternative', 'substitute']
            },
            'health': {
                'fitness': ['exercise', 'workout', 'training', 'physical activity'],
                'nutrition': ['diet', 'food', 'eating', 'meal planning'],
                'wellness': ['health', 'wellbeing', 'lifestyle', 'self-care'],
                'medical': ['healthcare', 'clinical', 'treatment', 'diagnosis']
            },
            'finance': {
                'fintech': ['financial technology', 'digital banking', 'payment systems'],
                'investment': ['portfolio', 'trading', 'stocks', 'assets'],
                'banking': ['financial services', 'credit', 'loans', 'deposits'],
                'insurance': ['coverage', 'policy', 'claims', 'risk management']
            }
        }
    
    async def initialize(self):
        """Initialize NLP models asynchronously"""
        if self._initialized:
            return
        
        try:
            logger.info("Initializing NLP models...")
            
            # Download required NLTK data
            await self._download_nltk_data()
            
            # Initialize spaCy model
            try:
                self.nlp = spacy.load("en_core_web_sm")
            except OSError:
                logger.warning("spaCy model 'en_core_web_sm' not found. Using basic tokenization.")
                self.nlp = None
            
            # Initialize sentence transformer
            try:
                self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
            except Exception as e:
                logger.warning(f"Could not load sentence transformer: {e}")
                self.sentence_model = None
            
            # Initialize NLTK components
            self.lemmatizer = WordNetLemmatizer()
            self.stop_words = set(stopwords.words('english'))
            
            self._initialized = True
            logger.info("NLP models initialized successfully")
            
        except Exception as e:
            logger.error(f"Error initializing NLP models: {e}")
            # Set basic fallbacks
            self.lemmatizer = WordNetLemmatizer() if 'WordNetLemmatizer' in locals() else None
            self.stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'}
    
    async def _download_nltk_data(self):
        """Download required NLTK data"""
        required_data = ['punkt', 'stopwords', 'wordnet', 'averaged_perceptron_tagger', 'omw-1.4']
        
        for data in required_data:
            try:
                nltk.data.find(f'tokenizers/{data}')
            except LookupError:
                try:
                    nltk.download(data, quiet=True)
                except Exception as e:
                    logger.warning(f"Could not download NLTK data '{data}': {e}")
    
    async def enhance_query(self, feature_description: str, target_market: str = "") -> Dict[str, Any]:
        """

        Enhance a feature description into multiple optimized search queries

        

        Args:

            feature_description: Original feature description

            target_market: Target market description

            

        Returns:

            Enhanced query data with multiple search strategies

        """
        await self.initialize()
        
        try:
            # Extract key concepts using NLP
            concepts = await self._extract_key_concepts(feature_description)
            
            # Generate semantic variations
            semantic_queries = await self._generate_semantic_queries(feature_description)
            
            # Expand with synonyms and related terms
            expanded_queries = await self._expand_with_synonyms(concepts)
            
            # Add industry-specific terms
            industry_queries = await self._add_industry_terms(concepts, target_market)
            
            # Generate market-specific queries
            market_queries = await self._generate_market_queries(feature_description, target_market)
            
            # Combine and rank queries
            all_queries = {
                'core_concepts': concepts['core_terms'],
                'semantic_variations': semantic_queries,
                'synonym_expansions': expanded_queries,
                'industry_specific': industry_queries,
                'market_focused': market_queries,
                'combined_queries': self._combine_queries(concepts, target_market)
            }
            
            return {
                'original_description': feature_description,
                'target_market': target_market,
                'extracted_concepts': concepts,
                'enhanced_queries': all_queries,
                'query_metadata': {
                    'total_queries': sum(len(v) if isinstance(v, list) else 1 for v in all_queries.values()),
                    'confidence_score': self._calculate_confidence(concepts),
                    'processing_method': 'advanced_nlp' if self.nlp else 'basic_processing'
                }
            }
            
        except Exception as e:
            logger.error(f"Error enhancing query: {e}")
            # Fallback to basic processing
            return await self._basic_query_enhancement(feature_description, target_market)
    
    async def _extract_key_concepts(self, text: str) -> Dict[str, Any]:
        """Extract key concepts using spaCy NLP"""
        concepts = {
            'core_terms': [],
            'entities': [],
            'technologies': [],
            'business_terms': [],
            'action_verbs': [],
            'descriptors': []
        }
        
        if self.nlp:
            # Use spaCy for advanced processing
            doc = self.nlp(text)
            
            # Extract named entities
            for ent in doc.ents:
                if ent.label_ in ['ORG', 'PRODUCT', 'TECHNOLOGY']:
                    concepts['entities'].append(ent.text.lower())
            
            # Extract key terms by POS tags
            for token in doc:
                if token.is_stop or token.is_punct or len(token.text) < 3:
                    continue
                
                lemma = token.lemma_.lower()
                
                if token.pos_ == 'NOUN' and token.dep_ in ['nsubj', 'dobj', 'pobj']:
                    concepts['core_terms'].append(lemma)
                elif token.pos_ == 'VERB' and token.tag_ not in ['MD', 'VBZ']:  # Not auxiliary verbs
                    concepts['action_verbs'].append(lemma)
                elif token.pos_ == 'ADJ':
                    concepts['descriptors'].append(lemma)
            
            # Extract noun phrases
            for chunk in doc.noun_chunks:
                if len(chunk.text.split()) <= 3:  # Keep phrases short
                    concepts['core_terms'].append(chunk.text.lower())
        
        else:
            # Fallback to basic processing
            words = word_tokenize(text.lower())
            if self.lemmatizer:
                words = [self.lemmatizer.lemmatize(word) for word in words if word.isalpha()]
            
            # Filter out stop words
            words = [word for word in words if word not in self.stop_words and len(word) > 2]
            concepts['core_terms'] = words[:10]  # Limit to top 10
        
        # Categorize terms
        concepts = self._categorize_terms(concepts)
        
        # Remove duplicates and sort by importance
        for key in concepts:
            if isinstance(concepts[key], list):
                concepts[key] = list(dict.fromkeys(concepts[key]))[:5]  # Top 5 per category
        
        return concepts
    
    def _categorize_terms(self, concepts: Dict[str, Any]) -> Dict[str, Any]:
        """Categorize terms into technology, business, etc."""
        tech_keywords = {'ai', 'app', 'software', 'platform', 'api', 'system', 'tool', 'technology', 'digital', 'online', 'mobile', 'web'}
        business_keywords = {'market', 'customer', 'business', 'service', 'product', 'solution', 'company', 'industry', 'revenue', 'profit'}
        
        all_terms = concepts['core_terms'] + concepts['entities']
        
        for term in all_terms:
            term_lower = term.lower()
            if any(tech in term_lower for tech in tech_keywords):
                concepts['technologies'].append(term)
            elif any(biz in term_lower for biz in business_keywords):
                concepts['business_terms'].append(term)
        
        return concepts
    
    async def _generate_semantic_queries(self, text: str) -> List[str]:
        """Generate semantically similar queries using sentence transformers"""
        if not self.sentence_model:
            return []
        
        try:
            # Generate variations by paraphrasing key concepts
            base_embedding = self.sentence_model.encode([text])
            
            # Create variations by replacing key terms with synonyms
            variations = []
            words = text.split()
            
            for i, word in enumerate(words):
                if len(word) > 4 and word.lower() not in self.stop_words:
                    # Try to find synonyms
                    synonyms = self._get_wordnet_synonyms(word)
                    for synonym in synonyms[:2]:  # Limit to 2 synonyms per word
                        new_text = words.copy()
                        new_text[i] = synonym
                        variations.append(' '.join(new_text))
            
            return variations[:5]  # Return top 5 variations
            
        except Exception as e:
            logger.warning(f"Error generating semantic queries: {e}")
            return []
    
    async def _expand_with_synonyms(self, concepts: Dict[str, Any]) -> List[str]:
        """Expand core terms with synonyms using WordNet"""
        expanded = []
        
        for term in concepts['core_terms'][:5]:  # Limit to top 5 terms
            synonyms = self._get_wordnet_synonyms(term)
            if synonyms:
                # Create queries with synonyms
                expanded.extend(synonyms[:3])  # Top 3 synonyms per term
        
        return list(set(expanded))  # Remove duplicates
    
    def _get_wordnet_synonyms(self, word: str) -> List[str]:
        """Get synonyms from WordNet"""
        synonyms = set()
        
        try:
            for syn in wordnet.synsets(word):
                for lemma in syn.lemmas():
                    synonym = lemma.name().replace('_', ' ')
                    if synonym.lower() != word.lower() and len(synonym) > 2:
                        synonyms.add(synonym)
        except Exception as e:
            logger.debug(f"Error getting synonyms for '{word}': {e}")
        
        return list(synonyms)[:5]  # Limit to 5 synonyms
    
    async def _add_industry_terms(self, concepts: Dict[str, Any], target_market: str) -> List[str]:
        """Add industry-specific terminology"""
        industry_queries = []
        
        # Detect industry from concepts and target market
        detected_industries = self._detect_industries(concepts, target_market)
        
        for industry in detected_industries:
            if industry in self.industry_ontology:
                ontology = self.industry_ontology[industry]
                
                # Match concepts to industry terms
                for concept in concepts['core_terms'][:3]:
                    for term, expansions in ontology.items():
                        if concept.lower() in term or term in concept.lower():
                            industry_queries.extend(expansions[:2])  # Add 2 expansions
        
        return list(set(industry_queries))[:8]  # Limit to 8 industry terms
    
    def _detect_industries(self, concepts: Dict[str, Any], target_market: str) -> List[str]:
        """Detect relevant industries from concepts and target market"""
        industries = []
        all_text = ' '.join(concepts['core_terms'] + [target_market]).lower()
        
        # Simple keyword matching for industry detection
        industry_keywords = {
            'technology': ['app', 'software', 'ai', 'tech', 'digital', 'platform', 'api'],
            'health': ['fitness', 'health', 'medical', 'wellness', 'nutrition', 'exercise'],
            'business': ['business', 'startup', 'company', 'market', 'customer'],
            'finance': ['finance', 'money', 'payment', 'banking', 'investment']
        }
        
        for industry, keywords in industry_keywords.items():
            if any(keyword in all_text for keyword in keywords):
                industries.append(industry)
        
        return industries[:2]  # Limit to 2 industries
    
    async def _generate_market_queries(self, feature_description: str, target_market: str) -> List[str]:
        """Generate market-focused search queries"""
        if not target_market:
            return []
        
        market_queries = []
        
        # Extract key terms from feature description
        feature_words = [word for word in feature_description.split() 
                        if len(word) > 3 and word.lower() not in self.stop_words][:3]
        
        # Extract market segments
        market_words = [word for word in target_market.split() 
                       if len(word) > 3 and word.lower() not in self.stop_words][:3]
        
        # Combine feature + market
        for feature_word in feature_words:
            for market_word in market_words:
                market_queries.append(f"{feature_word} {market_word}")
                market_queries.append(f"{market_word} {feature_word}")
        
        # Add market-specific patterns
        market_patterns = [
            f"{target_market} solutions",
            f"{target_market} tools",
            f"{target_market} software",
            f"{target_market} apps",
            f"{target_market} technology"
        ]
        
        market_queries.extend(market_patterns)
        
        return list(set(market_queries))[:6]  # Limit to 6 market queries
    
    def _combine_queries(self, concepts: Dict[str, Any], target_market: str) -> List[str]:
        """Combine different concepts into comprehensive, contextually relevant queries"""
        combined = []
        
        core_terms = concepts['core_terms'][:3]
        technologies = concepts['technologies'][:2]
        business_terms = concepts['business_terms'][:2]
        
        # Create contextual phrase combinations (preserve meaning)
        if len(core_terms) >= 2:
            # Keep related terms together
            main_concept = ' '.join(core_terms[:2])
            combined.append(main_concept)
            
            # Add third term only if it's contextually related
            if len(core_terms) >= 3:
                extended_concept = ' '.join(core_terms[:3])
                # Only add if it makes semantic sense
                if len(extended_concept.split()) <= 4:  # Avoid overly long phrases
                    combined.append(extended_concept)
        
        # Combine technology + business terms meaningfully
        for tech in technologies:
            for biz in business_terms:
                # Create meaningful combinations
                tech_biz_combo = f"{tech} {biz}"
                # Ensure the combination makes sense (not just random word pairs)
                if self._is_meaningful_combination(tech, biz):
                    combined.append(tech_biz_combo)
        
        # Add contextual market combinations
        if target_market:
            market_words = [word for word in target_market.split() if len(word) > 3][:2]
            for market_word in market_words:
                for term in core_terms[:2]:
                    # Create market-specific queries that maintain context
                    market_combo = f"{term} {market_word}"
                    if self._is_meaningful_combination(term, market_word):
                        combined.append(market_combo)
        
        # Remove duplicates and filter for relevance
        unique_combined = list(dict.fromkeys(combined))  # Preserve order while removing duplicates
        
        # Filter out combinations that are too generic or meaningless
        filtered_combined = [combo for combo in unique_combined if self._is_contextually_relevant(combo)]
        
        return filtered_combined[:5]  # Limit to 5 most relevant combined queries
    
    def _is_meaningful_combination(self, term1: str, term2: str) -> bool:
        """Check if two terms create a meaningful combination"""
        # Avoid combining very similar terms
        if term1.lower() == term2.lower():
            return False
        
        # Avoid combining terms that are substrings of each other
        if term1.lower() in term2.lower() or term2.lower() in term1.lower():
            return False
        
        # Check for semantic compatibility
        tech_terms = {'ai', 'ml', 'algorithm', 'neural', 'deep', 'machine', 'learning', 'data', 'model', 'system', 'network', 'automation', 'software', 'digital', 'technology', 'computer', 'intelligence'}
        business_terms = {'business', 'market', 'customer', 'service', 'product', 'solution', 'company', 'industry', 'enterprise', 'commercial', 'professional', 'management', 'strategy'}
        
        term1_is_tech = any(tech in term1.lower() for tech in tech_terms)
        term2_is_tech = any(tech in term2.lower() for tech in tech_terms)
        term1_is_business = any(biz in term1.lower() for biz in business_terms)
        term2_is_business = any(biz in term2.lower() for biz in business_terms)
        
        # Good combinations: tech + business, or related terms
        if (term1_is_tech and term2_is_business) or (term1_is_business and term2_is_tech):
            return True
        
        # Both are tech terms or both are business terms - can be good if not too similar
        if (term1_is_tech and term2_is_tech) or (term1_is_business and term2_is_business):
            return len(set(term1.lower().split()) & set(term2.lower().split())) == 0  # No overlapping words
        
        return True  # Default to allowing the combination
    
    def _is_contextually_relevant(self, query: str) -> bool:
        """Check if a query maintains contextual relevance"""
        words = query.lower().split()
        
        # Filter out queries that are too short or too long
        if len(words) < 1 or len(words) > 5:
            return False
        
        # Filter out queries with only stop words
        stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'}
        meaningful_words = [word for word in words if word not in stop_words and len(word) > 2]
        
        if len(meaningful_words) == 0:
            return False
        
        # Ensure at least one word is substantial (length > 3)
        if not any(len(word) > 3 for word in meaningful_words):
            return False
        
        return True
    
    def _calculate_confidence(self, concepts: Dict[str, Any]) -> float:
        """Calculate confidence score for the extraction"""
        total_concepts = sum(len(v) if isinstance(v, list) else 0 for v in concepts.values())
        
        # Base confidence on number of extracted concepts
        if total_concepts >= 15:
            return 0.9
        elif total_concepts >= 10:
            return 0.8
        elif total_concepts >= 5:
            return 0.7
        else:
            return 0.6
    
    async def _basic_query_enhancement(self, feature_description: str, target_market: str) -> Dict[str, Any]:
        """Fallback basic query enhancement when NLP models fail"""
        # Simple word extraction
        words = re.findall(r'\b\w{3,}\b', feature_description.lower())
        words = [word for word in words if word not in self.stop_words][:5]
        
        return {
            'original_description': feature_description,
            'target_market': target_market,
            'extracted_concepts': {'core_terms': words},
            'enhanced_queries': {
                'core_concepts': words,
                'semantic_variations': [],
                'synonym_expansions': [],
                'industry_specific': [],
                'market_focused': [target_market] if target_market else [],
                'combined_queries': [' '.join(words[:2])] if len(words) >= 2 else words
            },
            'query_metadata': {
                'total_queries': len(words) + (1 if target_market else 0),
                'confidence_score': 0.5,
                'processing_method': 'basic_fallback'
            }
        }
    
    def get_optimized_queries_for_platform(self, enhanced_data: Dict[str, Any], platform: str) -> List[str]:
        """

        Get platform-optimized queries from enhanced data

        

        Args:

            enhanced_data: Result from enhance_query()

            platform: Target platform ('news', 'reddit', etc.)

            

        Returns:

            List of optimized queries for the specific platform

        """
        queries = enhanced_data.get('enhanced_queries', {})
        
        platform_strategies = {
            'news': ['core_concepts', 'industry_specific', 'combined_queries'],
            'reddit': ['semantic_variations', 'market_focused', 'core_concepts'],
            'linkedin': ['industry_specific', 'business_terms', 'market_focused']
        }
        
        strategy = platform_strategies.get(platform, ['core_concepts', 'combined_queries'])
        optimized_queries = []
        
        for strategy_key in strategy:
            if strategy_key in queries:
                query_list = queries[strategy_key]
                if isinstance(query_list, list):
                    optimized_queries.extend(query_list)
                else:
                    optimized_queries.append(str(query_list))
        
        # Remove duplicates while preserving order
        seen = set()
        unique_queries = []
        for query in optimized_queries:
            if query not in seen:
                seen.add(query)
                unique_queries.append(query)
        
        return unique_queries[:8]  # Limit to 8 queries per platform


# Example usage and testing
async def test_nlp_enhancer():
    """Test the NLP query enhancer"""
    enhancer = NLPQueryEnhancer()
    
    # Test query enhancement
    feature = "AI-powered voice ordering system for restaurants"
    market = "small to medium restaurants, food service industry"
    
    print("Testing NLP Query Enhancement...")
    enhanced = await enhancer.enhance_query(feature, market)
    
    print(f"Original: {enhanced['original_description']}")
    print(f"Market: {enhanced['target_market']}")
    print(f"Core concepts: {enhanced['extracted_concepts']['core_terms']}")
    print(f"Confidence: {enhanced['query_metadata']['confidence_score']}")
    
    # Test platform-specific queries
    for platform in ['news', 'reddit']:
        queries = enhancer.get_optimized_queries_for_platform(enhanced, platform)
        print(f"{platform.title()} queries: {queries[:3]}")
    
    return enhanced

if __name__ == "__main__":
    asyncio.run(test_nlp_enhancer())