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app/intents.py
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# app/intents.py
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"""
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🎯 Penny's Intent Classification System
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Rule-based intent classifier designed for civic engagement queries.
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CURRENT: Simple keyword matching (fast, predictable, debuggable)
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FUTURE: Will upgrade to ML/embedding-based classification (Gemma/LayoutLM)
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This approach allows Penny to understand resident needs and route them
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to the right civic systems — weather, resources, events, translation, etc.
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"""
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import logging
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from typing import Dict, List, Optional
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from dataclasses import dataclass, field
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from enum import Enum
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# --- LOGGING SETUP (Azure-friendly) ---
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logger = logging.getLogger(__name__)
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# --- INTENT CATEGORIES (Enumerated for type safety) ---
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class IntentType(str, Enum):
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"""
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Penny's supported intent categories.
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Each maps to a specific civic assistance pathway.
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"""
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WEATHER = "weather"
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GREETING = "greeting"
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LOCAL_RESOURCES = "local_resources"
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EVENTS = "events"
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TRANSLATION = "translation"
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SENTIMENT_ANALYSIS = "sentiment_analysis"
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BIAS_DETECTION = "bias_detection"
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DOCUMENT_PROCESSING = "document_processing"
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HELP = "help"
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EMERGENCY = "emergency" # Critical safety routing
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UNKNOWN = "unknown"
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@dataclass
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class IntentMatch:
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"""
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Structured intent classification result.
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Includes confidence score and matched keywords for debugging.
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"""
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intent: IntentType
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confidence: float # 0.0 - 1.0
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matched_keywords: List[str]
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is_compound: bool = False # True if query spans multiple intents
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secondary_intents: List[IntentType] = field(default_factory=list)
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def to_dict(self) -> Dict:
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"""Convert to dictionary for logging and API responses."""
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return {
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"intent": self.intent.value,
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"confidence": self.confidence,
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"matched_keywords": self.matched_keywords,
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"is_compound": self.is_compound,
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"secondary_intents": [intent.value for intent in self.secondary_intents]
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}
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# --- INTENT KEYWORD PATTERNS (Organized by priority) ---
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class IntentPatterns:
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"""
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Penny's keyword patterns for intent matching.
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Organized by priority — critical intents checked first.
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"""
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# 🚨 PRIORITY 1: EMERGENCY & SAFETY (Always check first)
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EMERGENCY = [
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"911", "emergency", "urgent", "crisis", "danger", "help me",
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"suicide", "overdose", "assault", "abuse", "threatening",
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"hurt myself", "hurt someone", "life threatening"
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]
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# 🌍 PRIORITY 2: TRANSLATION (High civic value)
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TRANSLATION = [
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"translate", "in spanish", "in french", "in portuguese",
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"in german", "in chinese", "in arabic", "in vietnamese",
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"in russian", "in korean", "in japanese", "in tagalog",
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"convert to", "say this in", "how do i say", "what is", "in hindi"
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]
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# 📄 PRIORITY 3: DOCUMENT PROCESSING (Forms, PDFs)
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DOCUMENT_PROCESSING = [
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"process this document", "extract data", "analyze pdf",
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"upload form", "read this file", "scan this", "form help",
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"fill out", "document", "pdf", "application", "permit"
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]
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# 🔍 PRIORITY 4: ANALYSIS TOOLS
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SENTIMENT_ANALYSIS = [
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"how does this sound", "is this positive", "is this negative",
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"analyze", "sentiment", "feel about", "mood", "tone"
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]
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BIAS_DETECTION = [
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"is this biased", "check bias", "check fairness", "is this neutral",
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"biased", "objective", "subjective", "fair", "discriminatory"
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]
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# 🌤️ PRIORITY 5: WEATHER + EVENTS (Compound intent handling)
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WEATHER = [
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"weather", "rain", "snow", "sunny", "forecast", "temperature",
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"hot", "cold", "storm", "wind", "outside", "climate",
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"degrees", "celsius", "fahrenheit"
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]
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# Specific date/time keywords that suggest event context
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DATE_TIME = [
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"today", "tomorrow", "this weekend", "next week",
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"sunday", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday",
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"tonight", "this morning", "this afternoon", "this evening"
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]
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EVENTS = [
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"event", "things to do", "what's happening", "activities",
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"festival", "concert", "activity", "community event",
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"show", "performance", "gathering", "meetup", "celebration"
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]
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# 🏛️ PRIORITY 6: LOCAL RESOURCES (Core civic mission)
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LOCAL_RESOURCES = [
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"resource", "shelter", "library", "help center",
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"food bank", "warming center", "cooling center", "csb",
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"mental health", "housing", "community service",
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"trash", "recycling", "transit", "bus", "schedule",
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"clinic", "hospital", "pharmacy", "assistance",
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"utility", "water", "electric", "gas", "bill"
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]
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# 💬 PRIORITY 7: CONVERSATIONAL
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GREETING = [
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"hi", "hello", "hey", "what's up", "good morning",
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"good afternoon", "good evening", "howdy", "yo",
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"greetings", "sup", "hiya"
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]
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HELP = [
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"help", "how do i", "can you help", "i need help",
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"what can you do", "how does this work", "instructions",
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"guide", "tutorial", "show me how"
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]
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def classify_intent(message: str) -> str:
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"""
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🎯 Main classification function (backward-compatible).
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Returns intent as string for existing API compatibility.
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Args:
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message: User's query text
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Returns:
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Intent string (e.g., "weather", "events", "translation")
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"""
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try:
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result = classify_intent_detailed(message)
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return result.intent.value
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except Exception as e:
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logger.error(f"Intent classification failed: {e}", exc_info=True)
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return IntentType.UNKNOWN.value
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def classify_intent_detailed(message: str) -> IntentMatch:
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"""
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🧠 Enhanced classification with confidence scores and metadata.
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This function:
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1. Checks for emergency keywords FIRST (safety routing)
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2. Detects compound intents (e.g., "weather + events")
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3. Returns structured result with confidence + matched keywords
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Args:
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message: User's query text
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Returns:
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IntentMatch object with full classification details
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"""
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if not message or not message.strip():
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logger.warning("Empty message received for intent classification")
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return IntentMatch(
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intent=IntentType.UNKNOWN,
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confidence=0.0,
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matched_keywords=[]
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)
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try:
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text = message.lower().strip()
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logger.debug(f"Classifying intent for: '{text[:50]}...'")
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# --- PRIORITY 1: EMERGENCY (Critical safety routing) ---
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emergency_matches = _find_keyword_matches(text, IntentPatterns.EMERGENCY)
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if emergency_matches:
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logger.warning(f"🚨 EMERGENCY intent detected: {emergency_matches}")
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return IntentMatch(
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intent=IntentType.EMERGENCY,
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confidence=1.0, # Always high confidence for safety
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matched_keywords=emergency_matches
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)
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# --- PRIORITY 2: TRANSLATION ---
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translation_matches = _find_keyword_matches(text, IntentPatterns.TRANSLATION)
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if translation_matches:
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return IntentMatch(
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intent=IntentType.TRANSLATION,
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confidence=0.9,
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matched_keywords=translation_matches
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)
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# --- PRIORITY 3: DOCUMENT PROCESSING ---
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doc_matches = _find_keyword_matches(text, IntentPatterns.DOCUMENT_PROCESSING)
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if doc_matches:
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return IntentMatch(
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intent=IntentType.DOCUMENT_PROCESSING,
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confidence=0.9,
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matched_keywords=doc_matches
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)
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# --- PRIORITY 4: ANALYSIS TOOLS ---
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sentiment_matches = _find_keyword_matches(text, IntentPatterns.SENTIMENT_ANALYSIS)
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if sentiment_matches:
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return IntentMatch(
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intent=IntentType.SENTIMENT_ANALYSIS,
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confidence=0.85,
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matched_keywords=sentiment_matches
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)
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bias_matches = _find_keyword_matches(text, IntentPatterns.BIAS_DETECTION)
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if bias_matches:
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return IntentMatch(
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intent=IntentType.BIAS_DETECTION,
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confidence=0.85,
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matched_keywords=bias_matches
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)
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# --- PRIORITY 5: LOCAL RESOURCES (Check before events to avoid false matches) ---
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resource_matches = _find_keyword_matches(text, IntentPatterns.LOCAL_RESOURCES)
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# --- PRIORITY 6: COMPOUND INTENT HANDLING (Weather + Events) ---
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weather_matches = _find_keyword_matches(text, IntentPatterns.WEATHER)
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event_matches = _find_keyword_matches(text, IntentPatterns.EVENTS)
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date_matches = _find_keyword_matches(text, IntentPatterns.DATE_TIME)
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# If both resource and event keywords match, prioritize resources (more specific)
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if resource_matches and event_matches:
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# Check if resource keywords are more specific (e.g., "library" vs generic "show")
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specific_resource_keywords = ["library", "libraries", "food bank", "shelter", "clinic", "hospital", "pharmacy", "trash", "recycling", "transit", "bus"]
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has_specific_resource = any(kw in text for kw in specific_resource_keywords)
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if has_specific_resource:
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return IntentMatch(
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intent=IntentType.LOCAL_RESOURCES,
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confidence=0.9,
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matched_keywords=resource_matches
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)
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# Compound detection: "What events are happening this weekend?"
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# or "What's the weather like for Sunday's festival?"
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if event_matches and (weather_matches or date_matches):
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logger.info("Compound intent detected: events + weather/date")
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return IntentMatch(
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intent=IntentType.EVENTS, # Primary intent
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confidence=0.85,
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matched_keywords=event_matches + weather_matches + date_matches,
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is_compound=True,
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secondary_intents=[IntentType.WEATHER]
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)
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# --- PRIORITY 7: SIMPLE WEATHER INTENT ---
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if weather_matches:
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return IntentMatch(
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intent=IntentType.WEATHER,
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confidence=0.9,
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matched_keywords=weather_matches
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)
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# --- PRIORITY 8: LOCAL RESOURCES (if not already handled) ---
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if resource_matches:
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return IntentMatch(
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intent=IntentType.LOCAL_RESOURCES,
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confidence=0.9,
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matched_keywords=resource_matches
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)
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# --- PRIORITY 9: EVENTS (Simple check) ---
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if event_matches:
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return IntentMatch(
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intent=IntentType.EVENTS,
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confidence=0.85,
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matched_keywords=event_matches
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)
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# --- PRIORITY 9: CONVERSATIONAL ---
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greeting_matches = _find_keyword_matches(text, IntentPatterns.GREETING)
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if greeting_matches:
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return IntentMatch(
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intent=IntentType.GREETING,
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confidence=0.8,
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matched_keywords=greeting_matches
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)
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help_matches = _find_keyword_matches(text, IntentPatterns.HELP)
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if help_matches:
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return IntentMatch(
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intent=IntentType.HELP,
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confidence=0.9,
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matched_keywords=help_matches
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)
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# --- FALLBACK: UNKNOWN ---
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logger.info(f"No clear intent match for: '{text[:50]}...'")
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return IntentMatch(
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intent=IntentType.UNKNOWN,
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confidence=0.0,
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matched_keywords=[]
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)
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except Exception as e:
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logger.error(f"Error during intent classification: {e}", exc_info=True)
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return IntentMatch(
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intent=IntentType.UNKNOWN,
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confidence=0.0,
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matched_keywords=[],
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)
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# --- HELPER FUNCTIONS ---
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def _find_keyword_matches(text: str, keywords: List[str]) -> List[str]:
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"""
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Finds which keywords from a pattern list appear in the user's message.
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Args:
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text: Normalized user message (lowercase)
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keywords: List of keywords to search for
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Returns:
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List of matched keywords (for debugging/logging)
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"""
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try:
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matches = []
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for keyword in keywords:
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if keyword in text:
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matches.append(keyword)
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return matches
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except Exception as e:
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logger.error(f"Error finding keyword matches: {e}", exc_info=True)
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return []
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def get_intent_description(intent: IntentType) -> str:
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"""
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🗣️ Penny's plain-English explanation of what each intent does.
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Useful for help systems and debugging.
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Args:
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intent: IntentType enum value
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Returns:
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Human-readable description of the intent
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"""
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descriptions = {
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IntentType.WEATHER: "Get current weather conditions and forecasts for your area",
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IntentType.GREETING: "Start a conversation with Penny",
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IntentType.LOCAL_RESOURCES: "Find community resources like shelters, libraries, and services",
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IntentType.EVENTS: "Discover local events and activities happening in your city",
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IntentType.TRANSLATION: "Translate text between 27 languages",
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IntentType.SENTIMENT_ANALYSIS: "Analyze the emotional tone of text",
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IntentType.BIAS_DETECTION: "Check text for potential bias or fairness issues",
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IntentType.DOCUMENT_PROCESSING: "Process PDFs and forms to extract information",
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IntentType.HELP: "Learn how to use Penny's features",
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IntentType.EMERGENCY: "Connect with emergency services and crisis support",
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IntentType.UNKNOWN: "I'm not sure what you're asking — can you rephrase?"
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}
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return descriptions.get(intent, "Unknown intent type")
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def get_all_supported_intents() -> Dict[str, str]:
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"""
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📋 Returns all supported intents with descriptions.
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Useful for /help endpoints and documentation.
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Returns:
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Dictionary mapping intent values to descriptions
|
| 389 |
-
"""
|
| 390 |
-
try:
|
| 391 |
-
return {
|
| 392 |
-
intent.value: get_intent_description(intent)
|
| 393 |
-
for intent in IntentType
|
| 394 |
-
if intent != IntentType.UNKNOWN
|
| 395 |
-
}
|
| 396 |
-
except Exception as e:
|
| 397 |
-
logger.error(f"Error getting supported intents: {e}", exc_info=True)
|
| 398 |
-
return {}
|
| 399 |
-
|
| 400 |
-
|
| 401 |
-
# --- FUTURE ML UPGRADE HOOK ---
|
| 402 |
-
def classify_intent_ml(message: str, use_embedding_model: bool = False) -> IntentMatch:
|
| 403 |
-
"""
|
| 404 |
-
🔮 PLACEHOLDER for future ML-based classification.
|
| 405 |
-
|
| 406 |
-
When ready to upgrade from keyword matching to embeddings:
|
| 407 |
-
1. Load Gemma-7B or sentence-transformers model
|
| 408 |
-
2. Generate message embeddings
|
| 409 |
-
3. Compare to intent prototype embeddings
|
| 410 |
-
4. Return top match with confidence score
|
| 411 |
-
|
| 412 |
-
Args:
|
| 413 |
-
message: User's query
|
| 414 |
-
use_embedding_model: If True, use ML model (not implemented yet)
|
| 415 |
-
|
| 416 |
-
Returns:
|
| 417 |
-
IntentMatch object (currently falls back to rule-based)
|
| 418 |
-
"""
|
| 419 |
-
|
| 420 |
-
if use_embedding_model:
|
| 421 |
-
logger.warning("ML-based classification not yet implemented. Falling back to rules.")
|
| 422 |
-
|
| 423 |
-
# Fallback to rule-based for now
|
| 424 |
-
return classify_intent_detailed(message)
|
| 425 |
-
|
| 426 |
-
|
| 427 |
-
# --- TESTING & VALIDATION ---
|
| 428 |
-
def validate_intent_patterns() -> Dict[str, List[str]]:
|
| 429 |
-
"""
|
| 430 |
-
🧪 Validates that all intent patterns are properly configured.
|
| 431 |
-
Returns any overlapping keywords that might cause conflicts.
|
| 432 |
-
|
| 433 |
-
Returns:
|
| 434 |
-
Dictionary of overlapping keywords between intent pairs
|
| 435 |
-
"""
|
| 436 |
-
try:
|
| 437 |
-
all_patterns = {
|
| 438 |
-
"emergency": IntentPatterns.EMERGENCY,
|
| 439 |
-
"translation": IntentPatterns.TRANSLATION,
|
| 440 |
-
"document": IntentPatterns.DOCUMENT_PROCESSING,
|
| 441 |
-
"sentiment": IntentPatterns.SENTIMENT_ANALYSIS,
|
| 442 |
-
"bias": IntentPatterns.BIAS_DETECTION,
|
| 443 |
-
"weather": IntentPatterns.WEATHER,
|
| 444 |
-
"events": IntentPatterns.EVENTS,
|
| 445 |
-
"resources": IntentPatterns.LOCAL_RESOURCES,
|
| 446 |
-
"greeting": IntentPatterns.GREETING,
|
| 447 |
-
"help": IntentPatterns.HELP
|
| 448 |
-
}
|
| 449 |
-
|
| 450 |
-
overlaps = {}
|
| 451 |
-
|
| 452 |
-
# Check for keyword overlap between different intents
|
| 453 |
-
for intent1, keywords1 in all_patterns.items():
|
| 454 |
-
for intent2, keywords2 in all_patterns.items():
|
| 455 |
-
if intent1 >= intent2: # Avoid duplicate comparisons
|
| 456 |
-
continue
|
| 457 |
-
|
| 458 |
-
overlap = set(keywords1) & set(keywords2)
|
| 459 |
-
if overlap:
|
| 460 |
-
key = f"{intent1}_vs_{intent2}"
|
| 461 |
-
overlaps[key] = list(overlap)
|
| 462 |
-
|
| 463 |
-
if overlaps:
|
| 464 |
-
logger.warning(f"Found keyword overlaps between intents: {overlaps}")
|
| 465 |
-
|
| 466 |
-
return overlaps
|
| 467 |
-
|
| 468 |
-
except Exception as e:
|
| 469 |
-
logger.error(f"Error validating intent patterns: {e}", exc_info=True)
|
| 470 |
-
return {}
|
| 471 |
-
|
| 472 |
-
|
| 473 |
-
# --- LOGGING SAMPLE CLASSIFICATIONS (For monitoring) ---
|
| 474 |
-
def log_intent_classification(message: str, result: IntentMatch) -> None:
|
| 475 |
-
"""
|
| 476 |
-
📊 Logs classification results for Azure Application Insights.
|
| 477 |
-
Helps track intent distribution and confidence patterns.
|
| 478 |
-
|
| 479 |
-
Args:
|
| 480 |
-
message: Original user message (truncated for PII safety)
|
| 481 |
-
result: IntentMatch classification result
|
| 482 |
-
"""
|
| 483 |
-
try:
|
| 484 |
-
# Truncate message for PII safety
|
| 485 |
-
safe_message = message[:50] + "..." if len(message) > 50 else message
|
| 486 |
-
|
| 487 |
-
logger.info(
|
| 488 |
-
f"Intent classified | "
|
| 489 |
-
f"intent={result.intent.value} | "
|
| 490 |
-
f"confidence={result.confidence:.2f} | "
|
| 491 |
-
f"compound={result.is_compound} | "
|
| 492 |
-
f"keywords={result.matched_keywords[:5]} | " # Limit logged keywords
|
| 493 |
-
f"message_preview='{safe_message}'"
|
| 494 |
-
)
|
| 495 |
-
except Exception as e:
|
| 496 |
-
logger.error(f"Error logging intent classification: {e}", exc_info=True)
|
|
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