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import os
import json
from typing import List
from groq import Groq
from models import Ticket, TicketClassification, TopicTagEnum, SentimentEnum, PriorityEnum
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class TicketClassifier:
def __init__(self):
# Get API key from environment
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
logger.error("GROQ_API_KEY environment variable not found")
raise ValueError("GROQ_API_KEY environment variable is required. Please set it in your HF Spaces secrets or local environment.")
try:
self.client = Groq(api_key=api_key)
# Use the more reliable model for classification
self.models = [
"moonshotai/kimi-k2-instruct-0905"
]
self.model = "moonshotai/kimi-k2-instruct-0905"
logger.info("TicketClassifier initialized successfully with Groq client")
except Exception as e:
logger.error(f"Failed to initialize Groq client: {e}")
raise
def _create_classification_prompt(self, ticket: Ticket) -> str:
topic_tags_list = [tag.value for tag in TopicTagEnum]
sentiment_list = [sentiment.value for sentiment in SentimentEnum]
priority_list = [priority.value for priority in PriorityEnum]
prompt = f"""
You are an expert customer support analyst for Atlan, a data catalog and governance platform.
Analyze the following support ticket and provide a classification.
TICKET DETAILS:
ID: {ticket.id}
Subject: {ticket.subject}
Body: {ticket.body}
CLASSIFICATION REQUIREMENTS:
1. TOPIC TAGS (select 1-3 most relevant from the list):
{', '.join(topic_tags_list)}
2. SENTIMENT (select exactly one):
{', '.join(sentiment_list)}
3. PRIORITY (select exactly one):
{', '.join(priority_list)}
PRIORITY GUIDELINES:
- P0 (High): Urgent issues blocking customers, production failures, security concerns
- P1 (Medium): Important functionality questions, configuration issues, feature requests
- P2 (Low): General questions, documentation requests, best practices
RESPONSE FORMAT:
Please respond with a valid JSON object in this exact format:
{{
"topic_tags": ["tag1", "tag2"],
"sentiment": "sentiment_value",
"priority": "priority_value",
"reasoning": "Brief explanation of your classification decision"
}}
IMPORTANT: Use these exact values:
- For priority: "P0 (High)", "P1 (Medium)", or "P2 (Low)"
- For sentiment: "Frustrated", "Curious", "Angry", or "Neutral"
- For topic_tags: Use exact values from the topic list above
Ensure your response is valid JSON and uses only the exact values from the lists provided above.
"""
return prompt
def _normalize_topic_tags(self, tags):
"""Normalize topic tags to match enum values."""
normalized_tags = []
for tag in tags:
try:
normalized_tags.append(TopicTagEnum(tag))
except ValueError:
tag_lower = tag.lower()
if 'how' in tag_lower and 'to' in tag_lower:
normalized_tags.append(TopicTagEnum.HOW_TO)
elif 'api' in tag_lower or 'sdk' in tag_lower:
normalized_tags.append(TopicTagEnum.API_SDK)
elif 'best' in tag_lower and 'practice' in tag_lower:
normalized_tags.append(TopicTagEnum.BEST_PRACTICES)
elif 'sensitive' in tag_lower or 'pii' in tag_lower:
normalized_tags.append(TopicTagEnum.SENSITIVE_DATA)
elif 'troubleshoot' in tag_lower or 'debug' in tag_lower or 'error' in tag_lower:
normalized_tags.append(TopicTagEnum.TROUBLESHOOTING)
elif 'integrat' in tag_lower:
normalized_tags.append(TopicTagEnum.INTEGRATION)
else:
normalized_tags.append(TopicTagEnum.PRODUCT)
logger.warning(f"Unknown topic tag '{tag}', using 'Product' as fallback")
return normalized_tags or [TopicTagEnum.PRODUCT]
def _normalize_sentiment(self, sentiment):
"""Normalize sentiment to match enum values."""
try:
return SentimentEnum(sentiment)
except ValueError:
sentiment_lower = sentiment.lower()
if 'frustrat' in sentiment_lower:
return SentimentEnum.FRUSTRATED
elif 'angry' in sentiment_lower or 'mad' in sentiment_lower:
return SentimentEnum.ANGRY
elif 'curious' in sentiment_lower or 'interest' in sentiment_lower:
return SentimentEnum.CURIOUS
else:
return SentimentEnum.NEUTRAL
def _normalize_priority(self, priority):
"""Normalize priority to match enum values."""
try:
return PriorityEnum(priority)
except ValueError:
priority_lower = str(priority).lower()
if 'p0' in priority_lower or 'high' in priority_lower or 'urgent' in priority_lower:
return PriorityEnum.P0
elif 'p2' in priority_lower or 'low' in priority_lower:
return PriorityEnum.P2
else:
return PriorityEnum.P1 # Default to medium
async def classify_ticket(self, ticket: Ticket) -> TicketClassification:
"""Classify a single ticket using Groq API."""
for model in self.models:
try:
prompt = self._create_classification_prompt(ticket)
response = self.client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": "You are an expert customer support analyst. Always respond with valid JSON."
},
{
"role": "user",
"content": prompt
}
],
temperature=0.1,
max_tokens=500
)
# Extract and parse the response
content = response.choices[0].message.content.strip()
logger.info(f"Raw AI response for ticket {ticket.id} using model {model}: {content}")
# Try to extract JSON from the response
if content.startswith("```json"):
content = content[7:-3].strip()
elif content.startswith("```"):
content = content[3:-3].strip()
try:
classification_data = json.loads(content)
except json.JSONDecodeError as e:
logger.error(f"JSON decode error for ticket {ticket.id} using model {model}: {e}")
continue # Try next model
# Normalize and validate the classification data
topic_tags = self._normalize_topic_tags(classification_data.get("topic_tags", ["Product"]))
sentiment = self._normalize_sentiment(classification_data.get("sentiment", "Neutral"))
priority = self._normalize_priority(classification_data.get("priority", "P1"))
# Validate and convert the classification
return TicketClassification(
topic_tags=topic_tags,
sentiment=sentiment,
priority=priority,
reasoning=classification_data.get("reasoning", f"AI-generated classification using {model}")
)
except Exception as e:
logger.error(f"Error classifying ticket {ticket.id} with model {model}: {str(e)}")
continue # Try next model
# If all models fail, return fallback classification
logger.error(f"All models failed for ticket {ticket.id}, using fallback")
return TicketClassification(
topic_tags=[TopicTagEnum.PRODUCT],
sentiment=SentimentEnum.NEUTRAL,
priority=PriorityEnum.P1,
reasoning="All AI models failed, using fallback classification"
)
async def classify_tickets_bulk(self, tickets: List[Ticket]) -> List[TicketClassification]:
"""Classify multiple tickets."""
classifications = []
for ticket in tickets:
classification = await self.classify_ticket(ticket)
classifications.append(classification)
logger.info(f"Classified ticket {ticket.id}")
return classifications
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