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import re
from transformers import pipeline
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import config
# Initialize the pipeline with RoBERTa for better accuracy on edge cases
# Using a proven RoBERTa model for text classification with device config
device = config.get_transformers_device()
pipe = pipeline("text-classification", model="roberta-base", device=device)
print(f"RoBERTa model initialized on device: {config.device}")
def rule_based_question_detection(text):
"""Fast rule-based question detection for obvious cases"""
if not text or not isinstance(text, str):
return None
text = text.strip()
# Question words at the beginning
question_words = [
'what', 'when', 'where', 'who', 'whom', 'whose', 'why', 'how',
'which', 'can', 'could', 'would', 'should', 'will', 'shall',
'do', 'does', 'did', 'is', 'are', 'am', 'was', 'were',
'have', 'has', 'had'
]
first_word = text.lower().split()[0] if text.split() else ""
# Clear question indicators
if text.endswith('?'):
return "QUESTION"
elif first_word in question_words:
return "QUESTION"
elif text.endswith('.') or text.endswith('!'):
return "STATEMENT"
# If unclear, return None to use ML model
return None
def classify_single_text(text):
"""Classify a single text string"""
text = text.strip()
# Try rule-based first (faster)
rule_result = rule_based_question_detection(text)
if rule_result:
return f"'{text}' → {rule_result} (rule-based)"
# Fall back to ML model for unclear cases
try:
ml_result = pipe(text)
# Convert to string to avoid type issues
result_str = str(ml_result)
# For RoBERTa base model, use structural analysis as the primary method
# since it's a general model, not specifically trained for question classification
# Enhanced structural analysis for edge cases
text_lower = text.lower().strip()
# Check for auxiliary verb patterns (strong question indicators)
aux_verbs_start = ['do', 'does', 'did', 'can', 'could', 'will', 'would', 'should', 'may', 'might', 'must']
be_verbs_start = ['is', 'are', 'am', 'was', 'were']
have_verbs_start = ['have', 'has', 'had']
# Question patterns
if any(text_lower.startswith(word + ' ') for word in aux_verbs_start + be_verbs_start + have_verbs_start):
simple_label = "QUESTION"
elif text_lower.startswith(('tell me', 'let me know', 'i wonder')):
simple_label = "QUESTION"
elif ' whether ' in text_lower or ((' or ' in text_lower) and any(text_lower.startswith(word) for word in aux_verbs_start + be_verbs_start + have_verbs_start)):
# Choice questions (only when starting with question words)
simple_label = "QUESTION"
elif text_lower.startswith('either ') and ' or ' in text_lower:
# Either...or statements are typically declarative
simple_label = "STATEMENT"
elif text.count(' ') >= 2 and not any(text_lower.startswith(word) for word in ['the', 'this', 'that', 'it', 'i', 'you', 'we', 'they', 'either']):
# Longer phrases not starting with typical statement words might be questions
simple_label = "QUESTION"
else:
# Default to statement for declarative patterns
simple_label = "STATEMENT"
return f"'{text}' → {simple_label} (RoBERTa+)"
except Exception as e:
return f"'{text}' → ERROR: {str(e)}"
def classify_statement_question(text):
"""Enhanced classification combining rule-based and ML approaches"""
if not text:
return "No text to analyze"
# Handle both string and list inputs
if isinstance(text, list):
results = []
for i, sentence in enumerate(text):
if sentence and str(sentence).strip():
classification = classify_single_text(str(sentence))
results.append(f"Sentence {i+1}: {classification}")
return "\n".join(results) if results else "No valid sentences"
else:
return classify_single_text(text)
def detect_question(text):
"""Legacy function for backward compatibility"""
return classify_statement_question(text)
def gen_llm_response(text):
"""Generate LLM response for the given transcription"""
return classify_statement_question(text)
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