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import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
print("sarcasm module loaded (BERT Sarcasm Detector)")
# FIX: Use a Twitter-based Irony model (RoBERTa) which is better for social media/Reddit
MODEL_NAME = "cardiffnlp/twitter-roberta-base-irony"
try:
# FIX: Force use_fast=False to avoid Windows rust-tokenizer crashes
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.eval()
except Exception as e:
print(f"CRITICAL ERROR loading sarcasm model: {e}")
raise e
def sarcasm_score(text: str) -> float:
"""
Deep sarcasm probability (0-1).
Uses helinivan/english-sarcasm-detector (BERT-based).
"""
with torch.no_grad():
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
padding=True,
max_length=128
)
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
# The model 'helinivan/english-sarcasm-detector' labels:
# 0: Not Sarcastic
# 1: Sarcastic
# We want the probability of it being sarcastic (index 1)
return float(probs[0][1])
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