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2e8210f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gc
class SemanticAnalyzer:
def __init__(self):
print("🧠 [Step 2] Loading the English Semantic Analyzer (DistilBERT)...")
# Support for Apple Silicon (MPS), NVIDIA (CUDA), or CPU
if torch.backends.mps.is_available():
self.device = torch.device("mps")
elif torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
print(f" -> Computation device: {self.device}")
# Use DistilBERT: Lightweight, fast, and excellent for English context
self.model_name = "distilbert-base-uncased"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
# output_hidden_states=True to extract CLS vectors for similarity
self.model = AutoModelForSequenceClassification.from_pretrained(
self.model_name,
num_labels=2,
output_hidden_states=True
).to(self.device)
self.model.eval()
# [Vector A Space] Reference examples of English Dark Patterns & Subscription Traps
# These represent the "DNA" of deceptive billing behavior.
self.reference_bad_texts = [
"Start your free trial now, $0.00 today, automatically billed full price after 7 days.",
"No commitment, cancel anytime, but credit card required for identity verification.",
"Special $1 offer ends in 10 minutes, recurring monthly subscription starts thereafter.",
"Membership automatically renews at $49.99 per month unless cancelled via phone call.",
"Hidden service fees and processing charges applied at the final checkout step."
]
print(" -> Encoding global dark pattern references into vector space...")
self.reference_embeddings = self._get_embeddings(self.reference_bad_texts)
def clear_memory(self):
"""Prevents memory leaks during inference"""
gc.collect()
if self.device.type == "mps":
torch.mps.empty_cache()
elif self.device.type == "cuda":
torch.cuda.empty_cache()
def _get_embeddings(self, texts):
"""Converts text list into sentence embedding vectors (CLS token)"""
inputs = self.tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
# Use [CLS] token (index 0) from the last hidden layer
sentence_embeddings = outputs.hidden_states[-1][:, 0, :]
return sentence_embeddings
def calculate_x2_score(self, text):
if not text or len(text.strip()) < 10:
return 0.0
print("\n🧠 [Step 2] Analyzing semantic context for deceptive intent...")
# Truncate text to 512 tokens (standard for BERT-based models)
inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
# 1. Classification Logit (Raw Probability)
logits = outputs.logits
probs = F.softmax(logits, dim=-1)
prob_deceptive = probs[0][1].item()
# 2. Vector Similarity (Comparing vs known Dark Patterns)
current_embedding = outputs.hidden_states[-1][:, 0, :]
similarities = F.cosine_similarity(current_embedding, self.reference_embeddings)
max_sim = torch.max(similarities).item()
# Cleanup
del inputs, outputs, logits, probs, current_embedding, similarities
self.clear_memory()
# Final X2 Score Calculation (Ensemble of Probability + Similarity)
# Since the base model isn't fine-tuned specifically for dark patterns,
# Similarity (80%) is a stronger signal than raw classification (20%).
classification_score = prob_deceptive * 100
similarity_score = max(max_sim, 0) * 100
x2_score = (similarity_score * 0.8) + (classification_score * 0.2)
print(f" -> 📊 Intent Detection Prob: {prob_deceptive*100:.1f}%")
print(f" -> 🔗 Contextual Similarity to Traps: {max_sim*100:.1f}%")
print(f"📈 Final Semantic Risk Score: {x2_score:.2f}")
return x2_score
# ==========================================
# Unit Test
# ==========================================
if __name__ == "__main__":
analyzer = SemanticAnalyzer()
# Test: A typical subscription trap phrase
test_text = "Join now for $0! Just enter your card details for verification. You will be billed $99 after the trial."
analyzer.calculate_x2_score(test_text)
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