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# classifier.py
import torch
import time
from model_loader import classifier_model
from paraphraser import paraphrase_comment
from metrics import compute_semantic_similarity, compute_empathy_score
def classify_toxic_comment(comment):
"""
Classify a comment as toxic or non-toxic using the fine-tuned XLM-RoBERTa model.
If toxic, paraphrase the comment, re-evaluate, and compute essential metrics.
Returns the prediction label, confidence, color, toxicity score, bias score, paraphrased comment (if applicable), and its metrics.
"""
start_total = time.time()
print("Starting classification...")
if not comment.strip():
return "Error: Please enter a comment.", None, None, None, None, None, None, None, None, None, None
# Access the model and tokenizer
model = classifier_model.model
tokenizer = classifier_model.tokenizer
# Tokenize the input comment
start_classification = time.time()
inputs = tokenizer(comment, return_tensors="pt", truncation=True, padding=True, max_length=512)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Get the predicted class (0 = non-toxic, 1 = toxic)
predicted_class = torch.argmax(logits, dim=1).item()
label = "Toxic" if predicted_class == 1 else "Non-Toxic"
confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
label_color = "red" if label == "Toxic" else "green"
# Compute Toxicity Score (approximated as the probability of the toxic class)
toxicity_score = torch.softmax(logits, dim=1)[0][1].item()
toxicity_score = round(toxicity_score, 2)
# Simulate Bias Score (placeholder)
bias_score = 0.01 if label == "Non-Toxic" else 0.15
bias_score = round(bias_score, 2)
print(f"Classification took {time.time() - start_classification:.2f} seconds")
# If the comment is toxic, paraphrase it and compute essential metrics
paraphrased_comment = None
paraphrased_prediction = None
paraphrased_confidence = None
paraphrased_color = None
paraphrased_toxicity_score = None
paraphrased_bias_score = None
semantic_similarity = None
empathy_score = None
if label == "Toxic":
# Paraphrase the comment
start_paraphrase = time.time()
paraphrased_comment = paraphrase_comment(comment)
print(f"Paraphrasing took {time.time() - start_paraphrase:.2f} seconds")
# Re-evaluate the paraphrased comment
start_reclassification = time.time()
paraphrased_inputs = tokenizer(paraphrased_comment, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
paraphrased_outputs = model(**paraphrased_inputs)
paraphrased_logits = paraphrased_outputs.logits
paraphrased_predicted_class = torch.argmax(paraphrased_logits, dim=1).item()
paraphrased_label = "Toxic" if paraphrased_predicted_class == 1 else "Non-Toxic"
paraphrased_confidence = torch.softmax(paraphrased_logits, dim=1)[0][paraphrased_predicted_class].item()
paraphrased_color = "red" if paraphrased_label == "Toxic" else "green"
paraphrased_toxicity_score = torch.softmax(paraphrased_logits, dim=1)[0][1].item()
paraphrased_toxicity_score = round(paraphrased_toxicity_score, 2)
paraphrased_bias_score = 0.01 if paraphrased_label == "Non-Toxic" else 0.15 # Placeholder
paraphrased_bias_score = round(paraphrased_bias_score, 2)
print(f"Reclassification of paraphrased comment took {time.time() - start_reclassification:.2f} seconds")
# Compute essential metrics
start_metrics = time.time()
semantic_similarity = compute_semantic_similarity(comment, paraphrased_comment)
empathy_score = compute_empathy_score(paraphrased_comment)
print(f"Metrics computation took {time.time() - start_metrics:.2f} seconds")
print(f"Total processing time: {time.time() - start_total:.2f} seconds")
return (
f"Prediction: {label}", confidence, label_color, toxicity_score, bias_score,
paraphrased_comment, f"Prediction: {paraphrased_label}" if paraphrased_comment else None,
paraphrased_confidence, paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
semantic_similarity, empathy_score
) |