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| # classifier.py | |
| import torch | |
| from model_loader import classifier_model | |
| from paraphraser import paraphrase_comment | |
| from metrics import compute_semantic_similarity, compute_empathy_score, compute_bleu_score, compute_rouge_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. | |
| """ | |
| if not comment.strip(): | |
| return "Error: Please enter a comment.", None, None, 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 | |
| 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) | |
| # 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 | |
| bleu_score = None | |
| rouge_scores = None | |
| if label == "Toxic": | |
| # Paraphrase the comment | |
| paraphrased_comment = paraphrase_comment(comment) | |
| # Re-evaluate the paraphrased comment | |
| 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) | |
| # Compute essential metrics | |
| semantic_similarity = compute_semantic_similarity(comment, paraphrased_comment) | |
| empathy_score = compute_empathy_score(paraphrased_comment) | |
| bleu_score = compute_bleu_score(comment, paraphrased_comment) | |
| rouge_scores = compute_rouge_score(comment, paraphrased_comment) | |
| 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, bleu_score, rouge_scores | |
| ) |