| |
| 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 |
|
|
| |
| model = classifier_model.model |
| tokenizer = classifier_model.tokenizer |
|
|
| |
| start_classification = time.time() |
| inputs = tokenizer(comment, return_tensors="pt", truncation=True, padding=True, max_length=512) |
|
|
| |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| logits = outputs.logits |
|
|
| |
| 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" |
|
|
| |
| toxicity_score = torch.softmax(logits, dim=1)[0][1].item() |
| toxicity_score = round(toxicity_score, 2) |
|
|
| |
| 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") |
|
|
| |
| 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": |
| |
| start_paraphrase = time.time() |
| paraphrased_comment = paraphrase_comment(comment) |
| print(f"Paraphrasing took {time.time() - start_paraphrase:.2f} seconds") |
|
|
| |
| 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 |
| paraphrased_bias_score = round(paraphrased_bias_score, 2) |
| print(f"Reclassification of paraphrased comment took {time.time() - start_reclassification:.2f} seconds") |
|
|
| |
| 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 |
| ) |
