Update classifier.py

classifier.py

@@ -1,17 +1,17 @@
 # classifier.py
 import torch
-from model_loader import classifier_model  # Updated import
+from model_loader import classifier_model
 from paraphraser import paraphrase_comment
-from metrics import compute_semantic_similarity, compute_emotion_shift, compute_empathy_score, compute_bleu_score, compute_rouge_score, compute_entailment_score
+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 additional Stage 3 metrics.
+    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, None, None, None, None
+        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
@@ -39,7 +39,7 @@ def classify_toxic_comment(comment):
     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 additional metrics
+    # If the comment is toxic, paraphrase it and compute essential metrics
     paraphrased_comment = None
     paraphrased_prediction = None
     paraphrased_confidence = None
@@ -47,13 +47,9 @@ def classify_toxic_comment(comment):
     paraphrased_toxicity_score = None
     paraphrased_bias_score = None
     semantic_similarity = None
-    original_emotion = None
-    paraphrased_emotion = None
-    emotion_shift_positive = None
     empathy_score = None
     bleu_score = None
     rouge_scores = None
-    entailment_score = None
 
     if label == "Toxic":
         # Paraphrase the comment
@@ -74,18 +70,15 @@ def classify_toxic_comment(comment):
         paraphrased_bias_score = 0.01 if paraphrased_label == "Non-Toxic" else 0.15  # Placeholder
         paraphrased_bias_score = round(paraphrased_bias_score, 2)
 
-        # Compute additional Stage 3 metrics
+        # Compute essential metrics
         semantic_similarity = compute_semantic_similarity(comment, paraphrased_comment)
-        original_emotion, paraphrased_emotion, emotion_shift_positive = compute_emotion_shift(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)
-        entailment_score = compute_entailment_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, f"Original: {original_emotion}, Paraphrased: {paraphrased_emotion}, Positive Shift: {emotion_shift_positive}" if original_emotion else None,
-        empathy_score, bleu_score, rouge_scores, entailment_score
+        semantic_similarity, empathy_score, bleu_score, rouge_scores
     )