Update metrics.py

metrics.py

@@ -21,35 +21,6 @@ def compute_semantic_similarity(original, paraphrased):
         print(f"Error computing semantic similarity: {str(e)}")
         return None
 
-def compute_emotion_shift(original, paraphrased):
-    """
-    Compute the emotion shift between the original and paraphrased comment.
-    Returns the original emotion, paraphrased emotion, and whether the shift is positive.
-    """
-    try:
-        emotion_classifier = metrics_models.load_emotion_classifier()
-        original_emotions = emotion_classifier(original)
-        paraphrased_emotions = emotion_classifier(paraphrased)
-
-        # Get the top emotion for each
-        original_emotion = max(original_emotions[0], key=lambda x: x['score'])['label']
-        paraphrased_emotion = max(paraphrased_emotions[0], key=lambda x: x['score'])['label']
-
-        # Define negative and positive emotions
-        negative_emotions = ['anger', 'sadness', 'fear']
-        positive_emotions = ['joy', 'love', 'surprise']
-
-        # Determine if the shift is positive
-        emotion_shift_positive = (
-            (original_emotion in negative_emotions and paraphrased_emotion in positive_emotions) or
-            (original_emotion in negative_emotions and paraphrased_emotion not in negative_emotions)
-        )
-
-        return original_emotion, paraphrased_emotion, emotion_shift_positive
-    except Exception as e:
-        print(f"Error computing emotion shift: {str(e)}")
-        return None, None, None
-
 def compute_empathy_score(paraphrased):
     """
     Compute an empathy score for the paraphrased comment (placeholder).
@@ -95,22 +66,4 @@ def compute_rouge_score(original, paraphrased):
         }
     except Exception as e:
         print(f"Error computing ROUGE scores: {str(e)}")
-        return None
-
-def compute_entailment_score(original, paraphrased):
-    """
-    Compute the entailment score to check factual consistency using an NLI model.
-    Returns a score between 0 and 1.
-    """
-    try:
-        nli_classifier = metrics_models.load_nli_classifier()
-        result = nli_classifier(
-            original,
-            paraphrased,
-            candidate_labels=["entailment", "contradiction", "neutral"]
-        )
-        entailment_score = next(score for label, score in zip(result['labels'], result['scores']) if label == "entailment")
-        return round(entailment_score, 2)
-    except Exception as e:
-        print(f"Error computing entailment score: {str(e)}")
         return None