Update metrics.py

metrics.py

@@ -6,7 +6,7 @@ from transformers import pipeline
 # Load Sentence-BERT model for semantic similarity
 sentence_bert_model = SentenceTransformer('all-MiniLM-L6-v2')
 
-# Load a pre-trained emotion classifier (placeholder; replace with a specific model if available)
+# Load a pre-trained emotion classifier
 emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=None)
 
 def compute_semantic_similarity(original_comment, paraphrased_comment):
@@ -14,11 +14,8 @@ def compute_semantic_similarity(original_comment, paraphrased_comment):
     Compute the semantic similarity between the original and paraphrased comments using Sentence-BERT.
     Returns a score between 0 and 1 (higher is better).
     """
-    # Encode the comments into embeddings
     original_embedding = sentence_bert_model.encode(original_comment, convert_to_tensor=True)
     paraphrased_embedding = sentence_bert_model.encode(paraphrased_comment, convert_to_tensor=True)
-
-    # Compute cosine similarity
     similarity_score = util.cos_sim(original_embedding, paraphrased_embedding)[0][0].item()
     return round(similarity_score, 2)
 
@@ -29,11 +26,14 @@ def compute_emotion_shift(original_comment, paraphrased_comment):
     """
     # Classify emotions in the original comment
     original_emotions = emotion_classifier(original_comment)
-    original_dominant_emotion = max(original_emotions, key=lambda x: x['score'])['label']
+    # Since pipeline returns a list of lists, take the first (and only) inner list
+    original_emotions = original_emotions[0] if isinstance(original_emotions, list) and original_emotions else []
+    original_dominant_emotion = max(original_emotions, key=lambda x: x['score'])['label'] if original_emotions else "unknown"
 
     # Classify emotions in the paraphrased comment
     paraphrased_emotions = emotion_classifier(paraphrased_comment)
-    paraphrased_dominant_emotion = max(paraphrased_emotions, key=lambda x: x['score'])['label']
+    paraphrased_emotions = paraphrased_emotions[0] if isinstance(paraphrased_emotions, list) and paraphrased_emotions else []
+    paraphrased_dominant_emotion = max(paraphrased_emotions, key=lambda x: x['score'])['label'] if paraphrased_emotions else "unknown"
 
     # Define negative and positive emotions
     negative_emotions = ['anger', 'sadness', 'fear']
@@ -52,13 +52,8 @@ def compute_empathy_score(paraphrased_comment):
     Compute a proxy empathy score based on politeness keywords.
     Returns a score between 0 and 1 (higher indicates more empathy).
     """
-    # Define a list of politeness/empathy-related keywords
     empathy_keywords = ['please', 'thank you', 'appreciate', 'understand', 'sorry', 'consider', 'kindly', 'help', 'support']
-
-    # Count the number of empathy keywords in the paraphrased comment
     comment_lower = paraphrased_comment.lower()
     keyword_count = sum(1 for keyword in empathy_keywords if keyword in comment_lower)
-
-    # Normalize the score (arbitrary scaling; max 3 keywords for a score of 1)
     empathy_score = min(keyword_count / 3, 1.0)
     return round(empathy_score, 2)