Update metrics.py

metrics.py

@@ -1,12 +1,6 @@
 # metrics.py
-import nltk
-#from nltk.translate.bleu_score import sentence_bleu
-from rouge_score import rouge_scorer
 from model_loader import metrics_models
 
-# Download required NLTK data
-nltk.download('punkt')
-
 def compute_semantic_similarity(original, paraphrased):
     """
     Compute semantic similarity between the original and paraphrased comment using Sentence-BERT.
@@ -35,22 +29,4 @@ def compute_empathy_score(paraphrased):
         return round(score, 2)
     except Exception as e:
         print(f"Error computing empathy score: {str(e)}")
-        return None
-
-
-def compute_rouge_score(original, paraphrased):
-    """
-    Compute ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) between the original and paraphrased comment.
-    Returns a dictionary with ROUGE scores.
-    """
-    try:
-        scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
-        scores = scorer.score(original, paraphrased)
-        return {
-            'rouge1': round(scores['rouge1'].fmeasure, 2),
-            'rouge2': round(scores['rouge2'].fmeasure, 2),
-            'rougeL': round(scores['rougeL'].fmeasure, 2)
-        }
-    except Exception as e:
-        print(f"Error computing ROUGE scores: {str(e)}")
         return None