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# 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.
Returns a similarity score between 0 and 1.
"""
try:
sentence_bert = metrics_models.load_sentence_bert()
embeddings = sentence_bert.encode([original, paraphrased])
similarity = float(embeddings[0] @ embeddings[1].T)
return round(similarity, 2)
except Exception as e:
print(f"Error computing semantic similarity: {str(e)}")
return None
def compute_empathy_score(paraphrased):
"""
Compute an empathy score for the paraphrased comment (placeholder).
Returns a score between 0 and 1.
"""
try:
# Placeholder: Compute empathy based on word presence (e.g., "sorry", "understand")
empathy_words = ["sorry", "understand", "care", "help", "support"]
words = paraphrased.lower().split()
empathy_count = sum(1 for word in words if word in empathy_words)
score = empathy_count / len(words) if words else 0
return round(score, 2)
except Exception as e:
print(f"Error computing empathy score: {str(e)}")
return None
def compute_bleu_score(original, paraphrased):
"""
Compute the BLEU score between the original and paraphrased comment.
Returns a score between 0 and 1.
"""
try:
reference = [nltk.word_tokenize(original.lower())]
candidate = nltk.word_tokenize(paraphrased.lower())
score = sentence_bleu(reference, candidate, weights=(0.25, 0.25, 0.25, 0.25))
return round(score, 2)
except Exception as e:
print(f"Error computing BLEU 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 |