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# Emotion analysis
from src.exception.exception import customexception
from src.logger.logger import logging
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from scipy.special import softmax
import sys
# Pretrained model
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
sentiment_model = AutoModelForSequenceClassification.from_pretrained(MODEL)
class EmotionAnalyzer:
def __init__(self):
self.emotion_classifier = sentiment_model
self.emotion = ''
# Roberta model
def analyze_emotion(self, text):
try:
encoded_text = tokenizer(text, return_tensors='pt')
output = self.emotion_classifier(**encoded_text)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
scores_dict = {
'negative': scores[0],
'neutral': scores[1],
'positive': scores[2],
}
self.emotion = max(scores_dict)
logging.info("Sentiment of response generated.")
return self.emotion
except Exception as e:
raise customexception(e,sys) |