Sentiments.py

# from googletrans import Translator
# from transformers import AutoTokenizer
# from transformers import AutoModelForSequenceClassification
# from scipy.special import softmax
#
# MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
# tokenizer = AutoTokenizer.from_pretrained(MODEL)
# model = AutoModelForSequenceClassification.from_pretrained(MODEL)
#
#
# def google_text_translate(text, dest_lang):
#     translator = Translator()
#     return translator.translate(text, dest = dest_lang).text
#
#
# def polarity_scores_roberta(example):
#   encoded_text = tokenizer(example, return_tensors = 'pt')
#   output = model(**encoded_text)
#   scores = output[0][0].detach().numpy()
#   scores = softmax(scores)
#   scores_dict = {'Negative': scores[0],
#                'Neutral': scores[1],
#                'Positive': scores[2]}
#   return scores_dict