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| from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline | |
| tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa") | |
| model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa") | |
| emotion = pipeline('sentiment-analysis', | |
| model='arpanghoshal/EmoRoBERTa') | |
| def get_emotion(text): | |
| emotion_labels = emotion(text) | |
| emotion_detail = [item['label'] for item in emotion_labels] | |
| print("The detected emotion is:", emotion_detail) | |
| confidence_score = str(round([item['score'] for item in emotion_labels][0]*100, 2)) + "%" | |
| print("The confidence score is:", confidence_score) | |
| return emotion_detail, confidence_score |