from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification from scipy.special import softmax # Define the preprocess function def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Define the sentiment_analysis function def sentiment_analysis(text, tokenizer, model): text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores_ = output[0][0].detach().numpy() scores_ = softmax(scores_) labels = ['Negative', 'Positive'] scores = {l: float(s) for (l, s) in zip(labels, scores_)} return scores # Define the map_sentiment_score_to_rating function def map_sentiment_score_to_rating(score): min_score = 0.0 max_score = 1.0 min_rating = 1 max_rating = 10 rating = ((score - min_score) / (max_score - min_score)) * (max_rating - min_rating) + min_rating return rating