Create functions.py

functions.py

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+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