Update app.py

app.py

@@ -29,14 +29,15 @@ def preprocess(text):
 # Function to process the input and return prediction
 def sentiment_analysis(text):
     text = preprocess(text)
-    encoded_input = tokenizer(text, return_tensors="pt")
+
+    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
     output = model(**encoded_input)
-    scores_ = output.logits[0].detach().numpy()
+    scores_ = output[0][0].detach().numpy()
     scores_ = softmax(scores_)
     
     # Format output dict of scores
     labels = ["Negative", "Neutral", "Positive"]
-    scores = {l: float(s) for (l, s) in zip(labels, scores_)}
+    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
     
     return scores