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main.py

@@ -44,11 +44,10 @@ def analyze_text(text):
         ner_outputs = ner_model(**ner_inputs)
     
     ner_predictions = torch.argmax(ner_outputs, dim=-1)
-    ner_labels = [ner_config["id2label"][label] for label in ner_predictions[0].tolist()]
-
+    ner_labels = ner_predictions.tolist()
 
     # Tokenize input text for sentiment analysis
-    sentiment_inputs = sentiment_tokenizer.encode_plus(text, return_tensors="pt")
+    sentiment_inputs = sentiment_tokenizer(text, return_tensors="pt")
 
     # Perform sentiment analysis
     with torch.no_grad():