Update app.py

app.py

@@ -65,14 +65,14 @@ with mod_container:
     # let's pass the input to the loaded_model with torch compiled with cuda
     if prompt:
         # let's get the result
-        simscore = PathFinder.predict([prompt])
 
         from sentence_transformers import CrossEncoder
         loaded_model = CrossEncoder("cross-encoder/stsb-roberta-base")
         sentence_pairs = []
         for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
           sentence_pairs.append([sentence1, sentence2])
-
+        
+        simscore = PathFinder.predict([prompt])
         # sorting the df to get highest scoring xpath_container
         data['SBERT CrossEncoder_Score'] = loaded_model.predict(sentence_pairs)
         most_acc = data.head(5)