Update app.py

app.py

@@ -68,13 +68,13 @@ with mod_container:
         simscore = PathFinder.predict([prompt])
 
         from sentence_transformers import CrossEncoder
-        XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
+        loaded_model = CrossEncoder("cross-encoder/stsb-roberta-base")
         sentence_pairs = []
         for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
           sentence_pairs.append([sentence1, sentence2])
 
         # sorting the df to get highest scoring xpath_container
-        data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs)
+        data['SBERT CrossEncoder_Score'] = loaded_model.predict(sentence_pairs)
         most_acc = data.head(5)
         # predictions
         st.write("Highest Similarity score: ", simscore)