Update app.py

app.py

@@ -14,7 +14,7 @@ model = SentenceTransformer('neuml/pubmedbert-base-embeddings')
 with open("embeddings_1.pkl", "rb") as fIn:
     stored_data = pickle.load(fIn)
     stored_code = stored_data["SBS_code"]
-    stored_sentences = stored_data["sentences"]
+    stored_sentences = stored_data["Description"]
     stored_embeddings = stored_data["embeddings"]
 
 import streamlit as st
@@ -28,7 +28,7 @@ def mapping_code(user_input):
         similarities.append(similarity)
 
     # Combine similarity scores with 'code' and 'description'
-    result = list(zip(stored_data["SBS_code"],stored_data["sentences"], similarities))
+    result = list(zip(stored_data["SBS_code"],stored_data["Description"], similarities))
 
     # Sort results by similarity scores
     result.sort(key=lambda x: x[2], reverse=True)