Update app.py

app.py

@@ -45,10 +45,9 @@ embeddings = np.load("netflix_embeddings.npy")  #created using sentence_transfor
 metadata = pd.read_csv("netflix_metadata.csv") #created using sentence_transformers on kaggle
 
 # Vector search function
-def vector_search(query,response_amount=3):
+def vector_search(query,top_n=3):
     query_embedding = sentence_model.encode(query)
     similarities = cosine_similarity([query_embedding], embeddings)[0]
-    top_n=response_amount
     top_indices = similarities.argsort()[-top_n:][::-1]
     results = metadata.iloc[top_indices]
     result_text=""
@@ -61,6 +60,8 @@ def vector_search(query,response_amount=3):
 
 
 def set_response_amount(response_amount):
+    if response_amount is None:
+        return 3
     return response_amount
 
  # Based on the selected input type, make the appropriate input visible