Update app.py

app.py

@@ -115,7 +115,6 @@ def search(query):
     ##### Sematic Search #####
     # Encode the query using the bi-encoder and find potentially relevant passages
     query_embedding = bi_encoder.encode(query, convert_to_tensor=True)
-    query_embedding = query_embedding.cuda()
     hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)
     hits = hits[0]  # Get the hits for the first query