Update app.py

app.py

@@ -45,12 +45,12 @@ st.sidebar.success("Load Successfully!")
 #    print("Warning: No GPU found. Please add GPU to your notebook")
 
 #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
-bi_encoder = SentenceTransformer(option1)
+bi_encoder = SentenceTransformer(option1, ,device='cpu')
 bi_encoder.max_seq_length = 256    #Truncate long passages to 256 tokens
 top_k = 32                          #Number of passages we want to retrieve with the bi-encoder
 
 #The bi-encoder will retrieve 100 documents. We use a cross-encoder, to re-rank the results list to improve the quality
-cross_encoder = CrossEncoder(option2)
+cross_encoder = CrossEncoder(option2, ,device='cpu')
 
 passages = []
 
@@ -69,7 +69,6 @@ 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