Update app.py

app.py

@@ -41,8 +41,8 @@ option2 = st.sidebar.selectbox(
 
 st.sidebar.success("Load Successfully!")
 
-if not torch.cuda.is_available():
-    print("Warning: No GPU found. Please add GPU to your notebook")
+#if not torch.cuda.is_available():
+#    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)
@@ -53,7 +53,7 @@ top_k = 32                          #Number of passages we want to retrieve with
 cross_encoder = CrossEncoder(option2)
 
 # load pre-train embeedings files
-embedding_cache_path = 'etsy-embeddings.pkl'
+embedding_cache_path = 'etsy-embeddings-cpu.pkl'
 print("Load pre-computed embeddings from disc")
 with open(embedding_cache_path, "rb") as fIn:
   cache_data = pickle.load(fIn)
@@ -67,7 +67,7 @@ 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()
+    #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