Update app.py

app.py

@@ -43,6 +43,7 @@ st.sidebar.success("Load Successfully!")
 
 if not torch.cuda.is_available():
     print("Warning: No GPU found. Please add GPU to your notebook")
+    torch.load(path, map_location='cpu')
 
 #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
 bi_encoder = SentenceTransformer(option1)
@@ -64,17 +65,6 @@ with open(embedding_cache_path, "rb") as fIn:
 # answer the query
 def search(query):
     print("Input question:", query)
-
-    ##### BM25 search (lexical search) #####
-    #bm25_scores = bm25.get_scores(bm25_tokenizer(query))
-    #top_n = np.argpartition(bm25_scores, -5)[-5:]
-    #bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
-    #bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
-
-    #print("Top-10 lexical search (BM25) hits")
-    #for hit in bm25_hits[0:10]:
-    #    print("\t{:.3f}\t{}".format(hit['score'], passages[hit['corpus_id']].replace("\n", " ")))
-
     ##### Sematic Search #####
     # Encode the query using the bi-encoder and find potentially relevant passages
     query_embedding = bi_encoder.encode(query, convert_to_tensor=True)