Update app.py

app.py

@@ -137,7 +137,7 @@ def generate_query_expansion_candidates(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()
     # Get the hits for the first query
     encoder_hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)[0]
 
@@ -223,8 +223,8 @@ def re_rank_candidates(query, candidates, method):
         # first sort by cross_score + bi_score
         filtered_sorted_result = sorted(filtered_sorted_by_encoder, key=lambda x: x[1]['gms'], reverse=True
         )
-    st.write("Reranking Results: \n")
-    st.write(filtered_sorted_result)
+    return filtered_sorted_result
+
 
 # def reranking():
 #     rerank_list = []