Update app.py

app.py

@@ -52,12 +52,14 @@ top_k = 32                          #Number of passages we want to retrieve with
 #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)
 
+passages = []
+
 # load pre-train embeedings files
 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)
-  #corpus_sentences = cache_data['sentences']
+  passages = cache_data['sentences']
   corpus_embeddings = cache_data['embeddings']
 
 # This function will search all wikipedia articles for passages that