Update app.py

app.py

@@ -50,8 +50,6 @@ for sent in context.split("."):
         
         
 corpus_embeddings = np.load('task_embeddings_msmarco-distilbert-base-v4.npy')
-corpus_embeddings.shape
-
 
 
 
@@ -59,8 +57,10 @@ def find_sentences(query):
     query_embedding = model.encode(query)
     hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=1)
     hit = hits[0][0]
+    print(hit)
     corpus_id = hit['corpus_id']
     saved = corpus[corpus_id]
+    print(saved)
     # Find source document based on sentence index
     count = 0
     for idx, c in enumerate(sentence_count):