Update app.py

app.py

@@ -57,10 +57,11 @@ 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)
+    message(hit)
     corpus_id = hit['corpus_id']
+    message(corpus_id)
     saved = corpus[corpus_id]
-    print(saved)
+    message(saved)
     # Find source document based on sentence index
     count = 0
     for idx, c in enumerate(sentence_count):
@@ -74,6 +75,7 @@ def find_sentences(query):
 
 
 
+
 message_history = [{"text":"Let's find out the best task for your use case! Tell me about your use case :)", "is_user":False}]
 
 st.subheader("If you don't know how to build your machine learning product for your use case, Taskmaster is here to help you! 🪄✨")