Update app.py

app.py

@@ -21,6 +21,7 @@ wikipedia_embedding = load_embedding()
 st.success('Embedding loaded!')
 st_model_load.text("")
 
+@st.cache_resource
 def load_encoders():
     print("Loading encoders...")
     bi_encoder = SentenceTransformer('msmarco-MiniLM-L-6-v3')
@@ -40,20 +41,11 @@ st_text_area = st.text_area(
     value=st.session_state.text, 
     height=100
 )
-"""
-
 
-#We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
-# cf https://www.sbert.net/docs/pretrained-models/msmarco-v3.html
-bi_encoder = SentenceTransformer('msmarco-MiniLM-L-6-v3')
-bi_encoder.max_seq_length = 256     #Truncate long passages to 256 tokens
-top_k = 32                          #Number of passages we want to retrieve with the bi-encoder
 
-#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('cross-encoder/ms-marco-TinyBERT-L-2-v2')
-
-def search(query):
-    print("Input question:", query)
+def search():
+    st.session_state.text = st_text_area
+    query = st_text_area
     ##### Sematic Search #####
     # Encode the query using the bi-encoder and find potentially relevant passages
     question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
@@ -81,4 +73,6 @@ def search(query):
               "link: ", dataset["url"][hit['corpus_id']],"\n")
 
 
-"""
+# search button
+st_search_button = st.button('Search', on_click=search)
+