Update app.py

app.py

@@ -9,7 +9,7 @@ HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it
 
 @st.cache(allow_output_mutation=True)
 def get_model(model):
-	return pipeline("fill-mask", model=model, top_k=100)#set the maximum of tokens to be retrieved after each inference to model
+	return pipeline("fill-mask", model=model, top_k=20)#set the maximum of tokens to be retrieved after each inference to model
 
 def main(nlp, semantic_model):
     data_load_state = st.text('Inference to model...')
@@ -40,8 +40,13 @@ def main(nlp, semantic_model):
     
 if __name__ == '__main__':
     if st._is_running_with_streamlit:
-        st.caption("This is a simple auto-completion where the next token is predicted per probability and a weight if it is appeared in keyword user's history or there is a similarity to semantic user's history")
-        history_keyword_text = st.text_input("Enter users's history <keywords matc> (optional, i.e., 'Gates')", value="")
+        st.markdown("""
+# Introduction
+This is an example of an auto-complete approach where the next token suggested based on users's history Keyword match & Semantic similarity of users's history (log).
+The next token is predicted per probability and a weight if it is appeared in keyword user's history or there is a similarity to semantic user's history
+
+""")
+        history_keyword_text = st.text_input("Enter users's history <keywords match> (optional, i.e., 'Gates')", value="")
         
         text = st.text_input("Enter a text for auto completion...", value='Where is Bill')