efficiency?

app.py

@@ -1,17 +1,20 @@
 import streamlit as st
 
-# Create a text element and let the reader know the data is loading.
-data_load_state = st.text('Loading data...')
-# Load 10,000 rows of data into the dataframe.
-# Notify the reader that the data was successfully loaded.
 from transformers import pipeline
 
-nlp = pipeline("fill-mask", model="roberta-base")
-data_load_state.text('Loading data...done!')
+@st.cached
+def get_model():
+	return pipeline("fill-mask", model="roberta-base")
+
 
 text = st.text_input("Enter a sentence. Use a * for a mask.")
-result = nlp(text.replace("*",nlp.tokenizer.mask_token))
 
+# Create a text element and let the reader know the data is loading.
 
-for d in result:
-	st.write(str(d["score"])+"\n"+d["sequence"])
+if text:
+	data_load_state = st.text('Loading data...')
+	nlp = get_model()
+	result = nlp(text.replace("*",nlp.tokenizer.mask_token))
+	data_load_state.text('Loading data...done!')
+	for d in result:
+		st.write(str(d["score"])+"\n"+d["sequence"])