app.py

import streamlit as st

from transformers import pipeline

@st.cache(allow_output_mutation=True)
def get_model(model):
	return pipeline("fill-mask", model=model)


text = st.text_input("Enter a text for auto completion...")

history_keyword_text = st.text_input("Users's history keyword split with ','")

model = st.selectbox("choose a model", ["roberta-base", "bert-base-uncased", "gpt2", "t5"])

if text:
	data_load_state = st.text('Loading...')
	nlp = get_model(model)
	result = nlp(text+nlp.tokenizer.mask_token)
	data_load_state.text('Loading data...done!')
#	for c in result:
#		del c["token"]
	st.table(result)