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, top_k=100)




history_keyword_text = st.text_input("Enter users's history keywords (optional, i.e., 'Gates')")

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

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

if text:
    data_load_state = st.text('Loading model...')
    nlp = get_model(model)
    data_load_state = st.text('Inference to model...')
    result = nlp(text+' '+nlp.tokenizer.mask_token)
    data_load_state.text('')
    for index, r in enumerate(result):
        if r['sequence'].lower().strip() in history_keyword_text.lower().strip():
            result[index]['score']*=0.10
    st.table(result)