File size: 1,368 Bytes
253de62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
"""
we deploy the pipeline via streamlit.
"""
from typing import Tuple, List
import streamlit as st
from transformers import BartTokenizer
from idiomify.fetchers import fetch_config, fetch_idiomifier, fetch_idioms
from idiomify.pipeline import Pipeline
from idiomify.models import Idiomifier
@st.cache(allow_output_mutation=True)
def fetch() -> Tuple[Idiomifier, BartTokenizer, List[str]]:
config = fetch_config()['idiomifier']
model = fetch_idiomifier(config['ver'])
idioms = fetch_idioms(config['idioms_ver'])
tokenizer = BartTokenizer.from_pretrained(config['bart'])
return model, tokenizer, idioms
def main():
# fetch a pre-trained model
model, tokenizer, idioms = fetch()
pipeline = Pipeline(model, tokenizer)
st.title("Idiomify Demo")
text = st.text_area("Type sentences here",
value="Just remember there will always be a hope even when things look black")
with st.sidebar:
st.subheader("Supported idioms")
st.write(" / ".join(idioms))
if st.button(label="Idiomify"):
with st.spinner("Please wait..."):
sents = [sent for sent in text.split(".") if sent]
sents = pipeline(sents, max_length=200)
# highlight the rule & honorifics that were applied
st.write(". ".join(sents))
if __name__ == '__main__':
main()
|