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"""
we deploy the pipeline via streamlit.
"""
import re
import streamlit as st
from idiomify.fetchers import fetch_config, fetch_idiomifier, fetch_idioms, fetch_tokenizer
from idiomify.pipeline import Pipeline
@st.cache(allow_output_mutation=True)
def fetch_resources() -> tuple:
config = fetch_config()['idiomifier']
model = fetch_idiomifier(config['ver'])
tokenizer = fetch_tokenizer(config['tokenizer_ver'])
idioms = fetch_idioms(config['idioms_ver'])
return config, model, tokenizer, idioms
def main():
# fetch a pre-trained model
config, model, tokenizer, idioms = fetch_resources()
model.eval()
pipeline = Pipeline(model, tokenizer)
st.title("Idiomify Demo")
text = st.text_area("Type sentences here",
value="Just remember that there will always be a hope even when things look hopeless")
with st.sidebar:
st.subheader("Supported idioms")
idioms = [row["Idiom"] for _, row in idioms.iterrows()]
st.write(" / ".join(idioms))
if st.button(label="Idiomify"):
with st.spinner("Please wait..."):
sents = [sent for sent in text.split(".") if sent]
preds = pipeline(sents, max_length=200)
# highlight the rule & honorifics that were applied
preds = [re.sub(r"<idiom>|</idiom>", "`", pred)
for pred in preds]
st.markdown(". ".join(preds))
if __name__ == '__main__':
main()
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