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import time
import errant
import spacy
import streamlit as st
from happytransformer import HappyTextToText, TTSettings
from highlighter import show_edits, show_highlights
checkpoints = [
"aseifert/t5-base-jfleg-wi",
"aseifert/byt5-base-jfleg-wi",
"prithivida/grammar_error_correcter_v2",
"Modfiededition/t5-base-fine-tuned-on-jfleg",
]
@st.cache
def download_spacy_model(model="en"):
spacy.cli.download(model) # type: ignore
return True
@st.cache(suppress_st_warning=True, allow_output_mutation=True)
def get_model(model_name):
return HappyTextToText("T5", model_name)
@st.cache(suppress_st_warning=True, allow_output_mutation=True)
def get_annotator(lang: str):
return errant.load(lang)
def output(model, args, annotator, input_text):
with st.spinner("Checking for errors πŸ”"):
prefixed_input_text = "Grammar: " + input_text
result = model.generate_text(prefixed_input_text, args=args).text
try:
st.success(result)
show_highlights(annotator, input_text, result)
# st.table(show_edits(annotator, input_text, result))
except Exception as e:
st.error("Some error occured!" + str(e))
st.stop()
def main():
st.title("πŸ€— Writing Assistant")
st.markdown(
"""This writing assistant will proofread any text for you! See my [GitHub repo](https://github.com/aseifert/hf-writing-assistant) for implementation details."""
)
download_spacy_model()
annotator = get_annotator("en")
checkpoint = st.selectbox("Choose model", checkpoints)
model = get_model(checkpoint)
args = TTSettings(num_beams=5, min_length=1, max_length=1024)
default_text = "A dog is bigger then mouse."
default_text = "what be the reason for everyone leave the comapny"
input_text = st.text_area(
label="Original text",
value=default_text,
)
if st.button("✍️ Check"):
start = time.time()
output(model, args, annotator, input_text)
st.write("---")
st.text(f"Built by Team Writing Assistant ❀️ – prediction took {time.time() - start:.2f}s")
if __name__ == "__main__":
main()