app.py

import streamlit as st
import transformers
import tensorflow
import PIL
from PIL import Image
import time
from transformers import pipeline

model_checkpoint = "Modfiededition/t5-base-fine-tuned-on-jfleg"


@st.cache(allow_output_mutation=True, suppress_st_warning=True)
def load_model():
    return pipeline("text2text-generation", model=model_checkpoint)
model = load_model()

    
#prompts
st.title("당신의 영어 일기장을 위해! 🤖")
st.markdown("당신의 영어 문법을 체크해 드립니다.! This assitant uses **T5-base model ✍️** fine-tuned on jfleg dataset.")

st.subheader("Some examples: ")
example_1 = st.button("I am write on AI")
example_2 = st.button("This sentence has, bads grammar mistake!")

textbox = st.text_area('Write your text in this box:', '',height=100,  max_chars=500 )

button = st.button('Detect grammar mistakes:')

# output
st.subheader("Correct sentence: ")
if example_1:
    with st.spinner('In progress.......'):
        output_text = model("I am write on AI")[0]["generated_text"]
    st.markdown("## "+output_text)

if example_2:
    with st.spinner('In progress.......'):
        output_text = model("This sentence has, bads grammar mistake!")[0]["generated_text"]
    st.markdown("## "+output_text)
if button:
    with st.spinner('In progress.......'):
        if textbox:
            output_text = model(textbox)[0]["generated_text"]
        else:
            output_text = " "
    st.markdown("## "+output_text)