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| 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" | |
| def load_model(): | |
| return pipeline("text2text-generation", model=model_checkpoint) | |
| model = load_model() | |
| st.subheader("당신의 영어 일기장을 위해! 🤖") | |
| st.markdown("최고의 성능으로 문법을 체크해 드립니다.!") | |
| textbox = st.text_area('Write your text in this box:', '',height=10, max_chars=300 ) | |
| button = st.button('Detect grammar mistakes:') | |
| # output | |
| st.subheader("Correct sentence: ") | |
| if button: | |
| with st.spinner('In progress.......'): | |
| if textbox: | |
| output_text = model(textbox)[0]["generated_text"] | |
| else: | |
| output_text = " " | |
| st.markdown("## "+output_text) | |