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_data(allow_output_mutation=True) # @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("Writing Assistant for you 🤖") st.markdown("This writing assistant detects and corrects grammatical mistakes for you! This assitant uses **T5-base model ✍️** fine-tuned on jfleg dataset.") #image = Image.open('new_grammar.jpg') #st.image(image, caption='Image Credit: https://abrc.org.au/wp-content/uploads/2020/12/Grammar-checker.jpg') 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)