app.py

import streamlit as st
import transformers
import tensorflow


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("Writing Assistant for you 🦄")

textbox = st.text_area('Write your text:', '', height=200, max_chars=1000)

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

if button:
    output_text = model(textbox)[0]
 
    for i, line in enumerate(output_text.split("\n")):
        if ":" in line:
            speaker, speech = line.split(':')
            st.markdown(f'__{speaker}__: {speech}')
        else:
            st.markdown(line)

#inputs = tokenizer("Grammar: "+sent,return_tensors="tf")

#output_sequences = infer(inputs)

#generated_sequences = tokenizer.decode(output_ids)

#st.write(generated_sequences)