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import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
# Set up the Streamlit app
st.title("Correct your Grammar with Transformers")
st.write("")
st.write("Input your text here!")
# Create input text area
default_value = "Mike and Anna is skiing"
sent = st.text_area("Text", default_value, height=50)
# Create "Check Now" button
if st.button("Check Now"):
# Run Model
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device)
def correct_grammar(input_text, num_return_sequences=1):
batch = tokenizer([input_text], truncation=True, padding='max_length', max_length=len(input_text), return_tensors="pt").to(torch_device)
results = model.generate(**batch, max_length=len(input_text), num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
return results
# Prompts
results = correct_grammar(sent, num_return_sequences=1)
# Decode results
generated_sequences = []
for generated_sequence_idx, generated_sequence in enumerate(results):
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
generated_sequences.append(text)
# Check correctness
is_correct = sent == generated_sequences[0]
# Display correctness result
if is_correct:
st.write("Result: ", generated_sequences[0], " (Correct)", key="result_text", unsafe_allow_html=True)
else:
st.write("Result: ", generated_sequences[0], " (Wrong)", key="result_text", unsafe_allow_html=True)
# Display correct grammar sentence in a box
st.text("Correct Grammar Sentence:")
st.code(generated_sequences[0])
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