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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]) | |