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# Imports
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/grammar_error_correcter_v1")
# Use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Grammar correction function
def correct_grammar(text):
# Tokenize input text
inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True).to(device)
# Generate corrected text
outputs = model.generate(**inputs, max_length=512, num_beams=5, early_stopping=True)
# Decode the output and return the corrected text
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return corrected_text
# Gradio interface function
def correct_grammar_interface(text):
corrected_text = correct_grammar(text)
return corrected_text
# Gradio app interface
with gr.Blocks() as grammar_app:
gr.Markdown("<h1>Grammar Correction App</h1>")
with gr.Row():
input_box = gr.Textbox(label="Input Text", placeholder="Enter text to be corrected", lines=4)
output_box = gr.Textbox(label="Corrected Text", placeholder="Corrected text will appear here", lines=4)
submit_button = gr.Button("Correct Grammar")
# Bind the button click to the grammar correction function
submit_button.click(fn=correct_grammar_interface, inputs=input_box, outputs=output_box)
# Launch the app
if __name__ == "__main__":
grammar_app.launch()
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