app.py

import gradio as gr
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load T5 model and tokenizer
model_name = "t5-base"  # Use a smaller model for faster inference
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Grammar correction function
def correct_grammar(text):
    input_text = f"correct: {text}"
    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
    
    # Generate corrected text
    output_ids = model.generate(input_ids, max_length=512, num_beams=5, early_stopping=True)
    corrected_text = tokenizer.decode(output_ids[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 interface
with gr.Blocks() as grammar_app:
    gr.Markdown("<h1>Fast Grammar Correction with T5</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")
    
    # Button click event
    submit_button.click(fn=correct_grammar_interface, inputs=input_box, outputs=output_box)

# Launch the app
if __name__ == "__main__":
    grammar_app.launch()