# 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 with an increased max_length for handling larger input
    inputs = tokenizer([text], return_tensors="pt", padding=True, truncation=True, max_length=1024).to(device)
    
    # Generate corrected text with increased max_length and num_beams
    outputs = model.generate(**inputs, max_length=1024, 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 (up to 300 words)</h1>")
    
    with gr.Row():
        input_box = gr.Textbox(label="Input Text", placeholder="Enter text (up to 300 words)", lines=10)
        output_box = gr.Textbox(label="Corrected Text", placeholder="Corrected text will appear here", lines=10)

    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()