import gradio as gr
import torch
from transformers import AutoModelForObjectDetection, TableTransformerForObjectDetection
from PIL import Image
from pdf_processing import process_pdf

# Load model and process the PDF
def process_pdf_file(pdf_file):
    pdf_path = pdf_file.name  # Get the path to the uploaded PDF
    output_folder = "./output"  # Define the output folder
    
    # Call the existing function to process the PDF
    process_pdf(pdf_path, output_folder)
    
    return f"Processing completed. Output saved to {output_folder}"

# Create Gradio interface
def create_gradio_interface():
    input_pdf = gr.File(label="Upload PDF", type="filepath")  # Fixed argument here
    output_text = gr.Textbox(label="Processing Status", interactive=False)
    
    # Define the interface
    interface = gr.Interface(
        fn=process_pdf_file,
        inputs=input_pdf,
        outputs=output_text,
        live=True
    )
    return interface

# Start the application
if __name__ == "__main__":
    gradio_interface = create_gradio_interface()
    gradio_interface.launch()