app.py

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

# Process PDF file and save text or data to file
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
    
    # Process the PDF file (assuming the function does the necessary work)
    processed_text = process_pdf(pdf_path, output_folder)  # Modify this based on your function's output
    
    # Save processed text to a text file
    txt_file_path = save_remaining_text_to_txt(processed_text, output_folder, 1)  # Assuming page_num = 1 for now
    
    # Alternatively, you could generate CSV or any other output
    data = []  # Replace with your data
    csv_file_path = save_to_csv(data, output_folder, 'processed_data.csv')
    
    # Return file paths as outputs for Gradio
    return txt_file_path, csv_file_path

# Create Gradio interface
def create_gradio_interface():
    input_pdf = gr.File(label="Upload PDF", type="filepath")  # Fixed argument here
    output_txt_file = gr.File(label="Download Processed Text", interactive=False)
    output_csv_file = gr.File(label="Download Processed CSV", interactive=False)
    
    # Define the interface
    interface = gr.Interface(
        fn=process_pdf_file,
        inputs=input_pdf,
        outputs=[output_txt_file, output_csv_file],
        live=True
    )
    return interface

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