app.py

import os
import gradio as gr
import main
import shutil


def predict_from_pdf(pdf_file):
    # Create a temporary directory for file uploads
    upload_dir = "./catalogue/"
    os.makedirs(upload_dir, exist_ok=True)

    # Use the provided file path from Gradio's file object
    dest_file_path = os.path.join(upload_dir, os.path.basename(pdf_file.name))

    try:
        # Save the uploaded file using shutil.copy
        shutil.copy(pdf_file, dest_file_path)

        # Check if the file was saved successfully
        if not os.path.exists(dest_file_path):
            return None, f"Error: The file {dest_file_path} could not be found or opened."

        # Process the PDF and retrieve the product info
        df, response = main.process_pdf_catalog(dest_file_path)
        return df, response

    except Exception as e:
        return None, f"Error processing PDF: {str(e)}"


# Define example PDFs
pdf_examples = [
    ["catalogue/flexpocket.pdf"],
    ["catalogue/ASICS_Catalog.pdf"],
]

demo = gr.Interface(
    fn=predict_from_pdf,
    inputs=gr.File(label="Upload PDF Catalog"),
    outputs=["json", "text"],
    examples=pdf_examples,
    title="Open Source PDF Catalog Parser",
    description="Efficient PDF catalog processing using fitz and OpenLLM",
    article="Uses PyMuPDF for layout analysis and Llama-CPP for structured extraction"
)

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True)