import gradio as gr
import pdf_to_image
import image_to_text
from ml_engine.model_functions import is_it_title

def process_pdf(pdf):
    # Ensure we get the correct path to the uploaded file
    pdf_path = pdf.name  # `pdf` is now a NamedString/TempFile with a `.name` attribute

    pdf_pages_images = pdf_to_image.pdfToImg2(pdf_path)

    pages = []
    curr_pg = ""

    for img in pdf_pages_images:
        text = image_to_text.img2string(img)
        for line in text.split("\n"):
            if(len(line) == 0): continue
            if(is_it_title(line)):
                # print(f"TITLE FOUND: {line}") #Debug statement
                if(len(curr_pg) != 0):
                    pages.append(curr_pg)
                    curr_pg = ""
                
            curr_pg = (curr_pg + line + "\n")
    pages.append(curr_pg)
    # print(pages)
    return pages  # Returning a list of strings

# Gradio interface using latest syntax
with gr.Blocks() as demo:
    gr.Markdown("# PDF to Pages Processor")
    gr.Markdown("Upload a PDF and get a list of extracted pages as output.")

    # pdf_input = gr.File(label="Upload a PDF", file_types=[".pdf"])
    pdf_input = gr.File(label="Upload a PDF")
    output = gr.JSON(label="Extracted Pages")

    submit_button = gr.Button("Process PDF")

    # Define interaction
    submit_button.click(fn=process_pdf, inputs=pdf_input, outputs=output)

if __name__ == "__main__":
    demo.launch()