app.py

import json
import gradio as gr
from pdfminer.high_level import extract_pages, extract_text
from pdfminer.layout import LTTextBoxHorizontal, LTFigure, LTImage
import os  # Import os for file path manipulation

def parse_pdf(pdf_file, output_format):
    with open(pdf_file, 'rb') as file:
        pages = extract_pages(file)

        text = ""
        tables = []  # Placeholder for extracted table data
        images = []  # List to store extracted image data

        for page in pages:
            for element in page:
                if isinstance(element, LTTextBoxHorizontal):
                    text += element.get_text()
                elif isinstance(element, (LTFigure, LTImage)):
                    # Extract image data (e.g., save as image, convert to base64)
                    # ... (Implement image processing logic)
                    # Here's an example of extracting image data and saving the image
                    if hasattr(element, 'stream'):  # Check for image data stream (LTImage)
                        image_data = element.stream.read()
                    else:  # Handle LTFigure (may require additional processing)
                        # ... (Implement logic to extract image data from LTFigure)
                        # You might need libraries like Pillow for image manipulation
                        image_data = b"Placeholder for extracted image data"  # Example placeholder

                    image_filename = f"extracted_image_{len(images)}.jpg"
                    with open(image_filename, 'wb') as image_file:
                        image_file.write(image_data)
                    images.append({"filename": image_filename})  # Add filename to image data

        # Implement table extraction logic (e.g., using heuristics or advanced techniques)
        # You can use libraries like Camelot for complex tables
        # ...

    # Convert extracted data to desired format and populate download_data
    if output_format == "JSON":
        json_data = {
            "text": text,
            "tables": tables,  # Replace with actual table data
            "images": images  # List of dictionaries with filenames
        }
        download_data = json.dumps(json_data).encode("utf-8")  # Encode JSON for download

    elif output_format == "Markdown":
        # Implement table conversion using mistletoe or other Markdown libraries (uncomment if needed)
        # markdown_tables = mistletoe.markdown(convert_table=True)(tables)  # Example using mistletoe

        markdown_text = f"# Extracted Text\n\n{text}\n\n# Images\n"
        # Implement logic to embed images within Markdown (optional)
        # ... (e.g., use relative paths if images are saved locally)
        #  or (consider alternative Markdown image embedding methods)
        download_data = markdown_text.encode("utf-8")

    elif output_format == "HTML":
        # Implement table conversion using HTML table tags
        html_tables = "<table>"  # Start of HTML table (replace with actual table structure)
        # ... (Implement table data conversion to HTML)
        # html_tables += "</table>"

        html_text = f"<p>{text}</p>\n\n<h2>Tables</h2>\n{html_tables}\n\n<h2>Images</h2>\n"
        # Implement logic to display images within HTML (optional)
        # ... (e.g., use `<img>` tags with image source)
        download_data = html_text.encode("utf-8")

    # Create a temporary directory to store downloaded files (optional)
    # download_dir = tempfile.mkdtemp()  # Uncomment if needed for temporary storage

    # Return the extracted text and the filename (or path) for download
    return text, os.path.join(os.getcwd(), images[0]["filename"])  # Example using first image

iface = gr.Interface(
    fn=parse_pdf,
    inputs=["file", gr.Dropdown(["JSON", "Markdown", "HTML"])],
    outputs=[
        gr.Text(label="Output Text"),
        gr.File(label="Download Output")
    ],
    title="PDF Parser",
    description="Parse a PDF and choose the output format."
)

if __name__ == "__main__":