app.py

from PyPDF2 import PdfReader
import gradio as gr
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat
from paddleocr import PPStructureV3
from pdf2image import convert_from_path
import numpy as np
import torch
from docling_core.types.doc import DoclingDocument
from docling_core.types.doc.document import DocTagsDocument
from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image
from pathlib import Path

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

pipeline_options = PdfPipelineOptions(enable_remote_services=True)
converter = DocumentConverter(
    format_options={
        InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    }
)

pipeline = PPStructureV3()

processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
model = AutoModelForVision2Seq.from_pretrained(
    "ds4sd/SmolDocling-256M-preview",
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager",
).to(DEVICE)

def get_pdf_page_count(pdf_path):
    reader = PdfReader(pdf_path)
    return len(reader.pages)

def get_page_image(pdf_path, page_num):
    images = convert_from_path(pdf_path, first_page=page_num, last_page=page_num)
    page_image = images[0]
    return page_image

def get_docling_ocr(pdf_path, page_num):
    result = converter.convert(pdf_path, page_range=(page_num, page_num))
    markdown_text_docling = result.document.export_to_markdown()
    return markdown_text_docling

def get_paddle_ocr(pdf_path, page_num):
    page_image = get_page_image(pdf_path, page_num)

    output = pipeline.predict(input=np.array(page_image))

    markdown_list = []

    for res in output:
        md_info = res.markdown
        markdown_list.append(md_info)

    markdown_text_paddleOCR = pipeline.concatenate_markdown_pages(markdown_list)
    return markdown_text_paddleOCR

def get_smoldocling_ocr(pdf_path, page_num):
    page_image = get_page_image(pdf_path, page_num)
    image = load_image(page_image)

    # Create input messages
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {"type": "text", "text": "Convert this page to docling."}
            ]
        },
    ]

    # Prepare inputs
    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[image], return_tensors="pt")
    inputs = inputs.to(DEVICE)

    generated_ids = model.generate(**inputs, max_new_tokens=8192)
    prompt_length = inputs.input_ids.shape[1]
    trimmed_generated_ids = generated_ids[:, prompt_length:]
    doctags = processor.batch_decode(
        trimmed_generated_ids,
        skip_special_tokens=False,
    )[0].lstrip()

    # Populate document
    doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctags], [image])
    doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")

    markdown_text_smoldocling = doc.export_to_markdown()
    return markdown_text_smoldocling

title = "OCR Arena"
description = "A simple Gradio interface to extract text from PDFs and compare OCR models"
examples = [["data/amazon-10-k-2024.pdf"],
            ["data/goog-10-k-2023.pdf"]]

with gr.Blocks(theme=gr.themes.Glass()) as demo:
    gr.Markdown(f"# {title}\n{description}")
    with gr.Row():
        with gr.Column():
            pdf = gr.File(label="Input PDFs", file_types=[".pdf"])

            @gr.render(inputs=pdf)
            def show_slider(pdf_path):
                if pdf_path is None:
                    page_num = gr.Markdown("## No Input Provided")
                else:
                    page_count = get_pdf_page_count(pdf_path)
                    page_num = gr.Slider(1, page_count, value=1, step=1, label="Page Number")

                    with gr.Row():
                        clear_btn = gr.ClearButton(components=[pdf, page_num])
                        submit_btn = gr.Button("Submit", variant='primary')

                    submit_btn.click(get_page_image, inputs=[pdf, page_num], outputs=original).then(    
                        get_docling_ocr, inputs=[pdf, page_num], outputs=docling_ocr_out).then(
                        get_paddle_ocr, inputs=[pdf, page_num], outputs=paddle_ocr_out).then(
                        get_smoldocling_ocr, inputs=[pdf, page_num], outputs=smoldocling_ocr_out)

        with gr.Column():
            original = gr.Image(width=640, height=640, label="Original Page", interactive=False)
            docling_ocr_out = gr.Textbox(label="Docling OCR Output", type="text", interactive=False)
            paddle_ocr_out = gr.Textbox(label="Paddle OCR Output", type="text", interactive=False)
            smoldocling_ocr_out = gr.Textbox(label="SmolDocling OCR Output", type="text", interactive=False)

    examples_obj = gr.Examples(examples=examples, inputs=[pdf])

demo.launch()