import os
import base64
import tempfile
from io import BytesIO
import torch
import gradio as gr
from PIL import Image
from PyPDF2 import PdfReader
from ebooklib import epub
from pdf2image import convert_from_path
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
# Set cache and log paths
cache_dir = "/tmp/huggingface_cache"
os.environ["HF_HOME"] = cache_dir
os.environ["TORCH_HOME"] = cache_dir
os.makedirs(cache_dir, exist_ok=True)
# Patch logging to avoid permission errors
import logging
from logging import FileHandler
class SafeFileHandler(FileHandler):
def __init__(self, filename, mode='a', encoding=None, delay=False, errors=None):
# Redirect all logs to tmp
safe_path = os.environ.get("OLMOCR_LOG_PATH", "/tmp/olmocr-pipeline-debug.log")
super().__init__(safe_path, mode, encoding, delay, errors)
logging.FileHandler = SafeFileHandler
# Now import olmocr
from olmocr.run_ocr import ocr_pdf_to_text
from olmocr.prompts import build_finetuning_prompt
from olmocr.prompts.anchor import get_anchor_text
from olmocr.data.renderpdf import render_pdf_to_base64png
# Load model and processor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Qwen2VLForConditionalGeneration.from_pretrained(
"allenai/olmOCR-7B-0225-preview",
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
).eval().to(device)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
def ocr_page(pdf_path, page_num):
image_b64 = render_pdf_to_base64png(pdf_path, page_num + 1, target_longest_image_dim=1024)
anchor_text = get_anchor_text(pdf_path, page_num + 1, pdf_engine="pdfreport", target_length=4000)
prompt = build_finetuning_prompt(anchor_text)
messages = [{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}}
],
}]
prompt_text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
main_image = Image.open(BytesIO(base64.b64decode(image_b64)))
inputs = processor(text=[prompt_text], images=[main_image], return_tensors="pt", padding=True)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
**inputs,
temperature=0.8,
max_new_tokens=1024,
do_sample=True,
)
prompt_len = inputs["input_ids"].shape[1]
new_tokens = outputs[:, prompt_len:]
decoded = processor.tokenizer.batch_decode(new_tokens, skip_special_tokens=True)
return decoded[0] if decoded else ""
def create_epub_from_text(text, output_path, title, author, language, cover_image):
book = epub.EpubBook()
book.set_title(title)
book.set_language(language)
book.add_author(author)
with open(cover_image, "rb") as cover_file:
cover_data = cover_file.read()
cover_item = epub.EpubItem(uid="cover", file_name="cover.jpg", media_type="image/jpeg", content=cover_data)
book.add_item(cover_item)
chapter = epub.EpubHtml(title="Content", file_name="content.xhtml", lang=language)
chapter.set_content(f"{title}
{text}
")
book.add_item(chapter)
book.toc = (epub.Link("content.xhtml", "Content", "content"),)
book.add_item(epub.EpubNav())
epub.write_epub(output_path, book)
def convert_pdf_to_epub(pdf_file, title, author, language):
tmp_pdf_path = pdf_file.name
reader = PdfReader(tmp_pdf_path)
cover_path = "/tmp/cover.jpg"
images = convert_from_path(tmp_pdf_path, first_page=1, last_page=1)
images[0].save(cover_path, "JPEG")
# Use official AllenAI OCR function
ocr_text = ocr_pdf_to_text(
pdf_path=tmp_pdf_path,
model=model,
processor=processor
)
epub_path = "/tmp/output.epub"
create_epub_from_text(
text=ocr_text,
output_path=epub_path,
title=title,
author=author,
language=language,
cover_image=cover_path
)
return epub_path, cover_path
def interface_fn(pdf, title, author, language):
epub_path, _ = convert_pdf_to_epub(pdf, title, author, language)
return epub_path
demo = gr.Interface(
fn=interface_fn,
inputs=[
gr.File(label="Upload PDF", file_types=[".pdf"]),
gr.Textbox(label="EPUB Title", placeholder="e.g. Understanding AI"),
gr.Textbox(label="Author", placeholder="e.g. Allen AI"),
gr.Textbox(label="Language", placeholder="e.g. en", value="en"),
],
outputs=gr.File(label="Download EPUB"),
title="PDF to EPUB Converter (olmOCR)",
description="Upload a PDF to convert it into a structured EPUB. The first page is used as the cover. OCR is performed with the olmOCR model.",
allow_flagging="never",
)
if __name__ == "__main__":
demo.launch(share=True)