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Update app.py
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app.py
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@@ -11,17 +11,19 @@ from ebooklib import epub
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from pdf2image import convert_from_path
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from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
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from olmocr.prompts import build_finetuning_prompt
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from olmocr.prompts.anchor import get_anchor_text
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from olmocr.pipeline import PDFToTextOCR # ✅ Import the OCR pipeline
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# Set Hugging Face and Torch cache to a guaranteed-writable location
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cache_dir = "/tmp/huggingface_cache"
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os.environ["HF_HOME"] = cache_dir
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os.environ["TORCH_HOME"] = cache_dir
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os.makedirs(cache_dir, exist_ok=True)
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# Load model and processor
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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@@ -30,8 +32,8 @@ model = Qwen2VLForConditionalGeneration.from_pretrained(
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).eval().to(device)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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#
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def ocr_page(pdf_path, page_num):
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image_b64 = render_pdf_to_base64png(pdf_path, page_num + 1, target_longest_image_dim=1024)
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@@ -95,17 +97,17 @@ def create_epub_from_text(text, output_path, title, author, language, cover_imag
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def convert_pdf_to_epub(pdf_file, title, author, language):
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tmp_pdf_path = pdf_file.name
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# Read
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reader = PdfReader(tmp_pdf_path)
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cover_path = "/tmp/cover.jpg"
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images = convert_from_path(tmp_pdf_path, first_page=1, last_page=1)
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images[0].save(cover_path, "JPEG")
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# Run OCR
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ocr_text = "\n\n".join([page.text for page in ocr_result.pages])
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#
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epub_path = "/tmp/output.epub"
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create_epub_from_text(
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text=ocr_text,
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from pdf2image import convert_from_path
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from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
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# Set cache and log paths
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cache_dir = "/tmp/huggingface_cache"
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os.environ["HF_HOME"] = cache_dir
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os.environ["TORCH_HOME"] = cache_dir
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os.environ["OLMOCR_LOG_PATH"] = "/tmp/olmocr-pipeline-debug.log"
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os.makedirs(cache_dir, exist_ok=True)
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# Import olmocr pipeline after setting log path
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from olmocr.pipeline import PDFToTextOCR
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from olmocr.data.renderpdf import render_pdf_to_base64png
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from olmocr.prompts import build_finetuning_prompt
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from olmocr.prompts.anchor import get_anchor_text
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# Load model and processor
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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).eval().to(device)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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# Load OCR pipeline
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olmocr = PDFToTextOCR(model=model, processor=processor)
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def ocr_page(pdf_path, page_num):
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image_b64 = render_pdf_to_base64png(pdf_path, page_num + 1, target_longest_image_dim=1024)
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def convert_pdf_to_epub(pdf_file, title, author, language):
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tmp_pdf_path = pdf_file.name
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# Read PDF to get cover
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reader = PdfReader(tmp_pdf_path)
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first_page = reader.pages[0]
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cover_path = "/tmp/cover.jpg"
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images = convert_from_path(tmp_pdf_path, first_page=1, last_page=1)
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images[0].save(cover_path, "JPEG")
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# Run OCR
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ocr_text = olmocr.process(tmp_pdf_path)
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# Write EPUB
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epub_path = "/tmp/output.epub"
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create_epub_from_text(
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text=ocr_text,
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