feat: PDF text extraction and per-page classification#4
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Use pypdfium2 (permissively licensed, ships its own native library) so there is no system poppler dependency. Add fpdf2 as a dev dependency for test fixtures and a mypy override for the stub-less binding.
Add the Pdf handle (context-managed pypdfium2 document) and classify_pdf, which pulls each page's embedded text layer and runs it through the page classifier.
Build PDFs with fpdf2 and assert text extraction, page iteration, digital text pages, and a blank page classified as scanned.
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Wires the page classifier to real PDFs: pull each page's embedded text layer and classify it.
What
Pdf— a context-managed handle over a pypdfium2 document (len,page_text,page_texts), releasing native resources on exit.classify_pdf(path)— extracts every page's text and returns a per-pageClassification.Why pypdfium2
Permissively licensed and ships its own native PDFium library, so users install no system poppler. It also renders pages, which the OCR-fallback step will reuse.
Tests
Fixtures are generated in-process with
fpdf2(dev dependency): text extraction, multi-page iteration, text pages classifiedDIGITAL, and a blank page classifiedSCANNED.Notes
Verified locally: ruff, ruff format, mypy (strict), pytest (15) all pass.