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Give flashcards and tricks linking questions to correct answers to learn easily

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Running Command

cd /home/daytona/workspace/attachments/f7d44c29-72c7-4b82-959b-fa6bf15349fb/ && python3 -c " import pdfplumber with pdfplumber.open('Pharmacology Mid-2 with ans_250429_152045_250501_150153.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'--- PAGE {i+1} ---') print(text) print() "

The PDF appears to be image-based (scanned). Let me use OCR to extract the content.
Running Command

cd /home/daytona/workspace/attachments/f7d44c29-72c7-4b82-959b-fa6bf15349fb/ && python3 -c " from pypdf import PdfReader import sys reader = PdfReader('Pharmacology Mid-2 with ans_250429_152045_250501_150153.pdf') print(f'Pages: {len(reader.pages)}') # Try raw extraction for i in range(min(3, len(reader.pages))): page = reader.pages[i] print(f'Page {i+1} raw:', repr(page.extract_text()[:200])) # Check for images if '/XObject' in page['/Resources']: xobjects = page['/Resources']['/XObject'] print(f' XObjects: {list(xobjects.keys())}') "

Confirmed — it's a scanned image PDF. Let me use OCR (pytesseract + pdf2image) to extract text from all pages.
Running Command

python3 -c "import pytesseract; import pdf2image; print('both available')"

Running Command

python3 -c " import pdf2image import pytesseract from pathlib import Path pdf_path = '/home/daytona/workspace/attachments/f7d44c29-72c7-4b82-959b-fa6bf15349fb/Pharmacology Mid-2 with ans_250429_152045_250501_150153.pdf' print('Converting pages...') pages = pdf2image.convert_from_path(pdf_path, dpi=200, first_page=1, last_page=10) print(f'Got {len(pages)} pages') all_text = [] for i, img in enumerate(pages): text = pytesseract.image_to_string(img) all_text.append(f'=== PAGE {i+1} ===\n{text}') print(f'Page {i+1} chars: {len(text)}') full = '\n'.join(all_text) print(full[:5000]) " 2>&1 | head -200

OCR is working well. Now let me extract all 64 pages.
Running Command

python3 -c " import pdf2image import pytesseract pdf_path = '/home/daytona/workspace/attachments/f7d44c29-72c7-4b82-959b-fa6bf15349fb/Pharmacology Mid-2 with ans_250429_152045_250501_150153.pdf' pages = pdf2image.convert_from_path(pdf_path, dpi=200) print(f'Total pages: {len(pages)}') all_text = [] for i, img in enumerate(pages): text = pytesseract.image_to_string(img) all_text.append(f'=== PAGE {i+1} ===\n{text}') full = '\n'.join(all_text) with open('/home/daytona/workspace/pharm_ocr.txt', 'w') as f: f.write(full) print('Done. Total chars:', len(full)) " 2>&1

Memory issue with all 64 pages at once. Let me process in batches.
Running Command

python3 << 'EOF' import pdf2image import pytesseract pdf_path = '/home/daytona/workspace/attachments/f7d44c29-72c7-4b82-959b-fa6bf15349fb/Pharmacology Mid-2 with ans_250429_152045_250501_150153.pdf' all_text = [] batch_size = 8 total_pages = 64 for start in range(1, total_pages + 1, batch_size): end = min(start + batch_size - 1, total_pages) pages = pdf2image.convert_from_path(pdf_path, dpi=180, first_page=start, last_page=end) for j, img in enumerate(pages): page_num = start + j text = pytesseract.image_to_string(img) all_text.append(f'=== PAGE {page_num} ===\n{text}') print(f'Done pages {start}-{end}') full = '\n'.join(all_text) with open('/home/daytona/workspace/pharm_ocr.txt', 'w') as f: f.write(full) print(f'Complete. Total chars: {len(full)}') EOF

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I now have a very good picture of the content. Let me also check the answers section (which should be toward the end) and the remaining topics (hematology, etc.).
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