standard pharmacology examination answer writing drug classification adverse effects contraindications authoritative sources

~/

~/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107

mkdir -p "/tmp/workspace/pharmacology-paper2/src" "/tmp/workspace/pharmacology-paper2/output" && python -c "from pypdf import PdfReader; p='/tmp/workspace/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); print('PAGES',len(r.pages)); open('/tmp/workspace/pharmacology-paper2/src/extracted.txt','w',encoding='utf-8').write('\\n\\n'.join('--- PAGE %d ---\\n%s' % (i+1,(page.extract_text() or '')) for i,page in enumerate(r.pages)))"

python -c "import fitz; from PIL import Image,ImageDraw; p='/tmp/workspace/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; d=fitz.open(p); imgs=[]; [imgs.append(Image.frombytes('RGB' if pg.get_pixmap(matrix=fitz.Matrix(0.5,0.5),alpha=False).n==3 else 'L',[pg.get_pixmap(matrix=fitz.Matrix(0.5,0.5),alpha=False).width,pg.get_pixmap(matrix=fitz.Matrix(0.5,0.5),alpha=False).height],pg.get_pixmap(matrix=fitz.Matrix(0.5,0.5),alpha=False).samples).convert('RGB')) for pg in d]; W=800; thumbs=[]; [(im.thumbnail((W//4,350)),thumbs.append(im.copy())) for im in imgs]; out=Image.new('RGB',(W,((len(thumbs)+3)//4)*390),'white'); dr=ImageDraw.Draw(out); [(out.paste(im,((i%4)*(W//4),(i//4)*390+25)),dr.text(((i%4)*(W//4)+3,(i//4)*390+3),str(i+1),fill='black')) for i,im in enumerate(thumbs)]; out.save('/tmp/workspace/pharmacology-paper2/src/contact.jpg')"

python -c "from pypdf import PdfReader; import os; p='/tmp/workspace/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); os.makedirs('/tmp/workspace/pharmacology-paper2/src/pages',exist_ok=True); print([(i+1,len(list(pg.images))) for i,pg in enumerate(r.pages)]); [open(f'/tmp/workspace/pharmacology-paper2/src/pages/page_{i+1}_{j+1}.{im.name.split(".")[-1]}','wb').write(im.data) for i,pg in enumerate(r.pages) for j,im in enumerate(pg.images)]"

python -c "from pypdf import PdfReader; import os; p='/tmp/workspace/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); os.makedirs('/tmp/workspace/pharmacology-paper2/src/pages',exist_ok=True); print([(i+1,len(list(pg.images))) for i,pg in enumerate(r.pages)]); [open('/tmp/workspace/pharmacology-paper2/src/pages/page_%d_%d.jpg'%(i+1,j+1),'wb').write(im.data) for i,pg in enumerate(r.pages) for j,im in enumerate(pg.images)]"

python -c "from PIL import Image,ImageDraw; import os; base='/tmp/workspace/pharmacology-paper2/src/pages'; ims=[Image.open(base+'/page_%d_1.jpg'%i).convert('RGB') for i in range(1,32)]; cellw,cellh=300,430; out=Image.new('RGB',(cellw*4,cellh*8),'white'); d=ImageDraw.Draw(out); [(im.thumbnail((cellw-12,cellh-35)),out.paste(im,((idx%4)*cellw+6,(idx//4)*cellh+28)),d.text(((idx%4)*cellw+8,(idx//4)*cellh+6),'PAGE %d'%(idx+1),fill='black')) for idx,im in enumerate(ims)]; out.save('/tmp/workspace/pharmacology-paper2/src/contact.jpg')"

OCR/extract every question verbatim from this scanned PDF: /tmp/workspace/attachments/5822e1cc-c8ad-4050-8f14-b53628fd5107/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf. Pages were extracted as JPGs in /tmp/workspace/pharmacology-paper2/src/pages. Use any available visual/OCR techniques. Return a structured, page-by-page list of all questions, headings, marks, and only readable content. Do not answer them.

~/pharmacology-paper2/src/pages

python - <<'PY' from PIL import Image from pathlib import Path p=Path('/tmp/workspace/pharmacology-paper2/src/pages') for f in sorted(p.glob('*.jpg'), key=lambda x:int(x.stem.split('_')[1])): im=Image.open(f) print(f.name, im.size) PY

mkdir -p "/tmp/workspace/pharmacology-paper2/ocr" && for f in /tmp/workspace/pharmacology-paper2/src/pages/*.jpg; do b=$(basename "$f" .jpg); tesseract "$f" "/tmp/workspace/pharmacology-paper2/ocr/$b" -l eng --psm 6 2>/dev/null; done && python - <<'PY' from pathlib import Path for f in sorted(Path('/tmp/workspace/pharmacology-paper2/ocr').glob('*.txt'),key=lambda x:int(x.stem.split('_')[1])): print('\n-----',f.stem,'-----') print(f.read_text()[:7000]) PY

python - <<'PY' try: import easyocr; print('easyocr yes') except Exception as e: print('easyocr no',e) try: from paddleocr import PaddleOCR; print('paddle yes') except Exception as e: print('paddle no',e) try: import cv2; print('cv2 yes') except Exception as e: print('cv2 no') PY; which tesseract || true; which ocrmypdf || true

python -c "import importlib.util as u; print('easyocr',bool(u.find_spec('easyocr'))); print('paddleocr',bool(u.find_spec('paddleocr'))); print('cv2',bool(u.find_spec('cv2')))"; command -v tesseract || true; command -v ocrmypdf || true

python -c "import importlib.util as u; print(bool(u.find_spec('pytesseract'))); print(bool(u.find_spec('keras_ocr'))); print(bool(u.find_spec('transformers'))); print(bool(u.find_spec('torch')))"; python -m pip install --quiet rapidocr-onnxruntime

python - <<'PY' from rapidocr_onnxruntime import RapidOCR from pathlib import Path ocr=RapidOCR() src=Path('/tmp/workspace/pharmacology-paper2/src/pages'); out=Path('/tmp/workspace/pharmacology-paper2/ocr'); out.mkdir(exist_ok=True) for f in sorted(src.glob('*.jpg'), key=lambda x:int(x.stem.split('_')[1])): r,el=ocr(str(f)) lines=[] if not r else [z[1] for z in r] (out/(f.stem+'.txt')).write_text('\n'.join(lines)) print(f.name, len(lines),el) PY

python - <<'PY' from pathlib import Path p=Path('/tmp/workspace/pharmacology-paper2/ocr') for f in sorted(p.glob('*.txt'),key=lambda x:int(x.stem.split('_')[1])): print('\n\n===== PAGE',f.stem.split('_')[1],'=====') print(f.read_text()) PY

Which unit should I answer first?

pharmacology education AND systematic review

WHO consolidated guidelines tuberculosis treatment drug susceptible 2025 malaria HIV post exposure prophylaxis

Create a polished, concise but complete MBBS Pharmacology Paper II answer bank DOCX for EVERY unique question readable from the uploaded past-paper PDF. Source question inventory is in OCR report described by pages 1-31, and scanned page images are in /tmp/workspace/pharmacology-paper2/src/pages. Output must be /tmp/workspace/pharmacology-paper2/output/Pharmacology_Paper_II_Complete_Answer_Bank.docx. Use python-docx. Cover all unique long answers, short notes, cases, comparisons, lists, fill blanks, MCQs found on pages 1-31. Deduplicate repeats. Answers should be exam style with headings, classifications, MOA, indications, ADRs, contraindications, core rationales, and concise current caution where protocols differ by national program. Use current standard pharmacology knowledge, never invent unreadable questions; state '[not legible in scan]' only if necessary. Include a contents map and page breaks between units. Make the document readable and compact (about 40-65 pages preferred). Return exact file path plus a short inventory of covered topics.

~/pharmacology-paper2

~/pharmacology-paper2/src

**/* in ~/pharmacology-paper2/src

/tmp

/workspace

/tmp/workspace

~/attachments

**/*pharmacology* in /tmp/workspace

**/* in ~/attachments

**/*.pdf in /tmp

Please upload the past-paper PDF/OCR report (or provide the correct accessible directory) so I can extract the complete question inventory.