~/

K D Tripathi Essentials of Medical Pharmacology latest edition official publisher pharmacology

~/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536

pharmacology education AND examination

mkdir -p "/tmp/workspace/pharm-paper2/src" "/tmp/workspace/pharm-paper2/output" && python -c "from pypdf import PdfReader; p='/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); print('PAGES',len(r.pages)); open('/tmp/workspace/pharm-paper2/src/extracted.txt','w',encoding='utf-8').write('\\n\\n'.join(f'--- PAGE {i+1} ---\\n'+(pg.extract_text() or '') for i,pg in enumerate(r.pages)))"

python -c "import fitz; p='/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; d=fitz.open(p); print([(i+1, round(pg.rect.width), round(pg.rect.height), len(pg.get_images(full=True))) for i,pg in enumerate(d)])"

python -c "import importlib.util; print('pypdfium2',importlib.util.find_spec('pypdfium2')); print('pdf2image',importlib.util.find_spec('pdf2image')); print('PIL',importlib.util.find_spec('PIL')); print('easyocr',importlib.util.find_spec('easyocr')); print('rapidocr',importlib.util.find_spec('rapidocr_onnxruntime'))"

python -c "import pypdfium2 as pdfium, os; p='/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; d=pdfium.PdfDocument(p); out='/tmp/workspace/pharm-paper2/src/pages'; os.makedirs(out,exist_ok=True); [page.render(scale=2.2).to_pil().save(f'{out}/page_{i+1:02d}.png') for i,page in enumerate(d)]; print(len(d))"

python -c "from PIL import Image,ImageDraw; import os,math; folder='/tmp/workspace/pharm-paper2/src/pages'; files=[f'{folder}/page_{i:02d}.png' for i in range(1,32)]; out='/tmp/workspace/pharm-paper2/src/contact'; os.makedirs(out,exist_ok=True); [ (lambda batch,k: (lambda ims: (lambda sheet: ([(sheet.paste(im,((j%2)*600,(j//2)*850+35)) , ImageDraw.Draw(sheet).text(((j%2)*600+10,(j//2)*850+8),f'Page {k*4+j+1}',fill='black')) for j,im in enumerate(ims)],sheet.save(f'{out}/sheet_{k+1}.jpg'))[1])(Image.new('RGB',(1200,1700),'white')))([Image.open(x).convert('RGB').resize((600,815)) for x in batch]) )(files[k*4:k*4+4],k) for k in range(math.ceil(len(files)/4)) ]; print('done')"

python -c "import importlib.util;print(importlib.util.find_spec('pytesseract'))"; tesseract --version 2>&1 | sed -n '1p'

Extract every question exactly from the uploaded scanned PDF at /tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf. It has 31 image-only pages. Use PDF/image processing or OCR as needed. Return a complete numbered list grouped by page/year, preserving wording and mark allocations. Do not answer questions.

~/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536

/tmp/skills/pdf

python -c "from pypdf import PdfReader; p='/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); print(len(r.pages)); print([len((x.extract_text() or '')) for x in r.pages])" && python -c "import importlib.util; print('fitz',importlib.util.find_spec('fitz')); print('pytesseract',importlib.util.find_spec('pytesseract')); print('easyocr',importlib.util.find_spec('easyocr')); print('PIL',importlib.util.find_spec('PIL'))"

/tmp/workspace

~/pharm-paper2

which tesseract || true; which mutool || true; which pdftoppm || true; python -c "import importlib.util; print('pymupdf',importlib.util.find_spec('pymupdf')); print('ocrmypdf',importlib.util.find_spec('ocrmypdf')); print('cv2',importlib.util.find_spec('cv2'))"; python -c "from pypdf import PdfReader; p='/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf'; r=PdfReader(p); print(r.pages[0].images)"

mkdir -p "/tmp/workspace/pharm-paper2/output/pages" && pdftoppm -r 250 -png "/tmp/workspace/attachments/22e62e53-ebd2-47d1-87fd-6183d4763536/pharmacology paper 2 previous year according to our paper 2 syllabus.pdf" "/tmp/workspace/pharm-paper2/output/pages/page" >/dev/null 2>&1 && python -c "import importlib.util; print('keras_ocr',importlib.util.find_spec('keras_ocr')); print('paddleocr',importlib.util.find_spec('paddleocr')); print('transformers',importlib.util.find_spec('transformers')); print('torch',importlib.util.find_spec('torch'))"; ls -lh "/tmp/workspace/pharm-paper2/output/pages" | sed -n '1,8p'

python - <<'PY' from PIL import Image,ImageDraw from pathlib import Path p=Path('/tmp/workspace/pharm-paper2/output/pages'); out=Path('/tmp/workspace/pharm-paper2/output/sheets'); out.mkdir(exist_ok=True) fs=sorted(p.glob('*.png')) for b in range(0,len(fs),4): ims=[] for f in fs[b:b+4]: im=Image.open(f).convert('RGB'); im.thumbnail((850,1200)); ims.append((f,im.copy())) W=1700; H=max(im.height for _,im in ims)*2+80 sh=Image.new('RGB',(W,H),'white');d=ImageDraw.Draw(sh) for k,(f,im) in enumerate(ims): x=(k%2)*850;y=(k//2)*(H//2)+30 sh.paste(im,(x,y)); d.text((x+10,y-25),f.name,fill='black',stroke_width=0) sh.save(out/f'sheet-{b//4+1:02d}.jpg',quality=92) PY ls -lh "/tmp/workspace/pharm-paper2/output/sheets"

python - <<'PY' from PIL import Image from pathlib import Path src=Path('/tmp/workspace/pharm-paper2/output/sheets');out=Path('/tmp/workspace/pharm-paper2/output/sheets-small');out.mkdir(exist_ok=True) for f in src.glob('*.jpg'): im=Image.open(f);im.thumbnail((1200,1500));im.save(out/f.name,quality=55,optimize=True) PY ls -lh "/tmp/workspace/pharm-paper2/output/sheets-small"

python -m pip install -q rapidocr_onnxruntime && python -c "from rapidocr_onnxruntime import RapidOCR; print('ready')"

python - <<'PY' from rapidocr_onnxruntime import RapidOCR from pathlib import Path ocr=RapidOCR() src=Path('/tmp/workspace/pharm-paper2/output/pages'); lines=[] for f in sorted(src.glob('*.png')): r,_=ocr(str(f)) lines.append('\n\n===== '+f.name+' =====\n') if r: for box,text,score in r: lines.append(text) Path('/tmp/workspace/pharm-paper2/output/ocr.txt').write_text('\n'.join(lines)) PY