python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() if text: print(f'--- PAGE {i+1} ---') print(text[:3000]) "

python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf') as pdf: for i, page in enumerate(pdf.pages[:5]): text = page.extract_text() words = page.extract_words() print(f'--- PAGE {i+1} --- text_len={len(text) if text else 0}, words={len(words)}') if words: print([w[\"text\"] for w in words[:30]]) if text: print(text[:500]) "

MBBS practical table viva obstetrics gynaecology drugs instruments list

https://www.aliah.ac.in/upload/media/04-04-20_1586012291.pdf

python3 -c " from pypdf import PdfReader import base64, io reader = PdfReader('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf') print(f'Pages: {len(reader.pages)}') # Try extracting text from first few pages for i in range(min(10, len(reader.pages))): page = reader.pages[i] text = page.extract_text() if text and text.strip(): print(f'Page {i+1}: {text[:300]}') else: print(f'Page {i+1}: [no text - likely image]') "

python3 -c " from pypdf import PdfReader, PdfWriter import os # Extract individual pages as separate PDFs so we can analyse them reader = PdfReader('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf') print(f'Total pages: {len(reader.pages)}') # Save first 10 pages as individual PDFs for image conversion os.makedirs('/tmp/workspace/pdf_pages', exist_ok=True) for i in range(min(10, len(reader.pages))): writer = PdfWriter() writer.add_page(reader.pages[i]) with open(f'/tmp/workspace/pdf_pages/page_{i+1:03d}.pdf', 'wb') as f: writer.write(f) print('Done saving pages') "

python3 -c " # Convert PDF pages to PNG images using pypdf + Pillow from pypdf import PdfReader from PIL import Image import io, os # Check if pdf2image or fitz is available try: import fitz # PyMuPDF print('PyMuPDF available') doc = fitz.open('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf') print(f'Pages via fitz: {len(doc)}') # Render first page page = doc[0] mat = fitz.Matrix(2, 2) # 2x zoom pix = page.get_pixmap(matrix=mat) pix.save('/tmp/workspace/pdf_pages/page_001.png') print('Saved page 1 as PNG') except ImportError: print('PyMuPDF not available') try: from pdf2image import convert_from_path print('pdf2image available') except ImportError: print('pdf2image not available') "

python3 -c " from pdf2image import convert_from_path import os os.makedirs('/tmp/workspace/pdf_pages', exist_ok=True) # Convert first 8 pages to images images = convert_from_path('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf', dpi=150, first_page=1, last_page=8) for i, img in enumerate(images): img.save(f'/tmp/workspace/pdf_pages/page_{i+1:03d}.png', 'PNG') print(f'Saved page {i+1}: {img.size}') print('Done') " 2>&1 | head -30

python3 -c " from pdf2image import convert_from_path import os # Convert pages 9-20 to images images = convert_from_path('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf', dpi=150, first_page=9, last_page=20) for i, img in enumerate(images): pg = i + 9 img.save(f'/tmp/workspace/pdf_pages/page_{pg:03d}.png', 'PNG') print(f'Saved page {pg}') print('Done') " 2>&1 | head -20

I have a 128-page scanned PDF about drugs and instruments for OB/GYN MBBS table viva exams. The pages have been converted to PNG images saved at /tmp/workspace/pdf_pages/page_001.png through page_020.png. Your task: Read ALL the PNG image files in /tmp/workspace/pdf_pages/ and extract ALL text content from each page using the analyse_media tool. The images are at paths like /tmp/workspace/pdf_pages/page_001.png but to use analyse_media you need to convert them to base64 data URIs or serve them. Instead, use the execute tool to run a Python OCR script using pytesseract or easyocr to extract text from all 20 images. Try this approach: ```python import pytesseract from PIL import Image import os pages_dir = '/tmp/workspace/pdf_pages' all_text = {} for fname in sorted(os.listdir(pages_dir)): if fname.endswith('.png'): img = Image.open(os.path.join(pages_dir, fname)) text = pytesseract.image_to_string(img) all_text[fname] = text print(f"=== {fname} ===") print(text[:500]) ``` If pytesseract is not available, try easyocr: ```python import easyocr reader = easyocr.Reader(['en']) ``` Extract ALL text from ALL 20 pages. Then organize the content into: 1. List of all drugs mentioned with their uses 2. List of all instruments mentioned with their uses Return the complete organized text content - every drug name and instrument name you found, with their uses as described in the document.

~/pdf_pages

python3 -c "import pytesseract; print('pytesseract available')" 2>&1; python3 -c "from PIL import Image; print('PIL available')" 2>&1

python3 -c "import easyocr; print('easyocr available')" 2>&1; which tesseract 2>&1; tesseract --version 2>&1

pip install pytesseract 2>&1 | tail -5; apt-get install -y tesseract-ocr 2>&1 | tail -10

pip install tesserocr 2>&1 | tail -5; python3 -c "import tesserocr; print('tesserocr ok')" 2>&1

python3 -c " import tesserocr from PIL import Image import os pages_dir = '/tmp/workspace/pdf_pages' fnames = sorted([f for f in os.listdir(pages_dir) if f.endswith('.png')]) print(f'Found {len(fnames)} PNG files') # Test first page img = Image.open(os.path.join(pages_dir, fnames[0])) print(f'Image size: {img.size}') text = tesserocr.image_to_text(img) print('First 500 chars:') print(text[:500]) " 2>&1

python3 -c " import tesserocr print(tesserocr.get_languages()) " 2>&1; find / -name 'eng.traineddata' 2>/dev/null | head -5; ls /usr/share/tesseract-ocr/ 2>/dev/null || echo 'not found'; ls /usr/local/share/tessdata/ 2>/dev/null || echo 'not found'

# Download tessdata for English mkdir -p /tmp/tessdata wget -q "https://github.com/tesseract-ocr/tessdata/raw/main/eng.traineddata" -O /tmp/tessdata/eng.traineddata 2>&1 && echo "Downloaded eng.traineddata" || echo "wget failed" ls -lh /tmp/tessdata/

python3 -c " import urllib.request import os os.makedirs('/tmp/tessdata', exist_ok=True) url = 'https://github.com/tesseract-ocr/tessdata/raw/main/eng.traineddata' print('Downloading tessdata...') urllib.request.urlretrieve(url, '/tmp/tessdata/eng.traineddata') print('Done:', os.path.getsize('/tmp/tessdata/eng.traineddata'), 'bytes') " 2>&1

python3 -c " import tesserocr from PIL import Image import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' fnames = sorted([f for f in os.listdir(pages_dir) if f.endswith('.png')]) print(f'Found {len(fnames)} PNG files') # Test first page img = Image.open(os.path.join(pages_dir, fnames[0])) print(f'Image size: {img.size}') text = tesserocr.image_to_text(img, path='/tmp/tessdata/') print('First 800 chars:') print(text[:800]) " 2>&1

python3 -c " from PIL import Image import os img = Image.open('/tmp/workspace/pdf_pages/page_001.png') print('Size:', img.size) print('Mode:', img.mode) # Save a small preview img_small = img.resize((400, 300)) img_small.save('/tmp/preview_page1.png') print('Saved preview') # Check other pages for i in [1,2,3]: img2 = Image.open(f'/tmp/workspace/pdf_pages/page_00{i}.png') print(f'page_00{i}: {img2.size}, {img2.mode}') " 2>&1

# Let's try with better image preprocessing - rotate, enhance contrast, and try OCR again python3 -c " import tesserocr from PIL import Image, ImageEnhance, ImageFilter import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' # Try page_001 with different rotations and preprocessing img = Image.open('/tmp/workspace/pdf_pages/page_001.png') print('Size:', img.size) # Try converting to grayscale and enhancing img_gray = img.convert('L') enhancer = ImageEnhance.Contrast(img_gray) img_enhanced = enhancer.enhance(2.0) # Try different rotations for angle in [0, 90, 180, 270]: if angle != 0: img_rot = img_enhanced.rotate(angle, expand=True) else: img_rot = img_enhanced text = tesserocr.image_to_text(img_rot, path='/tmp/tessdata/') clean = ' '.join(text.split()) print(f'Rotation {angle}: first 200 chars: {clean[:200]}') print('---') " 2>&1

# Let's look at the actual PDF files that are also present - maybe they extract better python3 -c " import os # Check if PyMuPDF or pdfplumber is available try: import fitz print('PyMuPDF (fitz) available') except: print('PyMuPDF not available') try: import pdfplumber print('pdfplumber available') except: print('pdfplumber not available') try: import pdfminer print('pdfminer available') except: print('pdfminer not available') # list all PDF files pdfs = sorted([f for f in os.listdir('/tmp/workspace/pdf_pages') if f.endswith('.pdf')]) print(f'PDF files: {pdfs}') " 2>&1

python3 -c " import pdfplumber import os # Test with first PDF pdf_path = '/tmp/workspace/pdf_pages/page_001.pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Pages in PDF: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'=== Page {i+1} ===') print(text[:500] if text else '[No text extracted]') print() " 2>&1

pip install pymupdf 2>&1 | tail -5

python3 -c " import fitz # PyMuPDF import os # Test extracting image from PDF and then render it pdf_path = '/tmp/workspace/pdf_pages/page_001.pdf' doc = fitz.open(pdf_path) print(f'Pages: {len(doc)}') page = doc[0] print(f'Page rect: {page.rect}') text = page.get_text() print(f'Direct text: {repr(text[:200])}') # Render page to pixmap at higher resolution mat = fitz.Matrix(3, 3) # 3x zoom pix = page.get_pixmap(matrix=mat) print(f'Pixmap size: {pix.width}x{pix.height}') pix.save('/tmp/pdf_page1_rendered.png') print('Saved rendered page') " 2>&1

# The PDFs have image-based content. Let me try OCR with tesserocr using rendered PDF pages # First, let's try with the PDF-rendered image at higher resolution python3 -c " import tesserocr from PIL import Image import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' # Use the rendered PDF page at higher resolution img = Image.open('/tmp/pdf_page1_rendered.png') print('Size:', img.size) img_gray = img.convert('L') text = tesserocr.image_to_text(img_gray, path='/tmp/tessdata/', lang='eng') print('=== Extracted text ===') print(text[:1000]) " 2>&1

# The image appears to be upside down/mirrored. Let me render it with different orientations python3 -c " import fitz from PIL import Image import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' import tesserocr pdf_path = '/tmp/workspace/pdf_pages/page_001.pdf' doc = fitz.open(pdf_path) page = doc[0] # Try different rotation matrices for rotation in [0, 90, 180, 270]: mat = fitz.Matrix(2, 2).prerotate(rotation) pix = page.get_pixmap(matrix=mat) img = Image.frombytes('RGB', [pix.width, pix.height], pix.samples) img_gray = img.convert('L') text = tesserocr.image_to_text(img_gray, path='/tmp/tessdata/', lang='eng') clean = ' '.join(text.split()) print(f'Rotation {rotation}: [{clean[:300]}]') print() " 2>&1

python3 << 'EOF' import fitz from PIL import Image, ImageEnhance, ImageFilter import os import tesserocr os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' # First, get the best rotation for page_001 via the rendered PDF # We already know it's 180 degrees for page_001 # Strategy: use PDFs for pages 1-10, PNGs for pages 11-20 # For PDFs we know 180 rotation works. Let's test PNG pages with 180 rotation too. # Test PNG page_001 with 180 rotation img_png = Image.open(os.path.join(pages_dir, 'page_001.png')) img_png_rot = img_png.rotate(180, expand=True) img_gray = img_png_rot.convert('L') # Enhance contrast enhancer = ImageEnhance.Contrast(img_gray) img_enh = enhancer.enhance(1.5) text = tesserocr.image_to_text(img_enh, path='/tmp/tessdata/', lang='eng') print("PNG page_001 with 180 rotation:") print(text[:500]) EOF 2>&1

python3 << 'EOF' import fitz from PIL import Image, ImageEnhance import os import tesserocr os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' # Render PDFs at 300 DPI equivalent (using matrix scale of ~3) and extract text # page_001.pdf through page_010.pdf all_text = {} for i in range(1, 11): fname = f'page_{i:03d}.pdf' pdf_path = os.path.join(pages_dir, fname) doc = fitz.open(pdf_path) page = doc[0] # 180 degree rotation + scale 2.5x mat = fitz.Matrix(2.5, 2.5).prerotate(180) pix = page.get_pixmap(matrix=mat) img = Image.frombytes('RGB', [pix.width, pix.height], pix.samples) img_gray = img.convert('L') enhancer = ImageEnhance.Contrast(img_gray) img_enh = enhancer.enhance(1.5) text = tesserocr.image_to_text(img_enh, path='/tmp/tessdata/', lang='eng') all_text[fname] = text print(f"=== {fname} ===") print(text[:600]) print() EOF 2>&1

# The PDFs are image-based PDFs with poor OCR results. # Let me try directly with the PNG files - they may be higher quality # Also try with psm (page segmentation mode) 3 or 6 which are better for document text python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance, ImageFilter import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' # Test PNG pages with various preprocessing for page_num in [1, 3, 4, 5, 6]: fname = f'page_{page_num:03d}.png' img = Image.open(os.path.join(pages_dir, fname)) print(f"=== {fname}: size={img.size} ===") # Try 180 rotation since that worked for page_001 img_rot = img.rotate(180, expand=True) img_gray = img_rot.convert('L') # Use tesserocr with specific page segmentation mode with tesserocr.PyTessBaseAPI(path='/tmp/tessdata/', lang='eng') as api: api.SetImage(img_gray) api.SetVariable('tessedit_pageseg_mode', '3') # Fully automatic page segmentation text = api.GetUTF8Text() print(text[:600]) print() EOF 2>&1

python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance, ImageFilter import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' fnames = sorted([f for f in os.listdir(pages_dir) if f.endswith('.png')]) all_text = {} for fname in fnames: img = Image.open(os.path.join(pages_dir, fname)) w, h = img.size # Try 180 rotation (works for landscape pages) # For portrait pages (h > w), try 0 or 90 best_text = '' best_len = 0 rotations = [180, 0, 90, 270] if w > h else [0, 180, 90, 270] for angle in rotations: if angle != 0: img_rot = img.rotate(angle, expand=True) else: img_rot = img img_gray = img_rot.convert('L') enhancer = ImageEnhance.Contrast(img_gray) img_enh = enhancer.enhance(1.5) with tesserocr.PyTessBaseAPI(path='/tmp/tessdata/', lang='eng') as api: api.SetImage(img_enh) text = api.GetUTF8Text() # Score: count ASCII printable chars in words words = [w for w in text.split() if w.isascii() and any(c.isalpha() for c in w)] score = len(words) if score > best_len: best_len = score best_text = text all_text[fname] = best_text print(f"=== {fname} (best rotation, {best_len} words) ===") print(best_text[:800]) print() # Save all text with open('/tmp/all_ocr_text.txt', 'w') as f: for fname in sorted(all_text.keys()): f.write(f"\n\n{'='*60}\n=== {fname} ===\n{'='*60}\n\n") f.write(all_text[fname]) print("\n=== SAVED TO /tmp/all_ocr_text.txt ===") EOF 2>&1

# Many pages are rotated differently or have poor OCR. Let me check pages 3, 6, 7, 10, 11, 12 more carefully # Also let me try with higher resolution and better preprocessing python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance, ImageFilter import os os.environ['TESSDATA_PREFIX'] = '/tmp/tessdata' pages_dir = '/tmp/workspace/pdf_pages' # Check pages that gave 0 words - try all rotations explicitly problem_pages = ['page_003.png', 'page_006.png', 'page_007.png', 'page_010.png', 'page_011.png', 'page_012.png', 'page_014.png', 'page_016.png', 'page_019.png', 'page_020.png'] for fname in problem_pages: img = Image.open(os.path.join(pages_dir, fname)) w, h = img.size print(f"\n=== {fname}: size={img.size} ===") for angle in [0, 90, 180, 270]: if angle != 0: img_rot = img.rotate(angle, expand=True) else: img_rot = img img_gray = img_rot.convert('L') enhancer = ImageEnhance.Contrast(img_gray) img_enh = enhancer.enhance(2.0) with tesserocr.PyTessBaseAPI(path='/tmp/tessdata/', lang='eng') as api: api.SetImage(img_enh) text = api.GetUTF8Text() words = [w for w in text.split() if any(c.isalpha() for c in w)] clean = ' '.join(text.split()) if len(words) > 3: print(f" Rotation {angle} ({len(words)} words): {clean[:300]}") else: print(f" Rotation {angle}: ({len(words)} words) - poor") EOF 2>&1

# Let me look at what these pages actually look like by saving thumbnails for analysis python3 << 'EOF' from PIL import Image import os pages_dir = '/tmp/workspace/pdf_pages' output_dir = '/tmp/thumbnails' os.makedirs(output_dir, exist_ok=True) # Create thumbnails for problem pages problem_pages = ['page_003.png', 'page_006.png', 'page_007.png', 'page_010.png', 'page_011.png', 'page_012.png', 'page_014.png', 'page_016.png', 'page_019.png', 'page_020.png'] for fname in problem_pages: img = Image.open(os.path.join(pages_dir, fname)) # Create 400x400 max thumbnail img.thumbnail((600, 600)) thumb_path = os.path.join(output_dir, fname) img.save(thumb_path) print(f"Saved thumbnail: {fname} (original: {img.size})") EOF 2>&1

# Let me try a different approach - use base64 encoding to convert images to data URIs # Then use a simple HTTP server or encode directly # First let's check if there's a better OCR tool available python3 -c " import subprocess result = subprocess.run(['pip', 'list'], capture_output=True, text=True) for line in result.stdout.split('\n'): if any(x in line.lower() for x in ['ocr', 'google', 'vision', 'cloud', 'aws', 'boto']): print(line) " 2>&1 # Also check if we can install easyocr pip install easyocr 2>&1 | tail -5

# Try easyocr on a few problem pages python3 << 'EOF' import easyocr import os reader = easyocr.Reader(['en'], gpu=False) pages_dir = '/tmp/workspace/pdf_pages' # Test on page_003 and page_007 for fname in ['page_003.png', 'page_007.png', 'page_011.png', 'page_020.png']: fpath = os.path.join(pages_dir, fname) print(f"\n=== {fname} ===") results = reader.readtext(fpath, detail=0, paragraph=True) print('\n'.join(results[:20])) EOF 2>&1

# The images are too large for easyocr. Let's try a different approach - use the analyse_media tool # First, let's check what the problem pages look like by creating smaller previews python3 << 'EOF' from PIL import Image import os pages_dir = '/tmp/workspace/pdf_pages' out_dir = '/tmp/small_previews' os.makedirs(out_dir, exist_ok=True) problem_pages = ['page_003', 'page_006', 'page_007', 'page_010', 'page_011', 'page_012', 'page_014', 'page_016', 'page_019', 'page_020'] for pg in problem_pages: fpath = os.path.join(pages_dir, f'{pg}.png') img = Image.open(fpath) # Resize to small preview img.thumbnail((800, 800)) out_path = os.path.join(out_dir, f'{pg}_small.png') img.save(out_path) print(f"Saved {out_path} ({img.size})") EOF 2>&1

# Need to use fetch_image instead. First let's do more aggressive OCR on all problem pages # Try different PSM modes and rotations with tesserocr python3 << 'EOF' import tesserocr from PIL import Image, ImageFilter, ImageEnhance import os pages_dir = '/tmp/workspace/pdf_pages' def ocr_page(img_path, rotations=[0, 180, 90, 270], psm_modes=[3, 6, 11, 12]): """Try multiple rotations and PSM modes""" img = Image.open(img_path) best_result = "" best_word_count = 0 best_params = None for rot in rotations: if rot != 0: test_img = img.rotate(rot, expand=True) else: test_img = img.copy() # Enhance contrast enhancer = ImageEnhance.Contrast(test_img) test_img = enhancer.enhance(2.0) for psm in psm_modes: try: with tesserocr.PyTessBaseAPI(path='/tmp/tessdata', lang='eng') as api: api.SetPageSegMode(psm) api.SetImage(test_img) text = api.GetUTF8Text() words = [w for w in text.split() if len(w) > 2] if len(words) > best_word_count: best_word_count = len(words) best_result = text best_params = (rot, psm) except Exception as e: pass return best_result, best_word_count, best_params problem_pages = ['page_003', 'page_006', 'page_007', 'page_010', 'page_011', 'page_012', 'page_014', 'page_016', 'page_019', 'page_020'] results = {} for pg in problem_pages: fpath = os.path.join(pages_dir, f'{pg}.png') text, wc, params = ocr_page(fpath) results[pg] = (text, wc, params) print(f"\n{'='*50}") print(f"PAGE: {pg} | Words: {wc} | Best params: {params}") print(f"Text: {text[:500] if text else '(empty)'}") EOF 2>&1

python3 -c " from pdf2image import convert_from_path import os os.makedirs('/tmp/workspace/pdf_pages', exist_ok=True) # Convert all 128 pages in batches - first batch 1-40 images = convert_from_path('/tmp/workspace/attachments/c117fa65-1380-49a9-ae03-fa925041690c/DOC-20260715-WA0002..pdf', dpi=200, first_page=1, last_page=40) for i, img in enumerate(images): img.save(f'/tmp/workspace/pdf_pages/page_{i+1:03d}.png', 'PNG') print(f'Saved {len(images)} pages') " 2>&1