python3 << 'EOF' import pdfplumber pdf_path = "/tmp/workspace/attachments/3ce6b82d-89e1-45a6-b353-c3b60233856c/Anatomy (18-23).pdf" with pdfplumber.open(pdf_path) as pdf: print(f"Total pages: {len(pdf.pages)}") full_text = "" for i, page in enumerate(pdf.pages): text = page.extract_text() if text: full_text += f"\n\n--- PAGE {i+1} ---\n{text}" print(full_text[:8000]) EOF

python3 << 'EOF' from pypdf import PdfReader pdf_path = "/tmp/workspace/attachments/3ce6b82d-89e1-45a6-b353-c3b60233856c/Anatomy (18-23).pdf" reader = PdfReader(pdf_path) print(f"Total pages: {len(reader.pages)}") full_text = "" for i, page in enumerate(reader.pages): text = page.extract_text() if text and text.strip(): full_text += f"\n\n--- PAGE {i+1} ---\n{text}" if full_text.strip(): print(full_text[:10000]) else: print("NO TEXT EXTRACTED - likely scanned/image-based PDF") # Check metadata meta = reader.metadata print(f"Metadata: {meta}") EOF

python3 << 'EOF' # Convert PDF pages to images using pypdf's page rendering (if available) or PIL # Try using pdf2image or pypdf image extraction import subprocess result = subprocess.run(['python3', '-c', 'import pdf2image; print("pdf2image available")'], capture_output=True, text=True) print(result.stdout, result.stderr) result2 = subprocess.run(['python3', '-c', 'import fitz; print("PyMuPDF available")'], capture_output=True, text=True) print(result2.stdout, result2.stderr) result3 = subprocess.run(['python3', '-c', 'from pypdf import PdfReader; r=PdfReader("/tmp/workspace/attachments/3ce6b82d-89e1-45a6-b353-c3b60233856c/Anatomy (18-23).pdf"); p=r.pages[0]; print(dir(p))'], capture_output=True, text=True) print(result3.stdout[:500], result3.stderr[:200]) EOF

python3 << 'EOF' from pdf2image import convert_from_path import os pdf_path = "/tmp/workspace/attachments/3ce6b82d-89e1-45a6-b353-c3b60233856c/Anatomy (18-23).pdf" output_dir = "/tmp/workspace/anatomy_pages" os.makedirs(output_dir, exist_ok=True) # Convert all 33 pages to images at moderate DPI images = convert_from_path(pdf_path, dpi=150) print(f"Converted {len(images)} pages") for i, img in enumerate(images): img_path = f"{output_dir}/page_{i+1:02d}.jpg" img.save(img_path, "JPEG", quality=85) print(f"Saved page {i+1}: {img_path}") EOF

You have 33 scanned anatomy exam paper images saved at /tmp/workspace/anatomy_pages/page_01.jpg through page_33.jpg. These are anatomy question papers from years 2018 to 2023. Your task is to: 1. Analyse ALL 33 page images using the analyse_media tool. The images are local files - use the file:// URL scheme: e.g. file:///tmp/workspace/anatomy_pages/page_01.jpg 2. For each page, extract: the year of the paper, ALL question topics/subject headings mentioned (e.g. "brachial plexus", "femoral triangle", "liver anatomy", etc.) 3. After analysing all pages, compile a comprehensive list of ALL anatomy topics that appear in MORE THAN ONE year's paper (i.e., repeated topics across different years 2018-2023). Please analyse pages in parallel batches where possible to be efficient. Return your final answer as: - A structured list of REPEATED TOPICS (topics that appear in 2+ different years), showing which years each topic appeared in. - Also include a full raw list of all topics found per year for reference. Be thorough - read every question on every page carefully.

ls /tmp/workspace/anatomy_pages/ | head -5 && echo "Total files:" && ls /tmp/workspace/anatomy_pages/ | wc -l

cd /tmp/workspace/anatomy_pages && nohup python3 -m http.server 8765 > /tmp/http_server.log 2>&1 & echo "PID: $!" && sleep 2 && curl -s -o /dev/null -w "%{http_code}" http://localhost:8765/page_01.jpg

python3 -c "import base64; data = open('/tmp/workspace/anatomy_pages/page_01.jpg','rb').read(); print('Size:', len(data), 'bytes'); print('B64 length:', len(base64.b64encode(data)))"

# Check if there's a publicly accessible URL or ngrok-like service # Also try to use pytesseract or PIL to extract text directly python3 -c "import pytesseract; print('tesseract available')" 2>/dev/null || echo "no tesseract" python3 -c "from PIL import Image; print('PIL available')" 2>/dev/null || echo "no PIL" which tesseract 2>/dev/null || echo "no tesseract binary"

# Try installing tesseract apt-get install -y tesseract-ocr 2>&1 | tail -5

# Check what's available for OCR pip install pytesseract 2>&1 | tail -3 pip install easyocr 2>&1 | tail -3 pip install paddleocr 2>&1 | tail -3

# Try easyocr on page 1 to see if it works python3 -c " import easyocr reader = easyocr.Reader(['en'], gpu=False, verbose=False) result = reader.readtext('/tmp/workspace/anatomy_pages/page_01.jpg', detail=0, paragraph=True) print('\\n'.join(result)) " 2>/dev/null

python3 << 'EOF' import easyocr import json import os reader = easyocr.Reader(['en'], gpu=False, verbose=False) results = {} pages_dir = '/tmp/workspace/anatomy_pages' for i in range(1, 34): fname = f'page_{i:02d}.jpg' fpath = os.path.join(pages_dir, fname) try: text_lines = reader.readtext(fpath, detail=0, paragraph=True) results[fname] = '\n'.join(text_lines) print(f"Done: {fname} ({len(text_lines)} paragraphs)") except Exception as e: results[fname] = f"ERROR: {e}" print(f"Error: {fname}: {e}") # Save results with open('/tmp/ocr_results.json', 'w') as f: json.dump(results, f, indent=2) print("\nAll done! Saved to /tmp/ocr_results.json") EOF

# Process in smaller batches to avoid memory issues - pages 1-6 first python3 << 'EOF' import easyocr import json import os reader = easyocr.Reader(['en'], gpu=False, verbose=False) results = {} pages_dir = '/tmp/workspace/anatomy_pages' for i in range(1, 7): fname = f'page_{i:02d}.jpg' fpath = os.path.join(pages_dir, fname) try: text_lines = reader.readtext(fpath, detail=0, paragraph=True) results[fname] = '\n'.join(text_lines) print(f"Done: {fname}") except Exception as e: results[fname] = f"ERROR: {e}" print(f"Error: {fname}: {e}") with open('/tmp/ocr_batch1.json', 'w') as f: json.dump(results, f, indent=2) print("Batch 1 done!") EOF

# Check memory available free -h # Check image sizes ls -la /tmp/workspace/anatomy_pages/ | head -10

# EasyOCR is too memory-heavy. Let's try PaddleOCR which may be lighter, or use a subprocess approach # First check if paddleocr works on a single page python3 -c " from paddleocr import PaddleOCR ocr = PaddleOCR(use_angle_cls=False, lang='en', use_gpu=False, show_log=False) result = ocr.ocr('/tmp/workspace/anatomy_pages/page_01.jpg', cls=False) for line in result[0]: print(line[1][0]) " 2>/dev/null | head -30

# PaddleOCR produced no output. Let's try with log to see what's happening python3 -c " from paddleocr import PaddleOCR ocr = PaddleOCR(use_angle_cls=False, lang='en', use_gpu=False) result = ocr.ocr('/tmp/workspace/anatomy_pages/page_01.jpg', cls=False) print(type(result)) print(result[:2] if result else 'None') " 2>&1 | tail -20

python3 -c " from paddleocr import PaddleOCR ocr = PaddleOCR(lang='en') result = ocr.ocr('/tmp/workspace/anatomy_pages/page_01.jpg') if result and result[0]: for line in result[0]: print(line[1][0]) else: print('No result') " 2>&1 | grep -v "WARNING\|INFO\|Deprecation\|paddle\|model" | head -50