python3 << 'EOF' import pdfplumber pdf_path = "/tmp/workspace/attachments/4a2bb21c-7a41-4f30-9b9c-bc88e993b838/Anatomy 2013-2025_260728_133938.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) EOF

python3 << 'EOF' import pdfplumber pdf_path = "/tmp/workspace/attachments/4a2bb21c-7a41-4f30-9b9c-bc88e993b838/Anatomy 2013-2025_260728_133938.pdf" with pdfplumber.open(pdf_path) as pdf: 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}" with open("/tmp/workspace/anatomy_text.txt", "w") as f: f.write(full_text) print(f"Done. Total characters: {len(full_text)}") EOF

python3 << 'EOF' from pypdf import PdfReader pdf_path = "/tmp/workspace/attachments/4a2bb21c-7a41-4f30-9b9c-bc88e993b838/Anatomy 2013-2025_260728_133938.pdf" reader = PdfReader(pdf_path) print(f"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}" print(f"Total chars extracted: {len(full_text)}") if full_text: print(full_text[:3000]) else: print("No text extracted - PDF is likely image-based/scanned") EOF

python3 << 'EOF' from pypdf import PdfReader, PdfWriter import os pdf_path = "/tmp/workspace/attachments/4a2bb21c-7a41-4f30-9b9c-bc88e993b838/Anatomy 2013-2025_260728_133938.pdf" reader = PdfReader(pdf_path) print(f"Total pages: {len(reader.pages)}") # Save individual pages as separate PDFs for analysis os.makedirs("/tmp/workspace/anatomy_pages", exist_ok=True) for i, page in enumerate(reader.pages): writer = PdfWriter() writer.add_page(page) with open(f"/tmp/workspace/anatomy_pages/page_{i+1:02d}.pdf", "wb") as f: writer.write(f) print("All pages saved!") ls_out = os.listdir("/tmp/workspace/anatomy_pages") ls_out.sort() print(ls_out) EOF

python3 -c "import pdf2image; print('pdf2image available')" 2>&1; python3 -c "from PIL import Image; import fitz; print('PyMuPDF available')" 2>&1; python3 -c "import fitz; print('fitz version:', fitz.__version__)" 2>&1

python3 << 'EOF' from pdf2image import convert_from_path import os pdf_path = "/tmp/workspace/attachments/4a2bb21c-7a41-4f30-9b9c-bc88e993b838/Anatomy 2013-2025_260728_133938.pdf" os.makedirs("/tmp/workspace/anatomy_images", exist_ok=True) images = convert_from_path(pdf_path, dpi=150) print(f"Converted {len(images)} pages") for i, img in enumerate(images): img_path = f"/tmp/workspace/anatomy_images/page_{i+1:02d}.png" img.save(img_path, "PNG") print(f"Saved page {i+1}: {img_path}") EOF

You are analyzing a scanned anatomy exam question paper (years 2013-2025). All 22 pages have been converted to PNG images and are stored at /tmp/workspace/anatomy_images/page_01.png through page_22.png. Your job is to: 1. Use the `analyse_media` tool to read ALL 22 pages. Since you can only call analyse_media 5 times, you need to be strategic. For each call, pass one image URL at a time as a file path using the format: file:///tmp/workspace/anatomy_images/page_XX.png Actually, analyse_media takes a URL. Use the path format: file:///tmp/workspace/anatomy_images/page_01.png etc. 2. For EACH page, extract ALL the question numbers and their text/topic. 3. Identify which questions are related to NEUROANATOMY. Neuroanatomy includes: - Brain structures (cerebrum, cerebellum, brainstem, basal ganglia, thalamus, hypothalamus, hippocampus, etc.) - Spinal cord anatomy - Cranial nerves (all 12 pairs) - Meninges (dura mater, arachnoid, pia mater) - Ventricles and CSF pathways - Blood supply to brain/spinal cord (Circle of Willis, cerebral arteries) - Nerve tracts (corticospinal, spinothalamic, dorsal columns, etc.) - Peripheral nervous system anatomy - Autonomic nervous system anatomy - Dermatomes and myotomes - Any brain/spinal cord/nerve anatomy question 4. Return a comprehensive list with: - Page number - Question number - Brief description of what the question is about - Why it qualifies as neuroanatomy Analyse all 22 pages. Use all 5 analyse_media calls, each covering multiple pages by requesting detailed extraction of all question text visible on that page. IMPORTANT: Call analyse_media with focus="Extract ALL question numbers and their complete text from this anatomy exam paper page. List every question with its number." for each page image. Return a detailed, structured report of ALL neuroanatomy questions found.

~/anatomy_images

cd /tmp/workspace/anatomy_images && python3 -m http.server 8765 &>/tmp/http_server.log & echo "Server PID: $!"

sleep 1 && curl -s -o /dev/null -w "%{http_code}" http://localhost:8765/page_01.png

cat /tmp/http_server.log

python3 -m http.server 8765 --directory /tmp/workspace/anatomy_images > /tmp/http_server.log 2>&1 & sleep 2 && curl -s -o /dev/null -w "%{http_code}" "http://127.0.0.1:8765/page_01.png"

python3 -c "import pytesseract; from PIL import Image; print('OK')"

pip install pytesseract pillow -q && python3 -c "import pytesseract; from PIL import Image; print('OK')"

which tesseract && tesseract --version 2>&1 | head -3

apt-get install -y tesseract-ocr -q 2>&1 | tail -5

which convert && convert --version 2>&1 | head -3

ls -la /tmp/workspace/anatomy_images/page_01.png && file /tmp/workspace/anatomy_images/page_01.png

# Check if there's an ngrok or public URL tunnel available which ngrok 2>/dev/null || echo "no ngrok" # Check if there's any web accessible path ls /var/www/html 2>/dev/null || echo "no www" # Check environment env | grep -i url 2>/dev/null | head -5

# Try using Python with easyocr or other OCR tools python3 -c "import easyocr; print('easyocr OK')" 2>/dev/null || echo "no easyocr" python3 -c "import cv2; print('cv2 OK')" 2>/dev/null || echo "no cv2" python3 -c "from PIL import Image; img=Image.open('/tmp/workspace/anatomy_images/page_01.png'); print(img.size)" 2>/dev/null

# Try to use Google Vision or OpenAI-compatible API if available through environment env | grep -iE "GOOGLE|OPENAI|VISION|API_KEY|TOKEN" 2>/dev/null | head -10 || echo "no api keys found"

# Convert PNGs to base64 and try to read them via the analyse_media tool in a different way # First let's understand what's in the images using PIL to get basic info python3 << 'EOF' from PIL import Image import os img_dir = "/tmp/workspace/anatomy_images" for i in range(1, 23): fname = f"page_{i:02d}.png" fpath = os.path.join(img_dir, fname) img = Image.open(fpath) print(f"{fname}: {img.size}, mode={img.mode}, size={os.path.getsize(fpath)//1024}KB") EOF

# Install tesseract via a different method pip install tesserocr 2>/dev/null || echo "no tesserocr" # Try ocrmypdf python3 -c "import ocrmypdf; print('ocrmypdf OK')" 2>/dev/null || echo "no ocrmypdf" # Try doctr python3 -c "from doctr.io import DocumentFile; print('doctr OK')" 2>/dev/null || echo "no doctr" # Try paddleocr python3 -c "from paddleocr import PaddleOCR; print('paddle OK')" 2>/dev/null || echo "no paddle"

python3 -c "import tesserocr; print('tesserocr imported OK'); print(tesserocr.tesseract_version())"

python3 << 'EOF' import tesserocr from PIL import Image import os img_dir = "/tmp/workspace/anatomy_images" out_dir = "/tmp/workspace/ocr_output" os.makedirs(out_dir, exist_ok=True) # Check available languages print("Available langs:", tesserocr.get_languages()) EOF

# Find where tessdata is stored find / -name "eng.traineddata" 2>/dev/null | head -5 find / -name "tessdata" -type d 2>/dev/null | head -5

# Download tessdata mkdir -p /tmp/tessdata wget -q "https://github.com/tesseract-ocr/tessdata/raw/main/eng.traineddata" -O /tmp/tessdata/eng.traineddata && echo "Downloaded" || echo "Failed to download"

mkdir -p /tmp/tessdata && curl -L "https://github.com/tesseract-ocr/tessdata/raw/main/eng.traineddata" -o /tmp/tessdata/eng.traineddata && echo "Downloaded $(wc -c < /tmp/tessdata/eng.traineddata) bytes"

python3 << 'EOF' import tesserocr from PIL import Image # Test with page 1 img = Image.open('/tmp/workspace/anatomy_images/page_01.png') text = tesserocr.image_to_text(img, lang='eng', path='/tmp/tessdata') print(text[:2000]) EOF

python3 << 'EOF' import tesserocr from PIL import Image, ImageFilter, ImageEnhance import os def preprocess_image(img): """Preprocess image for better OCR""" # Convert to grayscale gray = img.convert('L') # Enhance contrast enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(2.0) # Resize to improve OCR (upscale by 2x) w, h = enhanced.size resized = enhanced.resize((w*2, h*2), Image.LANCZOS) return resized # Test with page 1 img = Image.open('/tmp/workspace/anatomy_images/page_01.png') processed = preprocess_image(img) text = tesserocr.image_to_text(processed, lang='eng', path='/tmp/tessdata', psm=tesserocr.PSM.AUTO_OSD) print("PAGE 1:") print(text[:3000]) EOF

python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance, ImageFilter import numpy as np def preprocess_for_ocr(img): gray = img.convert('L') # Apply sharpening sharpened = gray.filter(ImageFilter.SHARPEN) # Enhance contrast strongly enhancer = ImageEnhance.Contrast(sharpened) enhanced = enhancer.enhance(3.0) # Upscale w, h = enhanced.size resized = enhanced.resize((w*2, h*2), Image.LANCZOS) # Threshold to binary arr = np.array(resized) threshold = 128 binary = (arr > threshold).astype(np.uint8) * 255 return Image.fromarray(binary) img = Image.open('/tmp/workspace/anatomy_images/page_01.png') processed = preprocess_for_ocr(img) text = tesserocr.image_to_text(processed, lang='eng', path='/tmp/tessdata', psm=tesserocr.PSM.SINGLE_BLOCK) print("PAGE 1 with binarization:") print(text[:4000]) EOF

python3 << 'EOF' # Try reading with different PSM modes and see which gives best result import tesserocr from PIL import Image, ImageEnhance import os def ocr_page(page_num, psm=tesserocr.PSM.AUTO): img = Image.open(f'/tmp/workspace/anatomy_images/page_{page_num:02d}.png') # Simple grayscale + contrast gray = img.convert('L') enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(2.5) w, h = enhanced.size resized = enhanced.resize((int(w*1.5), int(h*1.5)), Image.LANCZOS) text = tesserocr.image_to_text(resized, lang='eng', path='/tmp/tessdata', psm=psm) return text # Try pages 1-5 with SPARSE_TEXT mode which is good for sparse layouts for pg in [1, 2, 3, 4, 5]: print(f"\n{'='*50}") print(f"PAGE {pg}") print('='*50) text = ocr_page(pg, psm=tesserocr.PSM.SPARSE_TEXT) print(text[:3000]) EOF

python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance import os def ocr_page(page_num): img = Image.open(f'/tmp/workspace/anatomy_images/page_{page_num:02d}.png') gray = img.convert('L') enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(2.5) w, h = enhanced.size resized = enhanced.resize((int(w*1.5), int(h*1.5)), Image.LANCZOS) text = tesserocr.image_to_text(resized, lang='eng', path='/tmp/tessdata', psm=tesserocr.PSM.SPARSE_TEXT) return text out_dir = "/tmp/workspace/ocr_output" os.makedirs(out_dir, exist_ok=True) for pg in range(1, 23): print(f"Processing page {pg}...") text = ocr_page(pg) with open(f"{out_dir}/page_{pg:02d}.txt", 'w') as f: f.write(text) print(f" -> {len(text)} chars") print("Done!") EOF

for i in $(seq 1 22); do echo "==============================" echo "PAGE $i" echo "==============================" cat /tmp/workspace/ocr_output/page_$(printf "%02d" $i).txt echo "" done

python3 << 'EOF' import tesserocr from PIL import Image, ImageEnhance def ocr_page_high_quality(page_num): img = Image.open(f'/tmp/workspace/anatomy_images/page_{page_num:02d}.png') gray = img.convert('L') enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(3.0) w, h = enhanced.size resized = enhanced.resize((int(w*2), int(h*2)), Image.LANCZOS) # Try with different PSM text = tesserocr.image_to_text(resized, lang='eng', path='/tmp/tessdata', psm=tesserocr.PSM.AUTO) return text # Pages that need re-examination for pg in [3, 20, 21]: print(f"\n{'='*50}") print(f"PAGE {pg} - HIGH QUALITY RESCAN") print('='*50) text = ocr_page_high_quality(pg) print(text[:5000]) EOF

python3 << 'EOF' # Let me try to get a better read of page 20 which seems to have a chart/table import tesserocr from PIL import Image, ImageEnhance, ImageFilter import numpy as np img = Image.open('/tmp/workspace/anatomy_images/page_20.png') # Try different preprocessing for the complex page gray = img.convert('L') arr = np.array(gray) print(f"Page 20 - pixel stats: min={arr.min()}, max={arr.max()}, mean={arr.mean():.1f}") print(f"Size: {img.size}") # Adaptive threshold from PIL import ImageOps equalized = ImageOps.equalize(gray) w, h = equalized.size resized = equalized.resize((w*2, h*2), Image.LANCZOS) text = tesserocr.image_to_text(resized, lang='eng', path='/tmp/tessdata', psm=tesserocr.PSM.SPARSE_TEXT_OSD) print("\nPage 20 text:") print(text[:5000]) EOF