Add images

I need to understand what you mean by "Add images" to this PDF. Let me clarify before proceeding.
Asking for Preferences

What would you like me to do with this PDF?

I need a bit more detail to do this properly.
Asking for Preferences

Where should the images come from? · Where in the PDF should the images be placed?

Let me start by reading the PDF skill and the PDF content to understand what's in it.
Reading File
Reading File
Now let me first extract the text from the PDF to understand its content, and simultaneously check the medical library for relevant images.
Running Command

python3 -c " import pdfplumber pdf_path = '/home/daytona/workspace/attachments/d83b66e3-d9b6-41a8-8763-e339723ed77f/I have excellent content from Campbell-Walsh-Wein .pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages[:5]): text = page.extract_text() print(f'--- PAGE {i+1} ---') print(text[:800] if text else '[No text]') print() "

Running Command

python3 -c " import pdfplumber pdf_path = '/home/daytona/workspace/attachments/d83b66e3-d9b6-41a8-8763-e339723ed77f/I have excellent content from Campbell-Walsh-Wein .pdf' with pdfplumber.open(pdf_path) as pdf: # Get all headings/topics across all pages all_text = [] for i, page in enumerate(pdf.pages): text = page.extract_text() if text: all_text.append(f'PAGE {i+1}: {text[:300]}') print('\n'.join(all_text[5:])) "

Now I have a clear picture of the PDF content. It covers:
  1. Pages 1-10: Tumour markers of GU system + Renal malignancies (RCC staging, management)
  2. Pages 11-17: Chest trauma
  3. Pages 18-20: Urinary bladder diverticula
  4. Pages 21-24: LUTS / BPH
  5. Pages 25-27: Necrotizing fasciitis / wound infections
  6. Pages 28-31: Spina bifida
Now let me search the medical library for relevant images for each topic simultaneously.
Finding Sources
Finding Sources
Finding Sources
Finding Sources
Finding Sources
Finding Sources
Reading File
Reading File
I found images in the textbook blocks. Let me read more from the RCC block and also look for more images across other topics.
Reading File
Reading File
I have found several images. Let me now look for images in the RCC Campbell-Walsh block and also search for chest trauma and BPH images.
Searching Content

"cdn.orris.care" in /textbooks/9780323546423

Finding Sources
Finding Sources
Reading File
I now have enough image URLs collected. Let me compile all the images I've found and then build the enriched PDF. Let me gather a few more relevant image URLs from BPH and do the final image insertion.
Reading File
Reading File
Now let me read more of block 54 to find the actual image URLs (cdn.orris.care links).
Running Command

python3 -c " import re # Read the block file and extract all image URLs with captions with open('/textbooks/9780323546423/9780323546423_block54.md', 'r') as f: content = f.read() # Find all image lines images = re.findall(r'!\[([^\]]*)\]\((https://cdn\.orris\.care/[^\)]+)\)\s*\n\s*\*([^\*]+)\*', content) for alt, url, caption in images[:15]: print(f'URL: {url}') print(f'Caption: {caption[:120]}') print() "

Browsing Directory

/

The textbook files are served through the read_file tool but not accessible as local filesystem paths. I already have the image URLs from reading the blocks. Let me compile everything I have and build the PDF with images now.
The images I've found so far:
  1. https://cdn.orris.care/cdss_images/c252200a3183c43b7e1b73602cb95dacb5c86fa0bde5607ef2787797eba08488.png - CT showing renal cancer (Fig 29.17 T4 Renal Cancer with metastatic nodes)
  2. https://cdn.orris.care/cdss_images/4c0989dc42da83579b6dfbd16c0facb5bebcaaea90956623c438bd1e415758f2.png - Urothelial cell cancer (Fig 29.23)
  3. https://cdn.orris.care/cdss_images/10d7db25c76590aac8228b05266bc91da6adbd43ecc782f5a70f3048d859fca2.png - Post-cryotherapy RCC (Fig 29.24)
  4. https://cdn.orris.care/cdss_images/15204d744810ab622a0efecd7a7213ac12ede1886b691fae34c519eeffccac5a.png - Checkpoint inhibitor mechanism (Fig 97.8)
Let me read a bit more of the radiology block to get more relevant images for staging.
Reading File
Excellent! Now I have enough images. Let me build the enriched PDF. I'll use the node fetch_images.js script to download the images, then use reportlab and pypdf to overlay them into the PDF.
The image plan:
  • After p.6-7 (RCC types/staging): Add CT of papillary RCC (Fig 29.14) - 85bdc63e...
  • After p.7 (staging table): Add T4 renal cancer CT (Fig 29.17) - c252200a...
  • After p.9 (management): Add checkpoint inhibitor mechanism (Fig 97.8) - 15204d74...
  • After p.4 (tumour markers summary table): Leave as text-heavy intro
Let me write and execute the Python script to build the enriched PDF:
Running Command

mkdir -p /home/daytona/workspace/campbell-images && python3 -c " import json, subprocess, base64, os images = [ ('rcc_papillary_ct', 'https://cdn.orris.care/cdss_images/85bdc63e1a1cef113189ed2bfcf2b5dd1ff2d2cbc94b7a92032b4a43cbc0f64d.png', 'Fig. 29.14: Papillary RCC - Pre and post-contrast CT showing large solid left renal mass with low-level enhancement typical of papillary cell carcinoma. (Grainger & Allison Radiology)'), ('rcc_t4_ct', 'https://cdn.orris.care/cdss_images/c252200a3183c43b7e1b73602cb95dacb5c86fa0bde5607ef2787797eba08488.png', 'Fig. 29.17: T4 Renal Cancer - CECT demonstrating large right renal mass with local infiltration, extension onto liver, and necrotic metastatic para-aortic nodes. (Grainger & Allison Radiology)'), ('checkpoint_inhibitor', 'https://cdn.orris.care/cdss_images/15204d744810ab622a0efecd7a7213ac12ede1886b691fae34c519eeffccac5a.png', 'Fig. 97.8: Mechanism of checkpoint inhibitors (PD-1/CTLA-4 pathway) used in metastatic RCC immunotherapy. (Campbell-Walsh-Wein Urology)'), ('urothelial_tcc', 'https://cdn.orris.care/cdss_images/4c0989dc42da83579b6dfbd16c0facb5bebcaaea90956623c438bd1e415758f2.png', 'Fig. 29.23: Urothelial Cell Cancer of the Kidney - Post-contrast CT showing large central soft-tissue mass invading renal pelvis causing hydronephrosis (TCC). (Grainger & Allison Radiology)'), ] result = json.loads(subprocess.check_output([ 'node', '/home/daytona/skills/shared/scripts/fetch_images.js' ] + [url for _, url, _ in images])) for i, (name, url, caption) in enumerate(images): img_data = result[i] if img_data.get('base64'): raw = base64.b64decode(img_data['base64'].split(',')[1]) out_path = f'/home/daytona/workspace/campbell-images/{name}.png' with open(out_path, 'wb') as f: f.write(raw) print(f'Saved: {out_path} ({len(raw)} bytes)') else: print(f'FAILED: {name} - {img_data.get(\"error\")}') "

All 4 images downloaded. Now let me insert them into the PDF at relevant pages.
Running Command

python3 << 'PYEOF' import io from pypdf import PdfReader, PdfWriter from reportlab.lib.pagesizes import A4 from reportlab.pdfgen import canvas from reportlab.lib.utils import ImageReader from PIL import Image as PILImage INPUT_PDF = '/home/daytona/workspace/attachments/d83b66e3-d9b6-41a8-8763-e339723ed77f/I have excellent content from Campbell-Walsh-Wein .pdf' OUTPUT_PDF = '/home/daytona/workspace/campbell-images/Campbell-Walsh-Wein_with_images.pdf' # Images to insert: (after_page_index (0-based), image_path, caption) # Page 6 = index 5 (RCC types table - after it, add papillary CT) # Page 7 = index 6 (staging table - after it, add T4 CT) # Page 9 = index 8 (management/targeted therapy - after it, add checkpoint inhibitor) # Page 19 = index 18 (bladder/TCC diverticulum complications - add TCC image) IMAGE_INSERTS = [ (5, '/home/daytona/workspace/campbell-images/rcc_papillary_ct.png', 'Fig. 29.14: Papillary RCC - Pre- and post-contrast CT showing large solid left renal mass\nwith low-level enhancement, typical of papillary cell carcinoma.\n(Source: Grainger & Allison\'s Diagnostic Radiology)'), (6, '/home/daytona/workspace/campbell-images/rcc_t4_ct.png', 'Fig. 29.17: T4 Renal Cancer - CECT demonstrating large right renal mass with local\ninfiltration onto liver, and necrotic metastatic para-aortic nodes.\n(Source: Grainger & Allison\'s Diagnostic Radiology)'), (9, '/home/daytona/workspace/campbell-images/checkpoint_inhibitor.png', 'Fig. 97.8: Mechanism of checkpoint inhibitors (PD-1/PD-L1 and CTLA-4 pathways)\nused as immunotherapy in metastatic RCC.\n(Source: Campbell-Walsh-Wein Urology 12th Ed.)'), (18, '/home/daytona/workspace/campbell-images/urothelial_tcc.png', 'Fig. 29.23: Urothelial (Transitional) Cell Carcinoma of the Kidney - Post-contrast CT\nshowing central soft-tissue mass invading renal pelvis causing hydronephrosis.\n(Source: Grainger & Allison\'s Diagnostic Radiology)'), ] def make_image_page(img_path, caption): """Create a PDF page with the image centred and caption below.""" buf = io.BytesIO() w, h = A4 # 595 x 842 pts c = canvas.Canvas(buf, pagesize=A4) # Background c.setFillColorRGB(0.97, 0.97, 0.97) c.rect(0, 0, w, h, fill=1, stroke=0) # Header bar c.setFillColorRGB(0.12, 0.29, 0.49) # dark navy c.rect(0, h - 40, w, 40, fill=1, stroke=0) c.setFillColorRGB(1, 1, 1) c.setFont('Helvetica-Bold', 11) c.drawString(20, h - 26, 'MEDICAL ILLUSTRATION — Campbell-Walsh-Wein Urology / Grainger & Allison Radiology') # Draw image margin = 40 max_img_h = h - 160 # leave room for caption and header max_img_w = w - 2 * margin pil_img = PILImage.open(img_path) iw, ih = pil_img.size ratio = min(max_img_w / iw, max_img_h / ih) draw_w = iw * ratio draw_h = ih * ratio x = (w - draw_w) / 2 y = 100 # bottom area for caption c.drawImage(ImageReader(img_path), x, y, width=draw_w, height=draw_h, preserveAspectRatio=True) # Border around image c.setStrokeColorRGB(0.12, 0.29, 0.49) c.setLineWidth(1.5) c.rect(x - 4, y - 4, draw_w + 8, draw_h + 8, fill=0, stroke=1) # Caption box c.setFillColorRGB(0.22, 0.22, 0.22) c.setFont('Helvetica', 9) lines = caption.split('\n') cap_y = 80 for line in reversed(lines): c.drawCentredString(w / 2, cap_y, line) cap_y -= 13 c.save() buf.seek(0) return PdfReader(buf).pages[0] reader = PdfReader(INPUT_PDF) writer = PdfWriter() original_pages = list(reader.pages) total = len(original_pages) print(f'Original pages: {total}') # Build insert map: after which original page (0-based) to insert an image page insert_map = {} for after_idx, img_path, caption in IMAGE_INSERTS: insert_map.setdefault(after_idx, []).append((img_path, caption)) for i, page in enumerate(original_pages): writer.add_page(page) if i in insert_map: for img_path, caption in insert_map[i]: img_page = make_image_page(img_path, caption) writer.add_page(img_page) print(f' Inserted image after page {i+1}: {img_path.split("/")[-1]}') with open(OUTPUT_PDF, 'wb') as f: writer.write(f) print(f'\nDone! Output: {OUTPUT_PDF}') print(f'Total pages in output: {len(writer.pages)}') PYEOF

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