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python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages[:3]): print(f'\n--- PAGE {i+1} ---') print(page.extract_text()) "

python3 -c " from pypdf import PdfReader reader = PdfReader('/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf') print(f'Pages: {len(reader.pages)}') for i, page in enumerate(reader.pages[:2]): print(f'Page {i+1} text: {repr(page.extract_text()[:200])}') # Check for images print(f'Page {i+1} images: {len(page.images)}') "

python3 -c " import fitz # PyMuPDF import os pdf_path = '/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf' out_dir = '/tmp/workspace/fssai_pages' os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) print(f'Pages: {len(doc)}') for i in range(len(doc)): page = doc[i] pix = page.get_pixmap(dpi=150) pix.save(f'{out_dir}/page_{i+1:02d}.png') print(f'Saved page {i+1}') "

pip install --user pymupdf -q && python3 -c "import fitz; print('fitz ok', fitz.__version__)"

python3 -c " import fitz import os pdf_path = '/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf' out_dir = '/tmp/workspace/fssai_pages' os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) print(f'Pages: {len(doc)}') for i in range(len(doc)): page = doc[i] pix = page.get_pixmap(dpi=150) pix.save(f'{out_dir}/page_{i+1:02d}.png') print(f'Saved page {i+1}') print('Done') "

I have a 13-page image-based PDF about FSSAI (Food Safety and Standards Authority of India) that has already been converted to PNG images. The images are saved at: /tmp/workspace/fssai_pages/page_01.png through page_13.png Your task: 1. Use the analyse_media tool on EACH of the 13 pages. The images are local files, but analyse_media needs a URL. So instead, use the read_file tool to check if there's a way, OR use execute to serve them, OR use the analyse_media tool with file:// path if supported. Actually - please use the execute tool to read each image as base64 and then use the analyse_media tool with each image file. The analyse_media tool accepts URLs. Since these are local files in the sandbox, try using "file:///tmp/workspace/fssai_pages/page_01.png" as the URL format. If that doesn't work, use python3 with pdfplumber or pymupdf to do OCR-style text extraction from the original PDF at: /tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf Try this approach with PyMuPDF which was already installed: ```python import fitz doc = fitz.open('/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf') for i, page in enumerate(doc): text = page.get_text() print(f"=== PAGE {i+1} ===") print(text) ``` If PyMuPDF extracts text, return ALL the text from all 13 pages verbatim. If PyMuPDF returns empty text (image-based), then use analyse_media with the file:// URLs for each page image. Analyse all 13 pages and return the full extracted text/content from each page. Return a complete, detailed transcription/description of ALL content across all 13 pages so that exam notes can be made from it.

python3 -c " import fitz doc = fitz.open('/tmp/workspace/attachments/280e2e97-d13e-4802-bdf9-0ebec00bce8e/FSSAI.pdf') print(f'Total pages: {len(doc)}') for i, page in enumerate(doc): text = page.get_text() print(f'=== PAGE {i+1} === (chars: {len(text)})') print(text[:200] if text else '[EMPTY]') print() "

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

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

pip install pytesseract -q 2>&1 | tail -3 apt-get install -y tesseract-ocr 2>&1 | tail -5

# Check if tesseract binary is available under a different path ls /usr/bin/tesseract 2>/dev/null || ls /usr/local/bin/tesseract 2>/dev/null || echo "tesseract not found" # Also check for other OCR options python3 -c "import easyocr; print('easyocr ok')" 2>&1 python3 -c "import paddleocr; print('paddleocr ok')" 2>&1

# Try to use the PDF skill to read the images # Check if there's a way to use the fetch_image tool through the pdf skill # Also check what OCR tools are available python3 -c " import subprocess, sys # Try installing easyocr result = subprocess.run([sys.executable, '-m', 'pip', 'install', 'easyocr', '-q'], capture_output=True, text=True) print(result.stdout[-500:] if result.stdout else '') print(result.stderr[-500:] if result.stderr else '') " 2>&1 | tail -10

python3 -c "import easyocr; print('easyocr installed successfully')" 2>&1

python3 -c " import easyocr reader = easyocr.Reader(['en'], gpu=False) result = reader.readtext('/tmp/workspace/fssai_pages/page_01.png', detail=0, paragraph=True) print('PAGE 1:') for line in result: print(line) " 2>&1

ls /tmp/workspace/fssai_pages/ | head -20

python3 -c " from PIL import Image import os page = Image.open('/tmp/workspace/fssai_pages/page_01.png') print('Size:', page.size, 'Mode:', page.mode) import sys print('PIL version:', Image.__version__ if hasattr(Image, '__version__') else 'ok') "

# First resize all pages to smaller size and convert to grayscale to save memory python3 -c " from PIL import Image import os src = '/tmp/workspace/fssai_pages' dst = '/tmp/workspace/fssai_small' os.makedirs(dst, exist_ok=True) for i in range(1, 14): fname = f'page_{i:02d}.png' img = Image.open(os.path.join(src, fname)) # Convert to grayscale img = img.convert('L') # Resize to 1000px wide, maintain aspect ratio w, h = img.size new_w = 1000 new_h = int(h * new_w / w) img = img.resize((new_w, new_h), Image.LANCZOS) out = os.path.join(dst, fname) img.save(out, optimize=True) print(f'{fname}: {w}x{h} -> {new_w}x{new_h}') print('Done resizing all pages') "

# Write a standalone script that processes ONE page and saves result # Run it in a subprocess with timeout to avoid OOM killing everything cat > /tmp/ocr_page.py << 'EOF' import sys import os import json page_num = int(sys.argv[1]) img_path = f'/tmp/workspace/fssai_small/page_{page_num:02d}.png' out_path = f'/tmp/workspace/fssai_ocr/page_{page_num:02d}.json' os.makedirs('/tmp/workspace/fssai_ocr', exist_ok=True) import easyocr reader = easyocr.Reader(['en'], gpu=False, verbose=False) results = reader.readtext(img_path, detail=1) # results is list of (bbox, text, confidence) data = [(text, float(conf)) for (bbox, text, conf) in results] with open(out_path, 'w') as f: json.dump(data, f) print(f'Page {page_num}: {len(data)} text blocks saved') EOF echo "Script written"

mkdir -p /tmp/workspace/fssai_ocr && python3 /tmp/ocr_page.py 1