cd /tmp/workspace/attachments/18c11cc5-f325-4863-80e7-e32bbda4a458/ && python3 -c " import pdfplumber with pdfplumber.open('Vijayalakshmi case.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): print(f'\n=== PAGE {i+1} ===') text = page.extract_text() print(text) tables = page.extract_tables() for j, table in enumerate(tables): print(f'\n--- TABLE {j+1} ---') for row in table: print(row) "

cd /tmp/workspace/attachments/18c11cc5-f325-4863-80e7-e32bbda4a458/ && python3 -c " from pypdf import PdfReader import base64 reader = PdfReader('Vijayalakshmi case.pdf') print(f'Pages: {len(reader.pages)}') # Check if there are images for i, page in enumerate(reader.pages): print(f'Page {i+1} images: {len(page.images)}') for img in page.images: print(f' Image name: {img.name}, size: {len(img.data)} bytes') "

cd /tmp/workspace/attachments/18c11cc5-f325-4863-80e7-e32bbda4a458/ && python3 -c " from pypdf import PdfReader reader = PdfReader('Vijayalakshmi case.pdf') for i, page in enumerate(reader.pages): for img in page.images: with open(f'/tmp/workspace/page_{i+1}.jpg', 'wb') as f: f.write(img.data) print(f'Saved page_{i+1}.jpg') "

python3 -c " import base64, json # Read image 1 and encode to base64 with open('/tmp/workspace/page_1.jpg', 'rb') as f: data = f.read() print(f'Page 1 size: {len(data)} bytes') # Try OCR using pytesseract try: from PIL import Image import pytesseract img = Image.open('/tmp/workspace/page_1.jpg') text = pytesseract.image_to_string(img) print('OCR TEXT PAGE 1:') print(text) except Exception as e: print(f'OCR failed: {e}') "

python3 -c " # Serve the image locally via a simple HTTP server approach - use base64 in data URL import base64 for i in range(1, 5): with open(f'/tmp/workspace/page_{i}.jpg', 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() print(f'Page {i}: data:image/jpeg;base64,{b64[:50]}...') print(f' Full length: {len(b64)}') " 2>&1 | head -20

# Start a simple HTTP server in background on port 8888 cd /tmp/workspace && python3 -m http.server 8888 & sleep 2 echo "Server started" curl -s -o /dev/null -w "%{http_code}" http://localhost:8888/page_1.jpg

# Try using markitdown to extract text from the PDF python3 -c " from markitdown import MarkItDown md = MarkItDown() result = md.convert('/tmp/workspace/attachments/18c11cc5-f325-4863-80e7-e32bbda4a458/Vijayalakshmi case.pdf') print(result.text_content[:5000]) "

python3 -c " from PIL import Image import os for i in range(1, 5): img = Image.open(f'/tmp/workspace/page_{i}.jpg') print(f'Page {i}: {img.size} mode={img.mode}') # Enhance contrast for better readability from PIL import ImageEnhance, ImageFilter enhanced = ImageEnhance.Contrast(img).enhance(2.0) enhanced = enhanced.filter(ImageFilter.SHARPEN) enhanced.save(f'/tmp/workspace/page_{i}_enhanced.jpg', quality=95) print(f' Saved enhanced version') print('Done') "

python3 -c " # Try to convert images to base64 and display inline, or use a different OCR # Check if any OCR tool is available import subprocess result = subprocess.run(['which', 'tesseract'], capture_output=True, text=True) print('tesseract:', result.stdout.strip()) result2 = subprocess.run(['which', 'convert'], capture_output=True, text=True) print('convert:', result2.stdout.strip()) result3 = subprocess.run(['pip', 'show', 'easyocr'], capture_output=True, text=True) print('easyocr:', result3.stdout[:200] if result3.stdout else 'not found') result4 = subprocess.run(['pip', 'show', 'paddleocr'], capture_output=True, text=True) print('paddleocr:', result4.stdout[:200] if result4.stdout else 'not found') # Try google vision or other available import pkgutil packages = [p.name for p in pkgutil.iter_modules() if 'ocr' in p.name.lower() or 'vision' in p.name.lower()] print('OCR-related packages:', packages) "

# Try installing pytesseract and tesseract pip install pytesseract --user -q 2>&1 | tail -3 apt-get list --installed 2>/dev/null | grep tesseract | head -5 which tesseract || echo "no tesseract binary" # Try using convert to upscale image and then use any available tool ls /usr/share/tesseract-ocr/ 2>/dev/null || echo "No tesseract-ocr share dir"

python3 << 'EOF' # Try using the markitdown with LLM support or any available image-reading library # Check available image processing libraries import sys # Try anthropic vision API if available try: import anthropic print("anthropic available:", anthropic.__version__) except: print("no anthropic") # Check what's available for image text extraction try: import google.generativeai as genai print("google generativeai available") except: print("no google generativeai") # Try reading with PIL and check image details from PIL import Image img = Image.open('/tmp/workspace/page_1.jpg') print(f"Page 1 image: {img.size}, mode={img.mode}") # Save a thumbnail to check content img_small = img.resize((400, 533)) img_small.save('/tmp/workspace/page_1_thumb.jpg') print("Saved thumbnail") # Print pixel values from different regions to understand layout import numpy as np arr = np.array(img) print(f"Image array shape: {arr.shape}") print(f"Min/Max pixel: {arr.min()}/{arr.max()}") # Check if it's mostly white (text on white background) white_pixels = np.sum(arr > 200) / arr.size print(f"White pixel fraction: {white_pixels:.2%}") EOF

I was unable to automatically extract text from the scanned PDF. Could you please type out the key details from the chart? Specifically: Patient demographics, diagnoses, and the list of all medications with doses/frequency?