Orris

Add information about every chart

I'll read the PDF skill first, then extract and analyze the charts from your physiology practical PDF.
Reading File
Now let me extract text from the PDF and also get a sense of what charts are present.
Running Command

cd /home/daytona/workspace/attachments/bb119b92-6cc4-4892-b882-1eadc171a1d9/ && python3 -c " import pdfplumber with pdfplumber.open('physiology practical charts.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'\\n=== PAGE {i+1} ===') print(text[:3000] if text else '[No text extracted]') "

The PDF is entirely image-based (scanned). I need to convert pages to images and use OCR/vision analysis. Let me first clarify what the user wants - do they want me to add information/annotations to a Word document, or a new PDF, describing what's in each chart? Let me first get a sample of what the charts look like.
Asking for Preferences

What would you like me to create? · The PDF has 123 pages. Which charts should I cover?

The PDF is 123 image-based pages. I have a budget of only 5 analyse_media calls, so I need to be strategic. I'll convert all pages to images, then use OCR (pytesseract or easyocr) to extract text from all pages, and supplement with vision analysis for a sample to understand the chart types. Let me run OCR on all pages first.
Running Command

mkdir -p /home/daytona/workspace/physio-charts/images && python3 -c " from pdf2image import convert_from_path import os pdf_path = '/home/daytona/workspace/attachments/bb119b92-6cc4-4892-b882-1eadc171a1d9/physiology practical charts.pdf' output_dir = '/home/daytona/workspace/physio-charts/images' print('Converting PDF to images...') pages = convert_from_path(pdf_path, dpi=150, fmt='jpeg') print(f'Total pages: {len(pages)}') for i, page in enumerate(pages): path = os.path.join(output_dir, f'page_{i+1:03d}.jpg') page.save(path, 'JPEG', quality=85) print('Done converting all pages.') " 2>&1

Killed due to memory. Let me convert in smaller batches with lower DPI.
Running Command

python3 -c " from pdf2image import convert_from_path import os pdf_path = '/home/daytona/workspace/attachments/bb119b92-6cc4-4892-b882-1eadc171a1d9/physiology practical charts.pdf' output_dir = '/home/daytona/workspace/physio-charts/images' os.makedirs(output_dir, exist_ok=True) # Convert 20 pages at a time at lower DPI batch_size = 20 total_pages = 123 for start in range(1, total_pages+1, batch_size): end = min(start + batch_size - 1, total_pages) print(f'Converting pages {start}-{end}...') pages = convert_from_path(pdf_path, dpi=100, fmt='jpeg', first_page=start, last_page=end) for i, page in enumerate(pages): page_num = start + i path = os.path.join(output_dir, f'page_{page_num:03d}.jpg') page.save(path, 'JPEG', quality=75) print(f' Saved {len(pages)} pages.') del pages print('All pages converted.') " 2>&1

All 123 pages converted. Now let me run OCR on all pages to extract text.
Running Command

python3 -c " import pytesseract from PIL import Image import os, json image_dir = '/home/daytona/workspace/physio-charts/images' output_file = '/home/daytona/workspace/physio-charts/ocr_results.json' results = {} files = sorted([f for f in os.listdir(image_dir) if f.endswith('.jpg')]) print(f'Found {len(files)} images') for fname in files: path = os.path.join(image_dir, fname) img = Image.open(path) text = pytesseract.image_to_string(img, config='--psm 6') page_num = int(fname.replace('page_','').replace('.jpg','')) results[page_num] = text.strip() if page_num % 10 == 0: print(f' Done page {page_num}') with open(output_file, 'w') as f: json.dump(results, f, indent=2) print(f'OCR complete. Saved to {output_file}') " 2>&1

Running Command

python3 -c " import json with open('/home/daytona/workspace/physio-charts/ocr_results.json') as f: data = json.load(f) # Print first 30 pages to understand structure for i in range(1, 31): text = data.get(str(i), '') if text: print(f'--- PAGE {i} ---') print(text[:400]) print() else: print(f'--- PAGE {i} --- [empty/no text]') " 2>&1

The OCR output is very garbled - the PDF appears to contain charts/diagrams that are mostly graphical (not text-based), rotated, or in a format that standard OCR cannot read well. The images are likely hand-drawn or printed charts with axis labels. I need to use vision analysis. Let me analyse a spread of pages visually to understand the chart types.
I'll save the images to an accessible location and use analyse_media on a representative sample across the 123 pages.
Running Command

# Make images accessible via a file:// URL - check if we can serve them # Let's look at what the images actually look like by checking file sizes ls -la /home/daytona/workspace/physio-charts/images/ | head -30 echo "..." ls -la /home/daytona/workspace/physio-charts/images/ | tail -20

I need to use analyse_media with the local file paths. The tool requires URLs - let me encode the images as base64 data URIs or serve them. Actually, analyse_media accepts file paths as URLs. Let me try using file:// URLs, or I can use the read_file tool to get images. Actually the best approach is to use the fetch_image tool which can read local files, or encode to base64.
Let me try a different approach - use analyse_media with a file:// URL for a few representative pages to understand the chart types, then use that knowledge plus enhanced OCR to build the full summary.
Running Command

# Try enhanced OCR with image preprocessing and tesseract page segmentation modes # Also try to identify which pages have the most content python3 -c " import os, json with open('/home/daytona/workspace/physio-charts/ocr_results.json') as f: data = json.load(f) # Look at pages 31-60 for variety for i in range(31, 61): text = data.get(str(i), '') if text and len(text) > 50: print(f'--- PAGE {i} (len={len(text)}) ---') print(text[:300]) print() " 2>&1

Page 56 has clear text! It mentions "Properties of cardiac muscle", "Extrasystole", "compensatory pause", "treppe/staircase phenomenon". This tells me these are physiology practical charts with questions and answers. Let me extract all pages that have readable content.
Running Command

python3 -c " import json with open('/home/daytona/workspace/physio-charts/ocr_results.json') as f: data = json.load(f) # Find all pages with meaningful text (>100 chars and contains real words) import re good_pages = [] for pg in range(1, 124): text = data.get(str(pg), '') # Check if text has actual English words words = re.findall(r'[A-Za-z]{3,}', text) if len(words) >= 5 or len(text) > 300: good_pages.append(pg) print(f'Pages with meaningful content: {good_pages}') print(f'Count: {len(good_pages)}') " 2>&1

Running Command

python3 -c " import json, re with open('/home/daytona/workspace/physio-charts/ocr_results.json') as f: data = json.load(f) good_pages = [3, 4, 7, 9, 23, 24, 25, 27, 37, 39, 44, 45, 47, 52, 56, 58, 62, 65, 70, 78, 82, 86, 88, 91, 96, 98, 100, 104, 109, 113, 117, 123] for pg in good_pages: text = data.get(str(pg), '') print(f'=== PAGE {pg} ===') print(text[:600]) print() " 2>&1

I can see page 56 has clear text about cardiac muscle charts. Page 98 appears to have text upside down (it's rotated). Let me use analyse_media on strategically chosen pages - pages with large file sizes or that appear to be the "question pages" to identify what charts are on each spread. I'll analyse 5 representative pages spread across the document.
analyse_media
File URLs don't work. I need to encode images as base64 data URIs. Let me do that for the key pages.
Running Command

python3 -c " import base64 # Encode page 56 as base64 data URI with open('/home/daytona/workspace/physio-charts/images/page_056.jpg', 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() print(f'data:image/jpeg;base64,{b64[:100]}...') print(f'Total length: {len(b64)} chars') " 2>&1

Data URIs are too long (160k chars) and may not work with analyse_media. The images need to be accessible via HTTP. Let me use a different approach - I'll use fetch_image which reads local files, or I'll use the read_file tool to read the image as binary, or better - I'll upload them to a temporary HTTP endpoint.
Actually, fetch_image is for fetching from URLs too. Let me check if there's a way to serve files locally or use base64 data URIs with analyse_media.
The best approach given my constraints is to:
  1. Use the OCR text I already have from the text-readable pages
  2. Use my medical knowledge to fill in chart information based on what I know about physiology practical charts
  3. Since page 56 had clear text about cardiac muscle properties, I can extract all the text from the full page 56 and nearby pages
Let me run a more thorough OCR with image preprocessing on the clearest pages, and also try to read the full text of page 56 and surrounding pages.
Running Command

python3 -c " import pytesseract from PIL import Image, ImageFilter, ImageEnhance import json # Process page 56 with enhanced preprocessing img = Image.open('/home/daytona/workspace/physio-charts/images/page_056.jpg') # Increase contrast and sharpen img = img.convert('L') # Grayscale enhancer = ImageEnhance.Contrast(img) img = enhancer.enhance(2.0) img = img.filter(ImageFilter.SHARPEN) # Try different PSM modes for psm in [3, 4, 6, 11, 12]: text = pytesseract.image_to_string(img, config=f'--psm {psm}') if len(text.strip()) > 100: print(f'=== PSM {psm} ===') print(text[:2000]) print() " 2>&1

Excellent! Image preprocessing + PSM 4 works perfectly! Now I can extract clear text from all pages. Let me run enhanced OCR with image preprocessing on ALL 123 pages.
Running Command

python3 -c " import pytesseract from PIL import Image, ImageFilter, ImageEnhance import json, os image_dir = '/home/daytona/workspace/physio-charts/images' output_file = '/home/daytona/workspace/physio-charts/ocr_enhanced.json' results = {} files = sorted([f for f in os.listdir(image_dir) if f.endswith('.jpg')]) print(f'Processing {len(files)} images with enhanced OCR...') for fname in files: path = os.path.join(image_dir, fname) img = Image.open(path) # Preprocess img_gray = img.convert('L') enhancer = ImageEnhance.Contrast(img_gray) img_enhanced = enhancer.enhance(2.0) img_sharp = img_enhanced.filter(ImageFilter.SHARPEN) # Use PSM 4 (single column) text = pytesseract.image_to_string(img_sharp, config='--psm 4') page_num = int(fname.replace('page_','').replace('.jpg','')) results[page_num] = text.strip() if page_num % 20 == 0: print(f' Done page {page_num}') with open(output_file, 'w') as f: json.dump(results, f, indent=2) print(f'Enhanced OCR complete. Saved to {output_file}') " 2>&1

Running Command

python3 << 'EOF' import json, re with open('/home/daytona/workspace/physio-charts/ocr_enhanced.json') as f: data = json.load(f) # Find all pages with meaningful content (more English words) good_pages = [] for pg in range(1, 124): text = data.get(str(pg), '') words = re.findall(r'[A-Za-z]{4,}', text) if len(words) >= 8: good_pages.append((pg, len(words), len(text))) print(f'Pages with good content: {len(good_pages)}') for pg, wcount, tlen in sorted(good_pages, key=lambda x: -x[1]): print(f' Page {pg}: {wcount} words, {tlen} chars') EOF

Running Command

python3 << 'EOF' import json with open('/home/daytona/workspace/physio-charts/ocr_enhanced.json') as f: data = json.load(f) # Print all pages 55-60 fully and page 98 for pg in [54, 55, 56, 57, 58, 59, 60, 97, 98, 99, 100]: text = data.get(str(pg), '') print(f'=== PAGE {pg} (len={len(text)}) ===') print(text[:2000]) print() EOF

The PDF structure is now clear. The document has alternating pages:
  • Odd-numbered pages: The actual chart/graph (pure image, no extractable text - these are hand-drawn or printed waveform diagrams)
  • Even-numbered pages (like 56): The Q&A text about the preceding chart
Page 55 shows upside-down/rotated text about "sinus venous" (sinoatrial node chart probably), and page 98 has rotated text about respiratory quotient.
The text-heavy pages (Q&A pages) are where the content is, but the OCR only got page 56 properly because the others may be rotated 90 or 180 degrees. Let me try rotating images for those pages.
Running Command

python3 << 'EOF' import pytesseract from PIL import Image, ImageFilter, ImageEnhance import json, re # The Q&A pages seem to occur every ~2 pages # Let's try rotating images for pages that seem to have text but upside down or rotated # Based on OCR showing reversed text (page 55, 98), try 180 degree rotation test_pages = [10, 18, 19, 28, 29, 30, 31, 37, 38, 40, 41, 48, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 98, 100, 105, 110, 115, 120] results_rotated = {} for pg in test_pages: path = f'/home/daytona/workspace/physio-charts/images/page_{pg:03d}.jpg' try: img = Image.open(path) best_text = '' best_words = 0 best_rotation = 0 for rotation in [0, 90, 180, 270]: rotated = img.rotate(rotation, expand=True) gray = rotated.convert('L') enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(2.0) sharp = enhanced.filter(ImageFilter.SHARPEN) text = pytesseract.image_to_string(sharp, config='--psm 4') words = re.findall(r'[A-Za-z]{4,}', text) if len(words) > best_words: best_words = len(words) best_text = text best_rotation = rotation if best_words >= 8: results_rotated[pg] = {'rotation': best_rotation, 'words': best_words, 'text': best_text} print(f'Page {pg}: best at {best_rotation}° ({best_words} words)') print(best_text[:400]) print() except Exception as e: print(f'Page {pg}: error - {e}') # Save results with open('/home/daytona/workspace/physio-charts/ocr_rotated.json', 'w') as f: json.dump(results_rotated, f, indent=2) EOF

Excellent! Now I have a clear picture of the structure. The PDF contains physiology practical exam charts. Let me do a comprehensive sweep of all pages with rotation detection to extract all the Q&A pages.
Running Command

python3 << 'EOF' import pytesseract from PIL import Image, ImageFilter, ImageEnhance import json, re all_results = {} for pg in range(1, 124): path = f'/home/daytona/workspace/physio-charts/images/page_{pg:03d}.jpg' try: img = Image.open(path) best_text = '' best_words = 0 best_rotation = 0 for rotation in [0, 90, 180, 270]: rotated = img.rotate(rotation, expand=True) gray = rotated.convert('L') enhancer = ImageEnhance.Contrast(gray) enhanced = enhancer.enhance(2.0) sharp = enhanced.filter(ImageFilter.SHARPEN) text = pytesseract.image_to_string(sharp, config='--psm 4') words = re.findall(r'[A-Za-z]{4,}', text) if len(words) > best_words: best_words = len(words) best_text = text best_rotation = rotation all_results[pg] = {'rotation': best_rotation, 'words': best_words, 'text': best_text} if pg % 20 == 0: print(f'Done page {pg}') except Exception as e: all_results[pg] = {'rotation': 0, 'words': 0, 'text': '', 'error': str(e)} with open('/home/daytona/workspace/physio-charts/ocr_all_rotated.json', 'w') as f: json.dump(all_results, f, indent=2) print('Done! Printing pages with >=8 words:') for pg in range(1, 124): r = all_results.get(pg, {}) if r.get('words', 0) >= 8: print(f' Page {pg}: rotation={r["rotation"]}°, words={r["words"]}') print(r['text'][:300]) print() EOF

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