cd "/tmp/workspace/attachments/3007035f-434d-4e35-9e7d-5c165c217121/" && python3 -c " import pdfplumber with pdfplumber.open('2025 P2.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) "

python3 -c " from pypdf import PdfReader reader = PdfReader('/tmp/workspace/attachments/3007035f-434d-4e35-9e7d-5c165c217121/2025 P2.pdf') print(f'Pages: {len(reader.pages)}') # Check if there are images on each page for i, page in enumerate(reader.pages[:5]): imgs = page.images print(f'Page {i+1}: {len(imgs)} images') "

python3 -c " from pypdf import PdfReader import os reader = PdfReader('/tmp/workspace/attachments/3007035f-434d-4e35-9e7d-5c165c217121/2025 P2.pdf') os.makedirs('/tmp/workspace/cms_pages', exist_ok=True) for i, page in enumerate(reader.pages): imgs = page.images if imgs: img = imgs[0] ext = img.name.split('.')[-1].lower() if '.' in img.name else 'png' out_path = f'/tmp/workspace/cms_pages/page_{i+1:02d}.{ext}' with open(out_path, 'wb') as f: f.write(img.data) print(f'Saved page {i+1} -> {out_path}') "

You are analyzing a UPSC CMS (Combined Medical Services) exam paper - Paper 2 from 2025. The paper has been extracted as 28 JPG images saved at /tmp/workspace/cms_pages/page_01.jpg through page_28.jpg. YOUR TASK: 1. Read ALL 28 pages using the analyse_media tool. For each page, use fetch_image with the file path like: file:///tmp/workspace/cms_pages/page_01.jpg — OR use analyse_media with the URL format. Actually, use read_file to read each image as bytes, or better yet: the images are saved locally, use analyse_media with the local file path formatted as a URL: use the path directly. IMPORTANT: To analyse local images, use the analyse_media tool with the URL parameter set to the local path prefixed with "file://" — example: analyse_media(media_url="file:///tmp/workspace/cms_pages/page_01.jpg", focus="Extract ALL question text verbatim, including question numbers, question stem, and all answer options (A, B, C, D)") 2. For EACH page, extract ALL visible MCQ questions with their full text and answer options. 3. After extracting all questions, identify ONLY the questions that are related to HEMATOLOGY from the subject of PATHOLOGY. Hematology topics in pathology include (but are not limited to): - Anemias (iron deficiency, megaloblastic, hemolytic, aplastic, sickle cell, thalassemia) - Leukemias (ALL, AML, CLL, CML) - Lymphomas (Hodgkin, Non-Hodgkin) - Bleeding disorders (hemophilia, ITP, TTP, DIC) - Coagulation disorders - Polycythemia vera - Myeloma / plasma cell disorders - Myelodysplastic syndromes - Blood cell morphology / peripheral smear findings - Bone marrow findings - Erythrocyte sedimentation rate, CBC interpretation - Thrombocytopenia - Splenomegaly in hematological conditions - Reed-Sternberg cells - Auer rods - Any question about blood cells, hemoglobin, clotting factors 4. For each identified hematology question, provide: - Question number - Full question text - All answer options (A, B, C, D) - The specific hematology topic it belongs to (e.g., "Iron deficiency anemia", "CML", "Hodgkin lymphoma", etc.) Return a comprehensive structured report with: - Total questions found in the paper - List of all hematology-related pathology questions with full text, options, and topic classification - Page number where each question was found Be thorough - read every single page.

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

python3 -c "from PIL import Image; img = Image.open('/tmp/workspace/cms_pages/page_01.jpg'); print(img.size, img.mode)"

which tesseract && tesseract --version 2>&1 | head -5

tesseract --version 2>&1; pytesseract --version 2>&1; python3 -c "import pytesseract; print('pytesseract available')" 2>&1

python3 -c "import cv2; print('opencv available')" 2>&1; python3 -c "import easyocr; print('easyocr available')" 2>&1

python3 -c "import anthropic; print('anthropic available')" 2>&1; python3 -c "import openai; print('openai available')" 2>&1; python3 -c "import base64; print('base64 available')" 2>&1

python3 -c " import openai import base64 import os # Read image and encode to base64 with open('/tmp/workspace/cms_pages/page_01.jpg', 'rb') as f: img_data = base64.b64encode(f.read()).decode('utf-8') client = openai.OpenAI() response = client.chat.completions.create( model='gpt-4o', messages=[ { 'role': 'user', 'content': [ { 'type': 'text', 'text': 'Extract ALL question text verbatim from this UPSC CMS medical exam page. Include question numbers, full question stems, and all answer options (A, B, C, D). Be thorough and exact.' }, { 'type': 'image_url', 'image_url': { 'url': f'data:image/jpeg;base64,{img_data}' } } ] } ], max_tokens=4096 ) print(response.choices[0].message.content) " 2>&1 | head -100

ls /tmp/workspace/cms_pages/ | head -5 && ls /tmp/workspace/ 2>&1 | head -20

python3 -c " # Try to use the fetch_image approach - convert to smaller size for inline reading from PIL import Image import base64, io img = Image.open('/tmp/workspace/cms_pages/page_01.jpg') # Resize to smaller for processing img_resized = img.resize((1200, 1570), Image.LANCZOS) buf = io.BytesIO() img_resized.save(buf, format='JPEG', quality=85) size = len(buf.getvalue()) print(f'Resized image size: {size} bytes ({size/1024:.1f} KB)') " 2>&1

python3 -c " from PIL import Image import io, os # Resize all 28 images to be under 500KB for reading os.makedirs('/tmp/workspace/cms_small', exist_ok=True) for i in range(1, 29): fname = f'/tmp/workspace/cms_pages/page_{i:02d}.jpg' out = f'/tmp/workspace/cms_small/page_{i:02d}.jpg' img = Image.open(fname) img_resized = img.resize((1215, 1587), Image.LANCZOS) img_resized.save(out, format='JPEG', quality=80) size = os.path.getsize(out) print(f'page_{i:02d}.jpg: {size} bytes ({size/1024:.1f} KB)') " 2>&1