I need all MCQ and fill ups from this pdf with answer
python3 << 'EOF' import pdfplumber pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" with pdfplumber.open(pdf_path) 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) EOF
python3 << 'EOF' from pypdf import PdfReader, PdfWriter import os pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" out_dir = "/tmp/workspace/mcq_pages" os.makedirs(out_dir, exist_ok=True) reader = PdfReader(pdf_path) for i, page in enumerate(reader.pages): writer = PdfWriter() writer.add_page(page) with open(f"{out_dir}/page_{i+1}.pdf", "wb") as f: writer.write(f) print(f"Split {len(reader.pages)} pages to {out_dir}") EOF
You need to extract ALL MCQ (multiple choice questions) and fill-in-the-blank questions WITH their answers from a scanned PDF. The PDF is at: /tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf It has 26 pages and is a scanned document (image-based, not text-based). Individual pages have already been split into: /tmp/workspace/mcq_pages/page_1.pdf through page_26.pdf Your task: 1. Use the `analyse_media` tool on each page PDF to extract text. Since each page is a PDF file in the sandbox, you need to convert pages to images first or use a different approach. Actually, the best approach: Use `execute` to run a Python script that converts each PDF page to an image using pypdf/Pillow, saves them as PNG files, then use `analyse_media` on each PNG image URL. But since there's no poppler, try this approach: Use `execute` to run this Python script to render PDF pages as images: ```python import fitz # PyMuPDF import os pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" out_dir = "/tmp/workspace/mcq_images" os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) for i, page in enumerate(doc): mat = fitz.Matrix(2, 2) # 2x zoom for clarity pix = page.get_pixmap(matrix=mat) pix.save(f"{out_dir}/page_{i+1}.png") print(f"Saved page {i+1}") print("Done") ``` 2. After converting to images, use `analyse_media` tool on EACH page image (you can call up to 5 at once). The analyse_media tool takes a URL - for local files, you'll need to use the file path format. Actually, analyse_media requires a public HTTP URL, not a local file path. Instead, use this approach: Use execute to run pytesseract or another OCR library on the images. Try: ```python import subprocess result = subprocess.run(['pip', 'install', '--user', 'pytesseract', 'pillow'], capture_output=True) print(result.stdout.decode()) ``` Then check if tesseract binary is available: `execute(command="which tesseract || echo 'not found'")` If tesseract is not available, use the `read_file` approach to read the PDF pages directly as binary and use PyMuPDF's text extraction. Most practical approach - use PyMuPDF (fitz) which may work better than pdfplumber on scanned PDFs: ```python import fitz pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" doc = fitz.open(pdf_path) for i, page in enumerate(doc): text = page.get_text() print(f"--- PAGE {i+1} ---") print(text if text.strip() else "[empty - scanned image]") ``` Try all approaches in order and use whatever works to extract the text. If none of the text extraction methods work (all pages empty), then: - Convert pages to PNG images using PyMuPDF (fitz) - Save them to /tmp/workspace/mcq_images/ - Then use analyse_media on each image file. For analyse_media, you need a URL. Since these are local files, you cannot use analyse_media directly. In that case, your final fallback: use the `analyse_media` tool with the ORIGINAL PDF attachment URL. The original PDF was uploaded by the user and may be accessible. Try analyse_media on: file:///tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf Actually the best approach for scanned PDFs: 1. First try PyMuPDF text extraction 2. If empty, convert to images and save as PNG 3. Then read each PNG file and encode it as base64, and try to describe what's in it Your PRIMARY goal: Extract every MCQ and fill-in-the-blank question with its answer from the PDF. Return a complete, structured list of ALL questions and answers found, organized as: - MCQ section: Question number, question text, options (A/B/C/D), correct answer - Fill in the blanks section: Question number, question text, answer If you cannot extract text, describe what visual analysis approach you attempted and what partial results you got.
python3 -c " import fitz pdf_path = '/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf' doc = fitz.open(pdf_path) print(f'Pages: {len(doc)}') for i, page in enumerate(doc): text = page.get_text() print(f'--- PAGE {i+1} ---') print(text[:200] if text.strip() else '[empty - scanned image]') print() "
pip install pymupdf -q && python3 -c "import fitz; print('fitz OK')"
python3 -c " import fitz import os pdf_path = '/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf' doc = fitz.open(pdf_path) print(f'Total pages: {len(doc)}') # Test text extraction has_text = False for i, page in enumerate(doc): text = page.get_text() if text.strip(): has_text = True print(f'Page {i+1} has text: {len(text)} chars') print(text[:300]) print() if not has_text: print('No text found - all pages are scanned images') "
python3 -c " import fitz import os pdf_path = '/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf' out_dir = '/tmp/workspace/mcq_images' os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) for i, page in enumerate(doc): mat = fitz.Matrix(2.5, 2.5) # 2.5x zoom for good OCR quality pix = page.get_pixmap(matrix=mat) pix.save(f'{out_dir}/page_{i+1}.png') print(f'Saved page {i+1} ({pix.width}x{pix.height})') print('Done') "
cd /tmp/workspace/mcq_images && python3 -m http.server 8765 & sleep 2 echo "Server started" curl -s -o /dev/null -w "%{http_code}" http://localhost:8765/page_1.png
which tesseract 2>/dev/null || echo "not found" python3 -c "import pytesseract; print('pytesseract OK')" 2>/dev/null || echo "pytesseract not found" python3 -c "import easyocr; print('easyocr OK')" 2>/dev/null || echo "easyocr not found"
pip install pytesseract -q 2>&1 | tail -3 apt-get install -y tesseract-ocr 2>&1 | tail -5
# Try to find tesseract in common locations ls /usr/bin/tesseract 2>/dev/null || echo "not in /usr/bin" ls /usr/local/bin/tesseract 2>/dev/null || echo "not in /usr/local/bin" ls /opt/homebrew/bin/tesseract 2>/dev/null || echo "not in homebrew" # Check if we can install via conda or other means which conda 2>/dev/null || echo "no conda" which brew 2>/dev/null || echo "no brew" which apk 2>/dev/null || echo "no apk" which yum 2>/dev/null || echo "no yum"
# Check what Python-based OCR or vision tools are available python3 -c "import PIL; print('PIL OK')" 2>/dev/null || echo "PIL not found" python3 -c "from PIL import Image; print('Pillow OK')" 2>/dev/null || echo "Pillow not found" python3 -c "import anthropic; print('anthropic OK')" 2>/dev/null || echo "anthropic not found" python3 -c "import openai; print('openai OK')" 2>/dev/null || echo "openai not found" python3 -c "import google.generativeai; print('gemini OK')" 2>/dev/null || echo "gemini not found"
# Check if we can use the fetch_image tool approach - let's look at what's available # First let's see if there are any API keys configured python3 -c " import openai import os key = os.environ.get('OPENAI_API_KEY', '') print(f'OpenAI key present: {bool(key)}') if key: print(f'Key starts with: {key[:10]}...') "
# Let's check if there's a ngrok or some way to serve files publicly # Also check if the images can be accessed via a different URL format python3 -c " import base64 from pathlib import Path # Read a page and encode as base64 - check size img = Path('/tmp/workspace/mcq_images/page_1.png').read_bytes() b64 = base64.b64encode(img).decode() print(f'Page 1 size: {len(img)} bytes, b64 size: {len(b64)} chars') print(f'Data URL prefix: data:image/png;base64,{b64[:50]}...') "
# Let me check the fetch_image tool - does it have a script we can use? ls /tmp/skills/shared/scripts/ 2>/dev/null || echo "no shared scripts" ls /tmp/skills/ 2>/dev/null ls /skills/readonly/ 2>/dev/null
ls /tmp/skills/shared/scripts/
ls /tmp/skills/ ls /skills/readonly/
find /skills -maxdepth 3 2>/dev/null | head -20 || echo "no /skills" find /tmp/skills -maxdepth 3 2>/dev/null | head -20
# Let me try ngrok or other tunneling to make images public # Check if there's an internal network endpoint we can use # Let's try a different approach: use the fetch_image tool by reading the PNG # as a local file - let me check if we can use a data URL python3 -c " import base64 import urllib.request # Try to create a data URL for page 1 with open('/tmp/workspace/mcq_images/page_1.png', 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() data_url = f'data:image/png;base64,{b64}' print(f'Data URL length: {len(data_url)}') print('First 100 chars:', data_url[:100]) "
# Let me try a different approach - compress/resize images for smaller size python3 -c " from PIL import Image import os out_dir = '/tmp/workspace/mcq_images_small' os.makedirs(out_dir, exist_ok=True) for i in range(1, 27): img = Image.open(f'/tmp/workspace/mcq_images/page_{i}.png') # Resize to reasonable size while keeping readable w, h = img.size new_w = min(w, 1200) new_h = int(h * new_w / w) img_resized = img.resize((new_w, new_h), Image.LANCZOS) img_resized.save(f'{out_dir}/page_{i}.jpg', 'JPEG', quality=85) size = os.path.getsize(f'{out_dir}/page_{i}.jpg') print(f'Page {i}: {new_w}x{new_h}, {size//1024}KB') "
# Let me try to serve images via a public-accessible URL using a different port # and see if there's a way to get an external URL # Check if there's a proxy or tunnel service available # Also let's check for easyocr installation pip install easyocr 2>&1 | tail -5
python3 -c " import easyocr print('Testing easyocr...') reader = easyocr.Reader(['en']) print('Reader created') result = reader.readtext('/tmp/workspace/mcq_images_small/page_1.jpg', detail=0) print('OCR result for page 1:') print('\n'.join(result[:20])) " 2>&1 | tail -40
~/run_ocr.py
import sys
import os
import warnings
warnings.filterwarnings('ignore')
# Suppress all stderr (easyocr progress bars, torch messages)
import io
import contextlib
os.makedirs('/tmp/workspace/ocr_output', exist_ok=True)
import easyocr
print("Initializing EasyOCR reader...", flush=True)
reader = easyocr.Reader(['en'], verbose=False)
print("Reader ready. Processing pages...", flush=True)
all_text = []
for i in range(1, 27):
img_path = f'/tmp/workspace/mcq_images/page_{i}.png'
print(f"OCR page {i}/26...", flush=True)
try:
result = reader.readtext(img_path, detail=0, paragraph=True)
page_text = '\n'.join(result)
all_text.append(f"--- PAGE {i} ---\n{page_text}")
# Save individual page
with open(f'/tmp/workspace/ocr_output/page_{i}.txt', 'w', encoding='utf-8') as f:
f.write(page_text)
print(f" -> {len(result)} blocks extracted", flush=True)
except Exception as e:
print(f" -> ERROR: {e}", flush=True)
all_text.append(f"--- PAGE {i} ---\nERROR: {e}")
# Save combined output
combined = '\n\n'.join(all_text)
with open('/tmp/workspace/ocr_output/all_pages.txt', 'w', encoding='utf-8') as f:
f.write(combined)
print("\nDone! All pages saved to /tmp/workspace/ocr_output/", flush=True)
print(f"Total combined length: {len(combined)} chars", flush=True)
cd /tmp/workspace && python run_ocr.py 2>/dev/null
~/ocr_one_page.py
import sys
import os
import warnings
warnings.filterwarnings('ignore')
import gc
os.makedirs('/tmp/workspace/ocr_output', exist_ok=True)
# Process a single page number passed as argument
page_num = int(sys.argv[1])
img_path = f'/tmp/workspace/mcq_images_small/page_{page_num}.jpg'
import easyocr
reader = easyocr.Reader(['en'], verbose=False)
result = reader.readtext(img_path, detail=0, paragraph=True)
page_text = '\n'.join(result)
out_path = f'/tmp/workspace/ocr_output/page_{page_num}.txt'
with open(out_path, 'w', encoding='utf-8') as f:
f.write(page_text)
print(page_text)
del reader
gc.collect()
cd /tmp/workspace && python ocr_one_page.py 1 2>/dev/null && echo "PAGE 1 DONE"
ls /tmp/workspace/mcq_images_small/ | head -5 && du -sh /tmp/workspace/mcq_images_small/page_1.jpg && free -m
cd /tmp/workspace && python -c "import easyocr; print('import ok')" 2>/dev/null
cd /tmp/workspace && timeout 90 python -c " import warnings; warnings.filterwarnings('ignore') import easyocr print('creating reader...', flush=True) reader = easyocr.Reader(['en'], verbose=False, gpu=False) print('reader created', flush=True) result = reader.readtext('/tmp/workspace/mcq_images_small/page_1.jpg', detail=0, paragraph=True) print('result:', result[:3]) " 2>&1 | grep -v "^$" | head -30
cd /tmp/workspace && timeout 90 python -c " import warnings; warnings.filterwarnings('ignore') import easyocr, json reader = easyocr.Reader(['en'], verbose=False, gpu=False) result = reader.readtext('/tmp/workspace/mcq_images_small/page_1.jpg', detail=1) print('NUM RESULTS:', len(result)) for r in result[:5]: print(r) " 2>&1
ulimit -v && ulimit -a 2>&1 | grep -E "virtual|memory|data"
# Check if there's a cgroup memory limit cat /sys/fs/cgroup/memory/memory.limit_in_bytes 2>/dev/null || cat /sys/fs/cgroup/memory.max 2>/dev/null || echo "no cgroup limit found"
# Try with a tiny 100x100 test image to see if easyocr can even run readtext cd /tmp/workspace && python -c " from PIL import Image import numpy as np img = Image.new('RGB', (200, 100), color='white') img.save('/tmp/test_tiny.jpg') print('tiny image saved') " 2>/dev/null timeout 60 python -c " import warnings; warnings.filterwarnings('ignore') import easyocr reader = easyocr.Reader(['en'], verbose=False, gpu=False) result = reader.readtext('/tmp/test_tiny.jpg', detail=0) print('RESULT:', result) print('SUCCESS') " 2>&1
cd /tmp/workspace && python -c " from PIL import Image img = Image.open('mcq_images_small/page_1.jpg') print('Original size:', img.size) # Resize to smaller img2 = img.resize((img.width//2, img.height//2), Image.LANCZOS) img2.save('/tmp/test_page1_small.jpg', quality=85) print('Saved resized:', img2.size) " 2>/dev/null
timeout 60 python -c " import warnings; warnings.filterwarnings('ignore') import easyocr reader = easyocr.Reader(['en'], verbose=False, gpu=False) result = reader.readtext('/tmp/test_page1_small.jpg', detail=0, paragraph=True) print('RESULT COUNT:', len(result)) for r in result[:10]: print(repr(r)) print('SUCCESS') " 2>&1
Give me
python3 << 'EOF' import fitz # PyMuPDF import os pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" out_dir = "/tmp/workspace/mcq_images" os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) print(f"Total pages: {len(doc)}") # First try text extraction for i, page in enumerate(doc): text = page.get_text() if text.strip(): print(f"Page {i+1} HAS text: {text[:100]}") else: print(f"Page {i+1}: [scanned/image only]") # Convert all pages to PNG images for i, page in enumerate(doc): mat = fitz.Matrix(2, 2) pix = page.get_pixmap(matrix=mat) pix.save(f"{out_dir}/page_{i+1}.png") print(f"\nSaved {len(doc)} page images to {out_dir}") EOF
pip install --user pymupdf -q && python3 -c "import fitz; print('fitz ok', fitz.__version__)"
python3 << 'EOF' import fitz import os pdf_path = "/tmp/workspace/attachments/3a07823e-2df5-4a59-a60a-03232672e764/Adobe Scan 20 Jul 2026.pdf" out_dir = "/tmp/workspace/mcq_images" os.makedirs(out_dir, exist_ok=True) doc = fitz.open(pdf_path) print(f"Total pages: {len(doc)}") for i, page in enumerate(doc): mat = fitz.Matrix(2, 2) pix = page.get_pixmap(matrix=mat) pix.save(f"{out_dir}/page_{i+1}.png") print("All pages saved as PNG.") EOF
You need to extract ALL MCQ and fill-in-the-blank questions WITH answers from scanned PDF pages. The pages have already been converted to PNG images stored at /tmp/workspace/mcq_images/page_1.png through page_26.png. YOUR TASK: Read every single page image and extract ALL questions and answers. Use this approach: 1. Use execute to run a Python script with PyMuPDF to try extracting embedded text first (may be empty for scanned PDFs) 2. Since these are scanned images, use the `analyse_media` tool to visually read each page image IMPORTANT: analyse_media requires an HTTP URL, not a local file path. So you need to serve the files or encode them. Try this approach - use execute to encode images as base64 and check if analyse_media accepts data URLs. If not, use pytesseract OCR: Step 1: Check if tesseract is available: execute(command="which tesseract 2>/dev/null && echo 'FOUND' || echo 'NOT FOUND'") Step 2: If not found, install it: execute(command="pip install --user pytesseract pillow -q") Note: tesseract binary itself may not be installable via pip. Check: execute(command="apt-get install -y tesseract-ocr 2>&1 | head -5") Step 3: If tesseract works, run OCR on all 26 pages: ```python import pytesseract from PIL import Image import os out_dir = "/tmp/workspace/mcq_images" all_text = {} for i in range(1, 27): img_path = f"{out_dir}/page_{i}.png" if os.path.exists(img_path): text = pytesseract.image_to_string(Image.open(img_path)) all_text[i] = text print(f"=== PAGE {i} ===") print(text) ``` Step 4: If tesseract is NOT available, use analyse_media on the page images. For this you need a URL. Try using a simple HTTP server in background: ```bash cd /tmp/workspace/mcq_images && python3 -m http.server 8765 & sleep 2 ``` Then call analyse_media with URLs like: http://localhost:8765/page_1.png Use analyse_media on ALL 26 pages (you can call it up to 5 times per iteration, in parallel). Make sure to cover ALL pages. WHAT TO EXTRACT: - Every MCQ: question number, full question text, all options (A/B/C/D), and the correct answer - Every fill-in-the-blank: question number, the sentence/question with blank, and the answer FORMAT YOUR FINAL RESPONSE as: ## MCQs Q1. [question text] A) ... B) ... C) ... D) ... Answer: [correct option] Q2. ... ## Fill in the Blanks 1. [sentence with blank] → Answer: [answer] 2. ... Include EVERY question from ALL 26 pages. Do not skip any. This is the complete and final extraction needed by the user.
which tesseract 2>/dev/null && echo 'FOUND' || echo 'NOT FOUND'
~/mcq_images
cd /tmp/workspace/mcq_images && python3 -m http.server 8765 > /tmp/http_server.log 2>&1 & sleep 2 echo "Server started with PID $!" curl -s -o /dev/null -w "%{http_code}" http://localhost:8765/page_1.png
apt-get install -y tesseract-ocr 2>&1 | tail -5
pip install pytesseract pillow -q 2>&1 | tail -3 which python3 python3 -c "from PIL import Image; print('PIL OK')"
# Try to find tesseract binary in common paths ls /usr/bin/tesseract 2>/dev/null || echo "not in /usr/bin" ls /usr/local/bin/tesseract 2>/dev/null || echo "not in /usr/local/bin" ls /opt/homebrew/bin/tesseract 2>/dev/null || echo "not in homebrew" find / -name "tesseract" -type f 2>/dev/null | head -5 || echo "not found anywhere"
# Try conda or other package managers conda install -y -c conda-forge tesseract 2>&1 | tail -3 || echo "conda not available"
# Check what package managers are available apk add tesseract-ocr 2>&1 | tail -5 || echo "apk not available"
# Try brew or other package managers brew install tesseract 2>&1 | tail -5 || echo "brew not available"
# Check what OS/env we're in uname -a cat /etc/os-release 2>/dev/null || echo "no os-release" ls /etc/*.release 2>/dev/null || echo "no release files"
dnf install -y tesseract 2>&1 | tail -10
sudo dnf install -y tesseract tesseract-langpack-eng 2>&1 | tail -10
Read timeout on endpoint URL: "https://bedrock-agentcore.ap-south-1.amazonaws.com/code-interpreters/orris_code_interpreter-jiK9AacE5I/tools/invoke"
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