I have given u my physio paper 1 syllabus paper pattern and pyq do detail analysis of the whole thing and teach me every topic from these papers give me all types of questions with answers also solve the above papers or just incorporate the questions from the question paper in my question answer bank
cd /tmp/workspace && python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'--- PAGE {i+1} ---') print(text) print() "
python3 -c " from pypdf import PdfReader import base64 reader = PdfReader('/tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF') print(f'Pages: {len(reader.pages)}') # Check if there are images for i, page in enumerate(reader.pages): text = page.extract_text() images = page.images print(f'Page {i+1}: text_len={len(text) if text else 0}, images={len(images)}') "
python3 -c " from pypdf import PdfReader import os reader = PdfReader('/tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF') os.makedirs('/tmp/workspace/physio_pages', exist_ok=True) for i, page in enumerate(reader.pages): for j, img in enumerate(page.images): ext = img.name.split('.')[-1] if '.' in img.name else 'png' out_path = f'/tmp/workspace/physio_pages/page_{i+1:02d}_{j}.{ext}' with open(out_path, 'wb') as f: f.write(img.data) print(f'Saved: {out_path} ({len(img.data)} bytes)') "
python3 -c " # Try OCR using pytesseract or another method import subprocess result = subprocess.run(['python3', '-c', 'import pytesseract; print(pytesseract.get_tesseract_version())'], capture_output=True, text=True) print('pytesseract:', result.stdout, result.stderr) # Try PIL result2 = subprocess.run(['python3', '-c', 'from PIL import Image; img = Image.open(\"/tmp/workspace/physio_pages/page_01_0.jpg\"); print(img.size, img.mode)'], capture_output=True, text=True) print('PIL:', result2.stdout, result2.stderr) "
python3 -c " # Convert images to base64 and check if we can serve them import base64 with open('/tmp/workspace/physio_pages/page_01_0.jpg', 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() print(f'Base64 length: {len(b64)}') print(f'Data URI prefix: data:image/jpeg;base64,{b64[:50]}...') "
python3 -c " # Try to use pdf2image or fitz (PyMuPDF) import subprocess r = subprocess.run(['python3', '-c', 'import fitz; print(fitz.__version__)'], capture_output=True, text=True) print('fitz/PyMuPDF:', r.stdout, r.stderr[:100]) r2 = subprocess.run(['python3', '-c', 'from pdf2image import convert_from_path; print(\"ok\")'], capture_output=True, text=True) print('pdf2image:', r2.stdout, r2.stderr[:100]) "
python3 << 'EOF' from pdf2image import convert_from_path import os, base64 pages = convert_from_path('/tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF', dpi=200) print(f'Converted {len(pages)} pages') os.makedirs('/tmp/workspace/physio_hi', exist_ok=True) for i, page in enumerate(pages): path = f'/tmp/workspace/physio_hi/page_{i+1:02d}.jpg' page.save(path, 'JPEG', quality=85) print(f' Saved page {i+1}: {os.path.getsize(path)} bytes') EOF
You have a physiology exam paper (19 pages as JPEG images) that have been extracted from a PDF. The images are saved at /tmp/workspace/physio_hi/page_01.jpg through page_19.jpg. Your task is to: 1. Use analyse_media tool on EACH page image. The images are local files - you'll need to convert them to base64 data URIs to pass as URLs to analyse_media. Actually, the analyse_media tool needs a URL. So instead, use the fetch_image tool on local file paths like: file:///tmp/workspace/physio_hi/page_01.jpg Actually IMPORTANT: fetch_image and analyse_media may not work with local file paths. Instead, do the following: - Use the execute tool to run Python code that reads each image, converts it to base64, and prints the text content using pytesseract if available - If pytesseract is not available, use the execute tool to run: pip install --user pytesseract and then use it - If that fails, use pdfplumber or pypdf on the original PDF Actually, the BEST approach: 1. First check if there's a way to get text from scanned images 2. Run: python3 -c "import pytesseract; print('ok')" - if it fails, install it 3. Check if tesseract binary is available: execute "which tesseract" 4. If tesseract is available, run OCR on each page image Here's what I know about the files: - Original PDF: /tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF - 19 pages of scanned images (physiology exam paper/PYQ) - High-res JPEGs at /tmp/workspace/physio_hi/page_01.jpg to page_19.jpg STEP 1: Check if tesseract is available STEP 2: If yes, OCR all pages and extract the full text STEP 3: Parse the extracted text to identify: a) Syllabus topics b) Paper pattern/marks distribution c) All exam questions with their marks d) Any instructions STEP 4: Also search the medical library database for the main physiology topics you find. Use query_library_db tool to search for relevant content: - First run: SELECT speciality, book_count FROM v_speciality_summary to see what's available - Then search for physiology topics STEP 5: Return a COMPLETE structured report containing: 1. Full OCR text from all 19 pages (verbatim) 2. Parsed list of ALL exam questions with marks 3. List of all topics covered 4. Paper pattern analysis Return the COMPLETE OCR text - do not summarize, give me everything verbatim from all pages.
which tesseract && tesseract --version && python3 -c "import pytesseract; print('pytesseract ok')"
~/physio_hi
apt-get install -y tesseract-ocr 2>&1 | tail -5
pip install pytesseract pillow 2>&1 | tail -3; python3 -c "from PIL import Image; print('PIL ok')"
ls /tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/
python3 -c " import subprocess result = subprocess.run(['find', '/', '-name', 'tesseract', '-type', 'f'], capture_output=True, text=True, timeout=10) print(result.stdout[:2000]) print(result.stderr[:500]) "
python3 -c " import subprocess # Try easyocr as alternative try: import easyocr print('easyocr available') except: print('easyocr not available') # Try google vision try: from google.cloud import vision print('google vision available') except: print('google vision not available') # Try pdf2image + pytesseract try: import pdf2image print('pdf2image available') except: print('pdf2image not available') # Try pdfplumber try: import pdfplumber print('pdfplumber available') except: print('pdfplumber not available') # Try pymupdf / fitz try: import fitz print('pymupdf/fitz available') except: print('pymupdf/fitz not available') "
python3 -c " import pdfplumber pdf_path = '/tmp/workspace/attachments/d01450e7-fe21-47a7-a6b4-45866f4be38f/physio pyq.PDF' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') # Try first page page = pdf.pages[0] text = page.extract_text() print('Page 1 text:') print(repr(text[:500]) if text else 'No text extracted (likely scanned)') print('---') chars = page.chars print(f'Number of chars on page 1: {len(chars)}') "
# Try installing easyocr pip install easyocr 2>&1 | tail -5
python3 << 'EOF' import easyocr import os reader = easyocr.Reader(['en'], gpu=False) print("EasyOCR reader initialized") # Test on page 1 result = reader.readtext('/tmp/workspace/physio_hi/page_01.jpg', detail=0, paragraph=True) print("Page 1 sample text:") for line in result[:10]: print(line) EOF
python3 -c " from PIL import Image import os # Check image sizes for i in range(1, 4): img = Image.open(f'/tmp/workspace/physio_hi/page_{i:02d}.jpg') print(f'Page {i}: {img.size}, mode={img.mode}') "
/tmp/ocr_pages.py
#!/usr/bin/env python3
"""OCR all physio exam pages using EasyOCR with resized images to avoid OOM."""
import os
import sys
import easyocr
from PIL import Image
# Initialize reader once
print("Initializing EasyOCR reader...", flush=True)
reader = easyocr.Reader(['en'], gpu=False, verbose=False)
print("Reader initialized.", flush=True)
output_dir = '/tmp/workspace/ocr_output'
os.makedirs(output_dir, exist_ok=True)
all_text = {}
for page_num in range(1, 20):
img_path = f'/tmp/workspace/physio_hi/page_{page_num:02d}.jpg'
if not os.path.exists(img_path):
print(f"Page {page_num}: file not found, skipping.", flush=True)
continue
print(f"Processing page {page_num}...", flush=True)
# Resize image to ~800px wide (about half) - grayscale to save memory
img = Image.open(img_path).convert('L')
w, h = img.size
new_w = 800
new_h = int(h * new_w / w)
img_small = img.resize((new_w, new_h), Image.LANCZOS)
small_path = f'/tmp/page_{page_num:02d}_small.jpg'
img_small.save(small_path, quality=85)
try:
results = reader.readtext(small_path, detail=0, paragraph=True)
text = '\n'.join(results)
all_text[page_num] = text
out_file = os.path.join(output_dir, f'page_{page_num:02d}.txt')
with open(out_file, 'w') as f:
f.write(text)
print(f" Page {page_num}: extracted {len(text)} chars, {len(results)} paragraphs", flush=True)
# Clean up small image
os.remove(small_path)
except Exception as e:
print(f" Page {page_num} ERROR: {e}", flush=True)
all_text[page_num] = f"[ERROR: {e}]"
# Save combined output
combined_path = os.path.join(output_dir, 'all_pages.txt')
with open(combined_path, 'w') as f:
for page_num in sorted(all_text.keys()):
f.write(f"\n{'='*60}\n")
f.write(f"PAGE {page_num}\n")
f.write(f"{'='*60}\n\n")
f.write(all_text[page_num])
f.write("\n")
print(f"\nDone! Combined output saved to {combined_path}", flush=True)
timeout 600 python3 /tmp/ocr_pages.py 2>&1
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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