cd /home/daytona/workspace/attachments/3b00e393-04be-4055-8d78-6e3f16805eb8/ && python3 -c " import pdfplumber # Extract PYQ PDF with pdfplumber.open('ptsr.pdf') as pdf: print(f'PYQ PDF Pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages[:5]): print(f'--- PAGE {i+1} ---') print(page.extract_text()) "

cd /home/daytona/workspace/attachments/3b00e393-04be-4055-8d78-6e3f16805eb8/ && python3 -c " import subprocess # Try using pdftotext first result = subprocess.run(['pdftotext', '-layout', 'ptsr.pdf', '-'], capture_output=True, text=True) print('STDOUT length:', len(result.stdout)) print('First 3000 chars:') print(result.stdout[:3000]) "

cd /home/daytona/workspace/attachments/3b00e393-04be-4055-8d78-6e3f16805eb8/ && python3 -c " # Try OCR with pytesseract import subprocess # Convert first few pages to images and OCR result = subprocess.run(['pdftoppm', '-r', '200', '-png', 'ptsr.pdf', '/tmp/ptsr_page'], capture_output=True, text=True) print('pdftoppm exit:', result.returncode) print('stderr:', result.stderr[:200]) import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) print('Generated files:', files[:10]) "

python3 -c " import pytesseract from PIL import Image import os # OCR all pages of PYQ PDF files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') print(f'Processed {f}: {len(text)} chars') full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr.txt', 'w') as out: out.write(full_text) print('Done. Total chars:', len(full_text)) "

python3 -c " import pytesseract from PIL import Image import os # OCR first 5 pages only to check content files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')])[:5] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) print(f'=== {f} ===') print(text[:1500]) print() " 2>&1

python3 -c " import pytesseract from PIL import Image import os, subprocess # First generate syllabus images result = subprocess.run(['pdftoppm', '-r', '200', '-png', '/home/daytona/workspace/attachments/3b00e393-04be-4055-8d78-6e3f16805eb8/5. NCISM_IIIBAMS_AyUG-PS.pdf', '/tmp/syllabus_page'], capture_output=True, text=True) print('Syllabus pdftoppm exit:', result.returncode) files = sorted([f for f in os.listdir('/tmp/') if f.startswith('syllabus_page')]) print('Syllabus pages:', len(files), files[:5]) " 2>&1

ls /tmp/syllabus_page* 2>/dev/null | head -5; echo "---"; ls /tmp/ptsr_page* 2>/dev/null | wc -l

ls /tmp/syllabus_page* | wc -l

You need to OCR all PYQ pages and syllabus pages, then analyze and map questions to syllabus topics. ## Step 1: OCR the PYQ pages (46 pages already converted to PNG at /tmp/ptsr_page-01.png to /tmp/ptsr_page-46.png) Run this Python script: ```python import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr.txt', 'w') as out: out.write(full_text) print('Done. Total chars:', len(full_text)) ``` Run this with execute tool with timeout=180. Process pages in batches of 10 if needed. ## Step 2: OCR the Syllabus pages (90 pages at /tmp/syllabus_page-001.png to /tmp/syllabus_page-090.png) Run similar script but only extract pages that contain "Prasuti" OR "Stri Rog" OR "PRASUTI" content. Check the first 30 pages first to find relevant ones. ```python import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('syllabus_page')]) all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') full_text = '\n'.join(all_text) with open('/tmp/syllabus_ocr.txt', 'w') as out: out.write(full_text) print('Done. Total chars:', len(full_text)) ``` Run with timeout=300. ## Step 3: Read both output files and analyze After OCR is done, read the files using read_file tool in chunks and: 1. Extract the COMPLETE SYLLABUS for Prasuti Tantra & Stri Rog (Paper I and Paper II) from the NCISM syllabus PDF. List all topics under each unit/section. 2. Extract ALL questions from ALL years of PYQ from ptsr.pdf. For each question note: - Year and exam (e.g., March 2025, April 2024, Sep 2022, etc.) - Paper number (Paper I or Paper II) - The question text - Which syllabus topic it maps to 3. Count how many times each topic/concept has appeared in exams. ## Step 4: Return the following in your final response: ### A) Complete Syllabus organized by Paper/Unit ### B) Topic-wise Important Questions with Repeat Count Format like this: --- **PAPER I - PRASUTI TANTRA** **UNIT: [Unit Name]** | # | Topic/Question | Appeared (Times) | Years | |---|---|---|---| | 1 | [Topic] | 3 | 2025, 2024, 2022 | --- List ALL important questions topic-wise. Mark topics that appeared 3+ times as HIGH PRIORITY, 2 times as MEDIUM PRIORITY, 1 time as SEEN ONCE. Be thorough and include every question from all years. The PDFs contain Gujarat Ayurved University BAMS 3rd Prof exam papers.

ls /tmp/ptsr_page-01.png /tmp/ptsr_page-46.png /tmp/syllabus_page-001.png /tmp/syllabus_page-090.png 2>&1 && echo "Files exist" && ls /tmp/ | grep -E "ptsr_page|syllabus_page" | wc -l

python3 << 'EOF' import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) print(f"Found {len(files)} PYQ files: {files[0]} to {files[-1]}") all_text = [] for i, f in enumerate(files): img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') if (i+1) % 10 == 0: print(f"Processed {i+1}/{len(files)}: {f}") full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr.txt', 'w') as out: out.write(full_text) print('Done. Total chars:', len(full_text)) EOF

python3 << 'EOF' import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) batch = files[0:15] all_text = [] for f in batch: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') print(f"Done: {f}") full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr_part1.txt', 'w') as out: out.write(full_text) print('Part1 done. Chars:', len(full_text)) EOF

python3 << 'EOF' import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) batch = files[15:30] all_text = [] for f in batch: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') print(f"Done: {f}") full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr_part2.txt', 'w') as out: out.write(full_text) print('Part2 done. Chars:', len(full_text)) EOF

cd /home/daytona/workspace/attachments/3b00e393-04be-4055-8d78-6e3f16805eb8/ && ls -la *.pdf 2>/dev/null; ls -la *.PDF 2>/dev/null; ls -la

# Check if previous OCR work is still available from earlier session ls /tmp/ptsr_page* 2>/dev/null | wc -l ls /tmp/syllabus_page* 2>/dev/null | wc -l ls /tmp/ptsr_ocr.txt 2>/dev/null ls /tmp/syllabus_ocr.txt 2>/dev/null

python3 << 'EOF' import pytesseract from PIL import Image import os # OCR PYQ pages - process all 46 files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')]) print(f"Processing {len(files)} PYQ pages...") all_text = [] for i, f in enumerate(files): img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') if (i+1) % 10 == 0: print(f" Done {i+1}/{len(files)}") full_text = '\n'.join(all_text) with open('/tmp/ptsr_ocr.txt', 'w') as out: out.write(full_text) print(f'PYQ OCR done. Total chars: {len(full_text)}') EOF

# Check what got done ls /tmp/ptsr_ocr.txt 2>/dev/null && wc -c /tmp/ptsr_ocr.txt # Try batch approach - only pages 1-15 first python3 << 'EOF' import pytesseract from PIL import Image import os files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')])[:15] all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') with open('/tmp/ptsr_batch1.txt', 'w') as out: out.write('\n'.join(all_text)) print('batch1 done', len(all_text), 'pages') EOF

python3 << 'EOF' import pytesseract from PIL import Image import os # Pages 16-30 files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')])[15:30] all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') with open('/tmp/ptsr_batch2.txt', 'w') as out: out.write('\n'.join(all_text)) print('batch2 done', len(all_text), 'pages') EOF

python3 << 'EOF' import pytesseract from PIL import Image import os # Pages 31-46 files = sorted([f for f in os.listdir('/tmp/') if f.startswith('ptsr_page')])[30:] all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') with open('/tmp/ptsr_batch3.txt', 'w') as out: out.write('\n'.join(all_text)) print('batch3 done', len(all_text), 'pages') EOF

python3 << 'EOF' import pytesseract from PIL import Image import os # Syllabus pages 1-20 files = sorted([f for f in os.listdir('/tmp/') if f.startswith('syllabus_page')])[:20] all_text = [] for f in files: img = Image.open(f'/tmp/{f}') text = pytesseract.image_to_string(img) all_text.append(f'=== {f} ===\n{text}') with open('/tmp/syl_batch1.txt', 'w') as out: out.write('\n'.join(all_text)) print('syl batch1 done') EOF

# Check which syllabus pages mention Prasuti/Stri Rog python3 -c " with open('/tmp/syl_batch1.txt') as f: text = f.read() # Find page markers and check content import re pages = re.split(r'=== syllabus_page-(\d+)\.png ===', text) for i in range(1, len(pages), 2): pg_num = pages[i] content = pages[i+1][:200] if i+1 < len(pages) else '' if any(k in content.upper() for k in ['PRASUTI','STRI','PRASRUTI','PRASA']): print(f'Page {pg_num}: {content[:150]}') print('---') "