143995b213
- Rename skill to better reflect its purpose (document-to-markdown conversion) - Update SKILL.md name, description, and trigger keywords - Add benchmark reference (2026-03-22) - Update marketplace.json entry (name, skills path, version 2.0.0) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
467 lines
16 KiB
Python
Executable File
467 lines
16 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Quality validator for document-to-markdown conversion.
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Compare original document with converted markdown to assess conversion quality.
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Generates HTML quality report with detailed metrics.
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Usage:
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uv run --with pymupdf scripts/validate_output.py document.pdf output.md
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uv run --with pymupdf scripts/validate_output.py document.pdf output.md --report report.html
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"""
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import argparse
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import html
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import re
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import subprocess
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import sys
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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@dataclass
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class ValidationMetrics:
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"""Quality metrics for conversion validation."""
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# Text metrics
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source_char_count: int = 0
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output_char_count: int = 0
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text_retention: float = 0.0
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# Table metrics
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source_table_count: int = 0
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output_table_count: int = 0
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table_retention: float = 0.0
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# Image metrics
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source_image_count: int = 0
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output_image_count: int = 0
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image_retention: float = 0.0
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# Structure metrics
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heading_count: int = 0
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list_count: int = 0
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code_block_count: int = 0
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# Quality scores
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overall_score: float = 0.0
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status: str = "unknown" # pass, warn, fail
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# Details
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warnings: list[str] = field(default_factory=list)
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errors: list[str] = field(default_factory=list)
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def extract_text_from_pdf(pdf_path: Path) -> tuple[str, int, int]:
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"""Extract text, table count, and image count from PDF."""
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try:
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import fitz # PyMuPDF
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doc = fitz.open(str(pdf_path))
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text_parts = []
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table_count = 0
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image_count = 0
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for page in doc:
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text_parts.append(page.get_text())
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# Count images
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image_count += len(page.get_images())
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# Estimate tables (look for grid-like structures)
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# This is approximate - tables are hard to detect in PDFs
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page_text = page.get_text()
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if re.search(r'(\t.*){2,}', page_text) or '│' in page_text:
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table_count += 1
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doc.close()
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return '\n'.join(text_parts), table_count, image_count
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except ImportError:
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# Fallback to pdftotext if available
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try:
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result = subprocess.run(
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['pdftotext', '-layout', str(pdf_path), '-'],
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capture_output=True,
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text=True,
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timeout=60
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)
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return result.stdout, 0, 0 # Can't count tables/images
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except Exception:
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return "", 0, 0
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def extract_text_from_docx(docx_path: Path) -> tuple[str, int, int]:
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"""Extract text, table count, and image count from DOCX."""
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try:
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import zipfile
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from xml.etree import ElementTree as ET
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with zipfile.ZipFile(docx_path, 'r') as z:
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# Extract main document text
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if 'word/document.xml' not in z.namelist():
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return "", 0, 0
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with z.open('word/document.xml') as f:
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tree = ET.parse(f)
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root = tree.getroot()
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# Extract text
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ns = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}
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text_parts = []
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for t in root.iter('{http://schemas.openxmlformats.org/wordprocessingml/2006/main}t'):
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if t.text:
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text_parts.append(t.text)
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# Count tables
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tables = root.findall('.//w:tbl', ns)
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table_count = len(tables)
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# Count images
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image_count = sum(1 for name in z.namelist()
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if name.startswith('word/media/'))
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return ' '.join(text_parts), table_count, image_count
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except Exception as e:
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return "", 0, 0
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def analyze_markdown(md_path: Path) -> dict:
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"""Analyze markdown file structure and content."""
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content = md_path.read_text()
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# Count tables (markdown tables with |)
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table_lines = [l for l in content.split('\n')
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if re.match(r'^\s*\|.*\|', l)]
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# Group consecutive table lines
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table_count = 0
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in_table = False
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for line in content.split('\n'):
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if re.match(r'^\s*\|.*\|', line):
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if not in_table:
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table_count += 1
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in_table = True
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else:
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in_table = False
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# Count images
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images = re.findall(r'!\[.*?\]\(.*?\)', content)
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# Count headings
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headings = re.findall(r'^#{1,6}\s+.+$', content, re.MULTILINE)
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# Count lists
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list_items = re.findall(r'^[\s]*[-*+]\s+', content, re.MULTILINE)
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list_items += re.findall(r'^[\s]*\d+\.\s+', content, re.MULTILINE)
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# Count code blocks
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code_blocks = re.findall(r'```', content)
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# Clean text for comparison
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clean_text = re.sub(r'```.*?```', '', content, flags=re.DOTALL)
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clean_text = re.sub(r'!\[.*?\]\(.*?\)', '', clean_text)
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clean_text = re.sub(r'\[.*?\]\(.*?\)', '', clean_text)
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clean_text = re.sub(r'[#*_`|>-]', '', clean_text)
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clean_text = re.sub(r'\s+', ' ', clean_text).strip()
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return {
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'char_count': len(clean_text),
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'table_count': table_count,
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'image_count': len(images),
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'heading_count': len(headings),
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'list_count': len(list_items),
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'code_block_count': len(code_blocks) // 2,
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'raw_content': content,
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'clean_text': clean_text
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}
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def validate_conversion(
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source_path: Path,
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output_path: Path
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) -> ValidationMetrics:
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"""Validate conversion quality by comparing source and output."""
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metrics = ValidationMetrics()
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# Analyze output markdown
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md_analysis = analyze_markdown(output_path)
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metrics.output_char_count = md_analysis['char_count']
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metrics.output_table_count = md_analysis['table_count']
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metrics.output_image_count = md_analysis['image_count']
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metrics.heading_count = md_analysis['heading_count']
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metrics.list_count = md_analysis['list_count']
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metrics.code_block_count = md_analysis['code_block_count']
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# Extract source content based on file type
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ext = source_path.suffix.lower()
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if ext == '.pdf':
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source_text, source_tables, source_images = extract_text_from_pdf(source_path)
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elif ext in ['.docx', '.doc']:
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source_text, source_tables, source_images = extract_text_from_docx(source_path)
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else:
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# For other formats, estimate from file size
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source_text = ""
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source_tables = 0
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source_images = 0
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metrics.warnings.append(f"Cannot analyze source format: {ext}")
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metrics.source_char_count = len(source_text.replace(' ', '').replace('\n', ''))
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metrics.source_table_count = source_tables
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metrics.source_image_count = source_images
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# Calculate retention rates
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if metrics.source_char_count > 0:
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# Use ratio of actual/expected, capped at 1.0
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metrics.text_retention = min(
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metrics.output_char_count / metrics.source_char_count,
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1.0
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)
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else:
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metrics.text_retention = 1.0 if metrics.output_char_count > 0 else 0.0
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if metrics.source_table_count > 0:
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metrics.table_retention = min(
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metrics.output_table_count / metrics.source_table_count,
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1.0
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)
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else:
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metrics.table_retention = 1.0 # No tables expected
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if metrics.source_image_count > 0:
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metrics.image_retention = min(
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metrics.output_image_count / metrics.source_image_count,
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1.0
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)
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else:
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metrics.image_retention = 1.0 # No images expected
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# Determine status based on thresholds
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if metrics.text_retention < 0.85:
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metrics.errors.append(f"Low text retention: {metrics.text_retention:.1%}")
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elif metrics.text_retention < 0.95:
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metrics.warnings.append(f"Text retention below optimal: {metrics.text_retention:.1%}")
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if metrics.source_table_count > 0 and metrics.table_retention < 0.9:
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metrics.errors.append(f"Tables missing: {metrics.table_retention:.1%} retained")
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elif metrics.source_table_count > 0 and metrics.table_retention < 1.0:
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metrics.warnings.append(f"Some tables may be incomplete: {metrics.table_retention:.1%}")
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if metrics.source_image_count > 0 and metrics.image_retention < 0.8:
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metrics.errors.append(f"Images missing: {metrics.image_retention:.1%} retained")
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elif metrics.source_image_count > 0 and metrics.image_retention < 1.0:
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metrics.warnings.append(f"Some images missing: {metrics.image_retention:.1%}")
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# Calculate overall score (0-100)
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metrics.overall_score = (
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metrics.text_retention * 50 +
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metrics.table_retention * 25 +
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metrics.image_retention * 25
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) * 100
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# Determine status
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if metrics.errors:
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metrics.status = "fail"
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elif metrics.warnings:
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metrics.status = "warn"
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else:
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metrics.status = "pass"
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return metrics
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def generate_html_report(
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metrics: ValidationMetrics,
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source_path: Path,
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output_path: Path
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) -> str:
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"""Generate HTML quality report."""
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status_colors = {
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"pass": "#28a745",
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"warn": "#ffc107",
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"fail": "#dc3545"
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}
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status_color = status_colors.get(metrics.status, "#6c757d")
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def metric_bar(value: float, thresholds: tuple) -> str:
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"""Generate colored progress bar."""
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pct = int(value * 100)
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if value >= thresholds[0]:
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color = "#28a745" # green
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elif value >= thresholds[1]:
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color = "#ffc107" # yellow
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else:
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color = "#dc3545" # red
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return f'''
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<div style="background: #e9ecef; border-radius: 4px; overflow: hidden; height: 20px;">
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<div style="background: {color}; width: {pct}%; height: 100%; transition: width 0.3s;"></div>
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</div>
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<span style="font-size: 14px; color: #666;">{pct}%</span>
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'''
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report = f'''<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<title>Conversion Quality Report</title>
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<style>
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body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; margin: 40px; background: #f5f5f5; }}
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.container {{ max-width: 800px; margin: 0 auto; background: white; padding: 30px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }}
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h1 {{ color: #333; border-bottom: 2px solid #eee; padding-bottom: 15px; }}
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.status {{ display: inline-block; padding: 8px 16px; border-radius: 4px; color: white; font-weight: bold; }}
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.metric {{ margin: 20px 0; padding: 15px; background: #f8f9fa; border-radius: 4px; }}
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.metric-label {{ font-weight: bold; color: #333; margin-bottom: 8px; }}
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.metric-value {{ font-size: 24px; color: #333; }}
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.issues {{ margin-top: 20px; }}
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.error {{ background: #f8d7da; color: #721c24; padding: 10px; margin: 5px 0; border-radius: 4px; }}
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.warning {{ background: #fff3cd; color: #856404; padding: 10px; margin: 5px 0; border-radius: 4px; }}
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table {{ width: 100%; border-collapse: collapse; margin: 15px 0; }}
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th, td {{ padding: 10px; text-align: left; border-bottom: 1px solid #eee; }}
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th {{ background: #f8f9fa; }}
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.score {{ font-size: 48px; font-weight: bold; color: {status_color}; }}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>📊 Conversion Quality Report</h1>
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<div style="text-align: center; margin: 30px 0;">
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<div class="score">{metrics.overall_score:.0f}</div>
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<div style="color: #666;">Overall Score</div>
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<div class="status" style="background: {status_color}; margin-top: 10px;">
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{metrics.status.upper()}
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</div>
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</div>
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<h2>📄 File Information</h2>
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<table>
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<tr><th>Source</th><td>{html.escape(str(source_path))}</td></tr>
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<tr><th>Output</th><td>{html.escape(str(output_path))}</td></tr>
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</table>
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<h2>📏 Retention Metrics</h2>
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<div class="metric">
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<div class="metric-label">Text Retention (target: >95%)</div>
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{metric_bar(metrics.text_retention, (0.95, 0.85))}
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<div style="font-size: 12px; color: #666; margin-top: 5px;">
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Source: ~{metrics.source_char_count:,} chars | Output: {metrics.output_char_count:,} chars
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</div>
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</div>
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<div class="metric">
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<div class="metric-label">Table Retention (target: 100%)</div>
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{metric_bar(metrics.table_retention, (1.0, 0.9))}
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<div style="font-size: 12px; color: #666; margin-top: 5px;">
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Source: {metrics.source_table_count} tables | Output: {metrics.output_table_count} tables
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</div>
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</div>
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<div class="metric">
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<div class="metric-label">Image Retention (target: 100%)</div>
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{metric_bar(metrics.image_retention, (1.0, 0.8))}
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<div style="font-size: 12px; color: #666; margin-top: 5px;">
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Source: {metrics.source_image_count} images | Output: {metrics.output_image_count} images
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</div>
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</div>
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<h2>📊 Structure Analysis</h2>
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<table>
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<tr><th>Headings</th><td>{metrics.heading_count}</td></tr>
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<tr><th>List Items</th><td>{metrics.list_count}</td></tr>
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<tr><th>Code Blocks</th><td>{metrics.code_block_count}</td></tr>
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</table>
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{'<h2>⚠️ Issues</h2><div class="issues">' + ''.join(f'<div class="error">❌ {html.escape(e)}</div>' for e in metrics.errors) + ''.join(f'<div class="warning">⚠️ {html.escape(w)}</div>' for w in metrics.warnings) + '</div>' if metrics.errors or metrics.warnings else ''}
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<div style="margin-top: 30px; padding-top: 20px; border-top: 1px solid #eee; color: #666; font-size: 12px;">
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Generated by markdown-tools validate_output.py
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</div>
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</div>
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</body>
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</html>
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'''
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return report
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def main():
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parser = argparse.ArgumentParser(
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description="Validate document-to-markdown conversion quality"
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)
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parser.add_argument(
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"source",
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type=Path,
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help="Original document (PDF, DOCX, etc.)"
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)
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parser.add_argument(
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"output",
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type=Path,
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help="Converted markdown file"
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)
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parser.add_argument(
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"--report",
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type=Path,
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help="Generate HTML report at this path"
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)
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parser.add_argument(
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"--json",
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action="store_true",
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help="Output metrics as JSON"
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)
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args = parser.parse_args()
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# Validate inputs
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if not args.source.exists():
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print(f"Error: Source file not found: {args.source}", file=sys.stderr)
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sys.exit(1)
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if not args.output.exists():
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print(f"Error: Output file not found: {args.output}", file=sys.stderr)
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sys.exit(1)
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# Run validation
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metrics = validate_conversion(args.source, args.output)
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# Output results
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if args.json:
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import json
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print(json.dumps({
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'text_retention': metrics.text_retention,
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'table_retention': metrics.table_retention,
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'image_retention': metrics.image_retention,
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'overall_score': metrics.overall_score,
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'status': metrics.status,
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'warnings': metrics.warnings,
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'errors': metrics.errors
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}, indent=2))
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else:
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# Console output
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status_emoji = {"pass": "✅", "warn": "⚠️", "fail": "❌"}.get(metrics.status, "❓")
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print(f"\n{status_emoji} Conversion Quality: {metrics.status.upper()}")
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print(f" Overall Score: {metrics.overall_score:.0f}/100")
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print(f"\n Text Retention: {metrics.text_retention:.1%}")
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print(f" Table Retention: {metrics.table_retention:.1%}")
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print(f" Image Retention: {metrics.image_retention:.1%}")
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if metrics.errors:
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print("\n Errors:")
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for e in metrics.errors:
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print(f" ❌ {e}")
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if metrics.warnings:
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print("\n Warnings:")
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for w in metrics.warnings:
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print(f" ⚠️ {w}")
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# Generate HTML report
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if args.report:
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report_html = generate_html_report(metrics, args.source, args.output)
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args.report.parent.mkdir(parents=True, exist_ok=True)
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args.report.write_text(report_html)
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print(f"\n📊 HTML report: {args.report}")
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# Exit with appropriate code
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sys.exit(0 if metrics.status != "fail" else 1)
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if __name__ == "__main__":
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main()
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