Add quality_analyzer.py — PDF quality scoring for FOIA/CIA filtering
Scans PDF folders and scores each document (0-100) based on: - Text content (word count, coherence, OCR garbage detection) - Font presence (scanned vs text-based) - File size, page count, filename quality - Language detection (Arabic/Russian/Turkish/English) Labels: high (70+), medium (40-69), low (20-39), noise (<20) Outputs JSON + CSV. Can move noise to Arsiv/noise with --move. Usage: --scan, --report, --export-csv, --move [--confirm] Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
salvacybersec
417
quality_analyzer.py
Executable file
417
quality_analyzer.py
Executable file
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#!/usr/bin/env python3
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"""
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PDF Quality Analyzer — Scan FOIA/CIA folders and classify documents.
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Extracts metadata + first 10 pages text for each PDF, scores quality,
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outputs JSON for filtering. No files moved — only analysis.
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Usage:
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python3 quality_analyzer.py --scan # Analyze all
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python3 quality_analyzer.py --scan --folder Istihbarat/CIA
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python3 quality_analyzer.py --scan --folder FOIA/documents
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python3 quality_analyzer.py --report # Show stats from scan
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python3 quality_analyzer.py --export-csv # Export to CSV
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python3 quality_analyzer.py --classify # Add quality labels
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python3 quality_analyzer.py --move # Move noise to Arsiv/noise
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"""
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import argparse
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import csv
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import json
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import os
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import re
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import subprocess
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import sys
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import time
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from pathlib import Path
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BOOKS_ROOT = Path("/mnt/storage/Common/Books")
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OUTPUT_PATH = Path(__file__).parent / "quality_analysis.json"
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CSV_PATH = Path(__file__).parent / "quality_analysis.csv"
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def extract_pdf_metadata(pdf_path):
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"""Extract metadata from a single PDF."""
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result = {
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"path": str(pdf_path),
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"filename": pdf_path.name,
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"folder": str(pdf_path.parent.relative_to(BOOKS_ROOT)),
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"size_kb": pdf_path.stat().st_size // 1024,
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"pages": 0,
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"has_fonts": False,
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"font_count": 0,
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"text_chars": 0,
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"text_words": 0,
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"text_lines": 0,
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"text_sample": "",
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"language": "unknown",
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"quality_score": 0,
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"quality_label": "",
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"issues": [],
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}
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# Page count via pdfinfo
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try:
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r = subprocess.run(["pdfinfo", str(pdf_path)], capture_output=True, text=True, timeout=5)
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for line in r.stdout.splitlines():
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if line.startswith("Pages:"):
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result["pages"] = int(line.split(":")[1].strip())
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break
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except Exception:
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result["issues"].append("pdfinfo_failed")
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# Font detection via pdffonts
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try:
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r = subprocess.run(["pdffonts", "-l", "10", str(pdf_path)],
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capture_output=True, text=True, timeout=5)
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font_lines = [l for l in r.stdout.strip().split("\n") if l.strip()]
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result["font_count"] = max(0, len(font_lines) - 2) # subtract header
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result["has_fonts"] = result["font_count"] > 0
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except Exception:
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result["issues"].append("pdffonts_failed")
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# Text extraction — first 10 pages
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try:
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r = subprocess.run(["pdftotext", "-l", "10", str(pdf_path), "-"],
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capture_output=True, text=True, timeout=15)
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text = r.stdout.strip()
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result["text_chars"] = len(text)
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result["text_words"] = len(text.split())
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result["text_lines"] = len([l for l in text.split("\n") if l.strip()])
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# Keep first 2000 chars as sample
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result["text_sample"] = text[:2000]
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# Language detection (simple heuristic)
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if text:
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arabic_chars = len(re.findall(r'[\u0600-\u06FF]', text))
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cyrillic_chars = len(re.findall(r'[\u0400-\u04FF]', text))
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turkish_chars = len(re.findall(r'[şçğüöıİŞÇĞÜÖ]', text))
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latin_chars = len(re.findall(r'[a-zA-Z]', text))
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total = arabic_chars + cyrillic_chars + turkish_chars + latin_chars
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if total > 0:
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if arabic_chars / total > 0.3:
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result["language"] = "arabic"
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elif cyrillic_chars / total > 0.3:
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result["language"] = "russian"
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elif turkish_chars / max(1, latin_chars) > 0.02:
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result["language"] = "turkish"
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elif latin_chars > 0:
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result["language"] = "english"
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except subprocess.TimeoutExpired:
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result["issues"].append("pdftotext_timeout")
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except Exception:
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result["issues"].append("pdftotext_failed")
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return result
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def score_quality(doc):
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"""Score document quality 0-100."""
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score = 0
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issues = list(doc.get("issues", []))
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# Text content quality (0-40 points)
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words = doc["text_words"]
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if words > 1000:
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score += 40
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elif words > 500:
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score += 30
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elif words > 100:
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score += 20
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elif words > 20:
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score += 10
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else:
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issues.append("very_low_text")
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# Has real fonts (0-15 points)
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if doc["has_fonts"]:
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score += 15
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else:
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issues.append("no_fonts_scanned")
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# File size reasonable (0-10 points)
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size_kb = doc["size_kb"]
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if 50 < size_kb < 50000:
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score += 10
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elif size_kb <= 50:
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issues.append("too_small")
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elif size_kb >= 50000:
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score += 5 # large but might be valuable
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# Page count (0-10 points)
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pages = doc["pages"]
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if pages >= 5:
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score += 10
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elif pages >= 2:
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score += 5
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else:
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issues.append("single_page")
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# Filename quality (0-10 points)
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fname = doc["filename"]
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if len(fname) > 20 and not fname.startswith("00"):
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# Has a real name, not just hash
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if any(c.isalpha() and c.isascii() for c in fname[:20]):
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score += 10
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elif any('\u0600' <= c <= '\u06FF' for c in fname):
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score += 5 # Arabic name, might be ok
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else:
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if re.match(r'^[0-9A-Fa-f]{10,}', fname):
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issues.append("hash_filename")
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# Text coherence — check for OCR garbage (0-15 points)
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sample = doc["text_sample"]
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if sample:
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# Count actual words vs garbage
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real_words = len(re.findall(r'\b[a-zA-Z\u0400-\u04FF\u0600-\u06FF]{3,}\b', sample))
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total_tokens = len(sample.split())
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if total_tokens > 0:
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coherence = real_words / total_tokens
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if coherence > 0.5:
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score += 15
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elif coherence > 0.3:
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score += 10
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elif coherence > 0.1:
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score += 5
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else:
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issues.append("garbled_text")
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doc["quality_score"] = min(100, score)
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doc["issues"] = issues
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return doc
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def classify(doc):
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"""Assign quality label based on score."""
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score = doc["quality_score"]
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if score >= 70:
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doc["quality_label"] = "high"
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elif score >= 40:
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doc["quality_label"] = "medium"
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elif score >= 20:
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doc["quality_label"] = "low"
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else:
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doc["quality_label"] = "noise"
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return doc
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def scan_folder(folder_path, existing=None):
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"""Scan all PDFs in a folder."""
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existing = existing or {}
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pdfs = sorted(folder_path.rglob("*.pdf"))
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total = len(pdfs)
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results = []
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skipped = 0
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print(f"Scanning {folder_path.relative_to(BOOKS_ROOT)}: {total} PDFs")
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for i, pdf in enumerate(pdfs):
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key = str(pdf)
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if key in existing:
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results.append(existing[key])
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skipped += 1
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continue
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if (i + 1) % 100 == 0 or i == 0:
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print(f" [{i+1}/{total}] {pdf.name[:50]}...", flush=True)
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try:
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doc = extract_pdf_metadata(pdf)
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doc = score_quality(doc)
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doc = classify(doc)
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results.append(doc)
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except Exception as e:
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results.append({
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"path": str(pdf),
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"filename": pdf.name,
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"folder": str(pdf.parent.relative_to(BOOKS_ROOT)),
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"size_kb": pdf.stat().st_size // 1024 if pdf.exists() else 0,
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"quality_score": 0,
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"quality_label": "error",
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"issues": [str(e)],
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"text_sample": "",
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})
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print(f" Done: {total - skipped} new, {skipped} cached")
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return results
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def load_existing():
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"""Load existing analysis."""
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if OUTPUT_PATH.exists():
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with open(OUTPUT_PATH) as f:
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data = json.load(f)
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return {d["path"]: d for d in data}
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return {}
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def save_results(results):
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"""Save analysis to JSON (without text_sample for size)."""
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with open(OUTPUT_PATH, "w") as f:
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json.dump(results, f, indent=2, ensure_ascii=False)
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print(f"Saved {len(results)} docs to {OUTPUT_PATH}")
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def print_report(results):
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"""Print quality distribution report."""
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by_folder = {}
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by_label = {"high": 0, "medium": 0, "low": 0, "noise": 0, "error": 0}
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by_lang = {}
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for doc in results:
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folder = doc.get("folder", "?")
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label = doc.get("quality_label", "?")
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lang = doc.get("language", "?")
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by_folder.setdefault(folder, {"high": 0, "medium": 0, "low": 0, "noise": 0, "total": 0})
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by_folder[folder][label] = by_folder[folder].get(label, 0) + 1
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by_folder[folder]["total"] += 1
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by_label[label] = by_label.get(label, 0) + 1
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by_lang[lang] = by_lang.get(lang, 0) + 1
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print(f"\n{'=' * 70}")
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print(f"QUALITY REPORT — {len(results)} documents")
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print(f"{'=' * 70}\n")
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print("Overall distribution:")
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for label in ["high", "medium", "low", "noise", "error"]:
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count = by_label.get(label, 0)
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pct = count * 100 // max(1, len(results))
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bar = "█" * (pct // 2)
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print(f" {label:<8} {count:>6} ({pct:>2}%) {bar}")
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print(f"\nLanguage distribution:")
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for lang, count in sorted(by_lang.items(), key=lambda x: -x[1]):
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print(f" {lang:<12} {count:>6}")
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print(f"\nPer folder:")
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print(f" {'Folder':<40} {'Total':>6} {'High':>6} {'Med':>6} {'Low':>6} {'Noise':>6}")
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print(f" {'-' * 72}")
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for folder, counts in sorted(by_folder.items(), key=lambda x: -x[1]["total"]):
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print(f" {folder:<40} {counts['total']:>6} "
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f"{counts.get('high', 0):>6} {counts.get('medium', 0):>6} "
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f"{counts.get('low', 0):>6} {counts.get('noise', 0):>6}")
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def export_csv(results):
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"""Export to CSV (without text_sample)."""
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fields = ["filename", "folder", "size_kb", "pages", "has_fonts", "text_words",
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"language", "quality_score", "quality_label", "issues"]
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with open(CSV_PATH, "w", newline="", encoding="utf-8") as f:
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writer = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
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writer.writeheader()
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for doc in sorted(results, key=lambda x: -x.get("quality_score", 0)):
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doc_copy = dict(doc)
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doc_copy["issues"] = "; ".join(doc.get("issues", []))
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writer.writerow(doc_copy)
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print(f"Exported {len(results)} docs to {CSV_PATH}")
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def move_classified(results, dry_run=True):
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"""Move noise files to Arsiv/noise, high quality to special folder."""
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noise_dir = BOOKS_ROOT / "Arsiv" / "noise"
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moved = 0
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for doc in results:
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if doc.get("quality_label") != "noise":
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continue
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src = Path(doc["path"])
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if not src.exists():
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continue
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dst = noise_dir / doc.get("folder", "") / src.name
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if dry_run:
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print(f" [DRY] {src.name} → {dst.parent}")
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else:
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dst.parent.mkdir(parents=True, exist_ok=True)
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src.rename(dst)
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moved += 1
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action = "Would move" if dry_run else "Moved"
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print(f"\n{action} {moved} noise files")
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if dry_run:
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print("Run with --move --confirm to actually move files")
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def main():
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parser = argparse.ArgumentParser(description="PDF Quality Analyzer")
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parser.add_argument("--scan", action="store_true", help="Scan and analyze PDFs")
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parser.add_argument("--folder", type=str, help="Specific folder under Books/ to scan")
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parser.add_argument("--report", action="store_true", help="Show report from existing analysis")
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parser.add_argument("--export-csv", action="store_true", help="Export to CSV")
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parser.add_argument("--classify", action="store_true", help="Re-classify existing analysis")
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parser.add_argument("--move", action="store_true", help="Move noise to Arsiv/noise (dry-run)")
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parser.add_argument("--confirm", action="store_true", help="Actually move files (with --move)")
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args = parser.parse_args()
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if args.scan:
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existing = load_existing()
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folders = []
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if args.folder:
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folders = [BOOKS_ROOT / args.folder]
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else:
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# Default: scan FOIA + CIA (the questionable ones)
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folders = [
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BOOKS_ROOT / "Istihbarat" / "CIA",
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BOOKS_ROOT / "FOIA" / "documents",
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BOOKS_ROOT / "Istihbarat" / "FOIA-FBI-Vault",
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BOOKS_ROOT / "Istihbarat" / "FOIA-IA-CIA-SogukSavas",
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BOOKS_ROOT / "Istihbarat" / "FOIA-IA-FBI",
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BOOKS_ROOT / "Istihbarat" / "FOIA-IA-CIA-Kuba-OrtaDogu",
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BOOKS_ROOT / "Istihbarat" / "FOIA-IA-WWII",
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BOOKS_ROOT / "Istihbarat" / "FOIA-CIA-Turkey",
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]
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all_results = [v for v in existing.values()
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if not any(str(Path(v["path"]).parent).startswith(str(f)) for f in folders)]
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for folder in folders:
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if folder.exists():
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results = scan_folder(folder, existing)
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all_results.extend(results)
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save_results(all_results)
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print_report(all_results)
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elif args.report:
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if not OUTPUT_PATH.exists():
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print("No analysis found. Run --scan first.")
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return
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with open(OUTPUT_PATH) as f:
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results = json.load(f)
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print_report(results)
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elif args.export_csv:
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if not OUTPUT_PATH.exists():
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print("No analysis found. Run --scan first.")
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return
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with open(OUTPUT_PATH) as f:
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results = json.load(f)
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export_csv(results)
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elif args.classify:
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if not OUTPUT_PATH.exists():
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print("No analysis found. Run --scan first.")
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return
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with open(OUTPUT_PATH) as f:
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results = json.load(f)
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results = [classify(score_quality(d)) for d in results]
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save_results(results)
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print_report(results)
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elif args.move:
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if not OUTPUT_PATH.exists():
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print("No analysis found. Run --scan first.")
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return
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with open(OUTPUT_PATH) as f:
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results = json.load(f)
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move_classified(results, dry_run=not args.confirm)
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else:
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parser.print_help()
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if __name__ == "__main__":
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main()
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