Domi31tls
8339069c83
- GUI v5 : vue unique épurée (tkinter), 2 étapes visuelles - Core ONNX : anonymisation regex + NER optionnel - Extraction globale des noms depuis champs structurés (Patient, Rédigé par, MME/Madame, DR) - Génération simultanée PDF Image + PDF Anonymisé (structure préservée) - Build Windows via Nuitka (script batch + GitHub Actions CI) - install.sh pour setup/run Linux Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
875 lines
34 KiB
Python
875 lines
34 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Core d'anonymisation (v2.1) + NER ONNX (optionnel, narratif uniquement)
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------------------------------------------------------------------------
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- Extraction 2 passes (pdfplumber -> pdfminer) + fallback 3e passe PyMuPDF si texte pauvre ou (cid:xx)
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- Règles regex (PII critiques) + clé:valeur (masquer valeur seulement) + overrides YAML
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- Rescan sécurité **sélectif** (EMAIL/TEL/IBAN/NIR), jamais dans [TABLES]
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- Redaction PDF (vector/raster) via PyMuPDF
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- NER ONNX **optionnel** (CamemBERT family) appliqué **après** les règles, sur le narratif
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Dépendances : pdfplumber, pdfminer.six, pillow, pymupdf, pyyaml (optionnel), transformers, optimum, onnxruntime
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"""
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from __future__ import annotations
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import io
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import json
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import re
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import List, Dict, Tuple, Optional, Any
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import pdfplumber
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from pdfminer.high_level import extract_text as pdfminer_extract_text
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from pdfminer.layout import LAParams
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from PIL import Image, ImageDraw
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try:
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import fitz # PyMuPDF
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except Exception:
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fitz = None
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try:
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import yaml # PyYAML for dictionaries
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except Exception:
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yaml = None
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try:
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from doctr.models import ocr_predictor as _doctr_ocr_predictor
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_DOCTR_AVAILABLE = True
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except Exception:
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_doctr_ocr_predictor = None # type: ignore
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_DOCTR_AVAILABLE = False
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# NER manager (facultatif)
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try:
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from ner_manager_onnx import NerModelManager, NerThresholds
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except Exception:
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NerModelManager = None # type: ignore
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NerThresholds = None # type: ignore
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# EDS-Pseudo manager (facultatif)
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try:
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from eds_pseudo_manager import EdsPseudoManager
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except Exception:
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EdsPseudoManager = None # type: ignore
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# ----------------- Defaults & Config -----------------
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DEFAULTS_CFG = {
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"version": 1,
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"encoding": "utf-8",
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"normalization": "NFKC",
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"whitelist": {
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"sections_titres": ["DIM", "GHM", "GHS", "RUM", "COMPTE", "RENDU", "DIAGNOSTIC"],
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"noms_maj_excepts": ["Médecin DIM", "Praticien conseil"],
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"org_gpe_keep": True,
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},
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"blacklist": {
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"force_mask_terms": [],
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"force_mask_regex": [],
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},
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"kv_labels_preserve": ["FINESS", "IPP", "N° OGC", "Etablissement"],
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"regex_overrides": [
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{
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"name": "OGC_court",
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"pattern": r"\b(?:N°\s*)?OGC\s*[:\-]?\s*([A-Za-z0-9\-]{1,3})\b",
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"placeholder": "[OGC]",
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"flags": ["IGNORECASE"],
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}
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],
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"flags": {
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"case_insensitive": True,
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"unicode_word_boundaries": True,
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"regex_engine": "python",
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},
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}
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PLACEHOLDERS = {
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"EMAIL": "[EMAIL]",
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"TEL": "[TEL]",
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"IBAN": "[IBAN]",
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"NIR": "[NIR]",
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"IPP": "[IPP]",
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"FINESS": "[FINESS]",
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"OGC": "[OGC]",
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"NOM": "[NOM]",
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"VILLE": "[VILLE]",
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"ETAB": "[ETABLISSEMENT]",
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"MASK": "[MASK]",
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"DATE": "[DATE]",
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"DATE_NAISSANCE": "[DATE_NAISSANCE]",
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"ADRESSE": "[ADRESSE]",
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"CODE_POSTAL": "[CODE_POSTAL]",
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"AGE": "[AGE]",
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"DOSSIER": "[DOSSIER]",
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"NDA": "[NDA]",
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}
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CRITICAL_PII_KEYS = {"EMAIL", "TEL", "IBAN", "NIR", "IPP", "DATE_NAISSANCE"}
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# Baseline regex
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RE_EMAIL = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
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RE_TEL = re.compile(r"(?<!\d)(?:\+33\s?|0)\d(?:[ .-]?\d){8}(?!\d)")
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RE_IBAN = re.compile(r"\b[A-Z]{2}\d{2}[A-Z0-9]{11,30}\b")
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RE_IPP = re.compile(r"\bIPP\s*[:\-]?\s*([A-Za-z0-9]{6,})\b", re.IGNORECASE)
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RE_FINESS = re.compile(r"\bFINESS\s*[:\-]?\s*(\d{9})\b", re.IGNORECASE)
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RE_OGC = re.compile(r"\b(?:N°\s*)?OGC\s*[:\-]?\s*([A-Za-z0-9\-]{1,})\b", re.IGNORECASE)
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RE_NIR = re.compile(
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r"\b([12])\s*(\d{2})\s*(0[1-9]|1[0-2]|2[AB])\s*(\d{2,3})\s*(\d{3})\s*(\d{3})\s*(\d{2})\b",
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re.IGNORECASE,
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)
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def validate_nir(nir_raw: str) -> bool:
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"""Vérifie la clé modulo 97 d'un NIR (13 chiffres + 2 clé). Supporte la Corse (2A/2B)."""
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digits_only = re.sub(r"\s+", "", nir_raw)
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if len(digits_only) < 15:
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return False
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body_str = digits_only[:13]
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key_str = digits_only[13:15]
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# Corse : 2A → 19, 2B → 18 (pour le calcul)
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body_str_calc = body_str.upper().replace("2A", "19").replace("2B", "18")
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try:
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body_int = int(body_str_calc)
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key_int = int(key_str)
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except ValueError:
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return False
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return key_int == (97 - (body_int % 97))
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RE_PERSON_CONTEXT = re.compile(
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r"(?:(?:Dr\.?|DR\.?|Docteur|Mme|MME|Madame|M\.|Mr\.?|Monsieur"
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r"|Nom\s*:\s*|Praticien|Médecin"
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r"|Rédigé\s+par|Validé\s+par|Signé\s+par|Saisi\s+par"
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r")\s+)"
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r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' .]+(?:\s+[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\'.]+)*)"
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)
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SPLITTER = re.compile(r"\s*[:|;\t]\s*")
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# --- Extraction globale de noms depuis champs structurés ---
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_UC_NAME_TOKEN = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+"
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RE_EXTRACT_PATIENT = re.compile(
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r"Patient\(?e?\)?\s*:\s*"
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rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)"
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r"(?=\s+Né|\s+né|\s+N°|\s*$)",
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re.MULTILINE,
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)
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RE_EXTRACT_REDIGE = re.compile(
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r"(?:Rédigé|Validé|Signé|Saisi)\s+par\s+"
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rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
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)
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RE_EXTRACT_MME_MR = re.compile(
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r"(?:MME|Madame|Monsieur|Mr\.?)\s+"
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r"((?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,})(?:\s+[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,})*)",
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)
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RE_EXTRACT_DR_DEST = re.compile(
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r"(?:DR\.?|Docteur)\s+"
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rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
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)
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CID_PATTERN = re.compile(r"\(cid:\d+\)")
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# --- Nouvelles regex : dates, adresses, âges, dossiers ---
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_MOIS_FR = r"(?:janvier|février|mars|avril|mai|juin|juillet|août|septembre|octobre|novembre|décembre)"
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RE_DATE_NAISSANCE = re.compile(
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r"(?:n[ée]+\s+le|date\s+de\s+naissance|DDN)\s*[:\-]?\s*"
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r"(\d{1,2}[\s/.\-]\d{1,2}[\s/.\-]\d{2,4}|\d{1,2}\s+" + _MOIS_FR + r"\s+\d{4})",
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re.IGNORECASE,
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)
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RE_DATE = re.compile(
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r"\b(\d{1,2})\s*[/.\-]\s*(\d{1,2})\s*[/.\-]\s*(\d{4})\b"
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r"|"
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r"\b(\d{1,2})\s+" + _MOIS_FR + r"\s+(\d{4})\b",
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re.IGNORECASE,
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)
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RE_ADRESSE = re.compile(
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r"\b\d{1,4}[\s,]*(?:bis|ter)?\s*,?\s*"
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r"(?:rue|avenue|av\.|boulevard|bd|place|chemin|allée|impasse|route|cours|passage|square)"
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r"\s+[A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\s\-']{2,}",
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re.IGNORECASE,
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)
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RE_CODE_POSTAL = re.compile(
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r"(?:(?:code\s*postal|CP)\s*[:\-]?\s*(\d{5}))"
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r"|"
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r"(?:(\d{5})[ \t]+[A-ZÉÈÀÙ][a-zéèàùâêîôû]+(?:[\s\-][A-ZÉÈÀÙ][a-zéèàùâêîôû]+)*)",
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re.IGNORECASE,
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)
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RE_AGE = re.compile(
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r"(?:âg[ée]+\s+de\s+|patient(?:e)?\s+de\s+)?(\d{1,3})\s*ans\b",
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re.IGNORECASE,
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)
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RE_NUMERO_DOSSIER = re.compile(
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r"(?:dossier|n°\s*dossier|NDA)\s*[:\-n°]+\s*([A-Za-z0-9\-/]{4,})"
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r"|"
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r"(?:référence|réf\.)\s*[:\-n°]+\s*([A-Za-z0-9\-/]{4,})",
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re.IGNORECASE,
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)
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@dataclass
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class PiiHit:
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page: int
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kind: str
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original: str
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placeholder: str
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bbox_hint: Optional[Tuple[float, float, float, float]] = None
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@dataclass
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class AnonResult:
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text_out: str
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tables_block: str
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audit: List[PiiHit] = field(default_factory=list)
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# ----------------- Config loader -----------------
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def load_dictionaries(config_path: Optional[Path]) -> Dict[str, Any]:
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cfg = DEFAULTS_CFG.copy()
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if config_path and config_path.exists() and yaml is not None:
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try:
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user = yaml.safe_load(config_path.read_text(encoding="utf-8")) or {}
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for k, v in user.items():
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cfg[k] = v
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except Exception:
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pass
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return cfg
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# ----------------- Extraction -----------------
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def extract_text_with_fallback_ocr(pdf_path: Path) -> Tuple[List[str], List[List[str]], bool]:
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"""Extraction texte multi-passes avec fallback OCR (docTR).
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Retourne (pages_text, tables_lines, ocr_used).
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"""
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pages_text: List[str] = []
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tables_lines: List[List[str]] = []
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ocr_used = False
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with pdfplumber.open(pdf_path) as pdf:
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for p in pdf.pages:
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t = p.extract_text(x_tolerance=2.5, y_tolerance=4.0) or ""
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pages_text.append(t)
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rows: List[str] = []
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try:
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tables = p.extract_tables()
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for tbl in tables or []:
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for row in tbl:
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clean = [c if c is not None else "" for c in row]
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rows.append("\t".join(clean).strip())
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except Exception:
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pass
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tables_lines.append(rows)
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total_chars = sum(len(x or "") for x in pages_text)
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need_fallback = total_chars < 500
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if not need_fallback:
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need_fallback = any(CID_PATTERN.search(x or "") for x in pages_text)
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if need_fallback:
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text_all = pdfminer_extract_text(
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str(pdf_path),
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laparams=LAParams(char_margin=2.0, word_margin=0.1, line_margin=0.8, boxes_flow=0.5),
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)
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split = [x for x in text_all.split("\f") if x]
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if split:
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pages_text = split
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# 3e passe PyMuPDF si toujours pauvre/cid
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total_chars = sum(len(x or "") for x in pages_text)
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if (total_chars < 500 or any(CID_PATTERN.search(x or "") for x in pages_text)) and fitz is not None:
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try:
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doc = fitz.open(str(pdf_path))
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pages_text = [doc[i].get_text("text") or "" for i in range(len(doc))]
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doc.close()
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except Exception:
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pass
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# 4e passe : OCR docTR si toujours très peu de texte (PDF scanné)
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total_chars = sum(len(x or "") for x in pages_text)
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if total_chars < 200 and _DOCTR_AVAILABLE and fitz is not None:
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try:
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model = _doctr_ocr_predictor(det_arch="db_resnet50", reco_arch="crnn_vgg16_bn", pretrained=True)
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doc = fitz.open(str(pdf_path))
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ocr_pages: List[str] = []
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for i in range(len(doc)):
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pix = doc[i].get_pixmap(dpi=300)
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img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
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import numpy as np
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result = model([np.array(img)])
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page_text = ""
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for block in result.pages[0].blocks:
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for line in block.lines:
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words = [w.value for w in line.words]
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page_text += " ".join(words) + "\n"
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ocr_pages.append(page_text)
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doc.close()
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if sum(len(p) for p in ocr_pages) > total_chars:
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pages_text = ocr_pages
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ocr_used = True
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except Exception:
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pass
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return pages_text, tables_lines, ocr_used
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# Alias pour compatibilité ascendante
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def extract_text_three_passes(pdf_path: Path):
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pages_text, tables_lines, _ = extract_text_with_fallback_ocr(pdf_path)
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return pages_text, tables_lines
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# ----------------- Helpers -----------------
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def _compile_user_regex(pattern: str, flags_list: List[str]):
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flags = 0
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for f in flags_list or []:
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u = f.upper()
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if u == "IGNORECASE": flags |= re.IGNORECASE
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if u == "MULTILINE": flags |= re.MULTILINE
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if u == "DOTALL": flags |= re.DOTALL
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return re.compile(pattern, flags)
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def _apply_overrides(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict[str, Any]) -> str:
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for ov in cfg.get("regex_overrides", []) or []:
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pattern = ov.get("pattern"); placeholder = ov.get("placeholder", PLACEHOLDERS["MASK"]) ; name = ov.get("name", "override")
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flags_list = ov.get("flags", [])
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try:
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rx = _compile_user_regex(pattern, flags_list)
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except Exception:
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continue
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def _rep(m: re.Match):
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audit.append(PiiHit(page_idx, name, m.group(0), placeholder))
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return placeholder
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line = rx.sub(_rep, line)
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# force-mask literals
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for term in (cfg.get("blacklist", {}).get("force_mask_terms", []) or []):
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if not term: continue
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word_rx = re.compile(rf"\b{re.escape(term)}\b", re.IGNORECASE)
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if word_rx.search(line):
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audit.append(PiiHit(page_idx, "force_term", term, PLACEHOLDERS["MASK"]))
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line = word_rx.sub(PLACEHOLDERS["MASK"], line)
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# force-mask regex
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for pat in (cfg.get("blacklist", {}).get("force_mask_regex", []) or []):
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try:
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rx = re.compile(pat, re.IGNORECASE)
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except Exception:
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continue
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if rx.search(line):
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audit.append(PiiHit(page_idx, "force_regex", pat, PLACEHOLDERS["MASK"]))
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line = rx.sub(PLACEHOLDERS["MASK"], line)
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return line
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def _mask_admin_label(line: str, audit: List[PiiHit], page_idx: int) -> str:
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m = RE_FINESS.search(line)
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if m:
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val = m.group(1); audit.append(PiiHit(page_idx, "FINESS", val, PLACEHOLDERS["FINESS"]))
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return RE_FINESS.sub(lambda _: f"FINESS : {PLACEHOLDERS['FINESS']}", line)
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m = RE_OGC.search(line)
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if m:
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val = m.group(1); audit.append(PiiHit(page_idx, "OGC", val, PLACEHOLDERS["OGC"]))
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return RE_OGC.sub(lambda _: f"N° OGC : {PLACEHOLDERS['OGC']}", line)
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m = RE_IPP.search(line)
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if m:
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val = m.group(1); audit.append(PiiHit(page_idx, "IPP", val, PLACEHOLDERS["IPP"]))
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return RE_IPP.sub(lambda _: f"IPP : {PLACEHOLDERS['IPP']}", line)
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return line
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def _mask_line_by_regex(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict[str, Any]) -> str:
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# user overrides & force-masks d'abord
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line = _apply_overrides(line, audit, page_idx, cfg)
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# EMAIL
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def _repl_email(m: re.Match) -> str:
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audit.append(PiiHit(page_idx, "EMAIL", m.group(0), PLACEHOLDERS["EMAIL"]))
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return PLACEHOLDERS["EMAIL"]
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line = RE_EMAIL.sub(_repl_email, line)
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# TEL
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def _repl_tel(m: re.Match) -> str:
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audit.append(PiiHit(page_idx, "TEL", m.group(0), PLACEHOLDERS["TEL"]))
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return PLACEHOLDERS["TEL"]
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line = RE_TEL.sub(_repl_tel, line)
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# IBAN
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def _repl_iban(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "IBAN", m.group(0), PLACEHOLDERS["IBAN"]))
|
||
return PLACEHOLDERS["IBAN"]
|
||
line = RE_IBAN.sub(_repl_iban, line)
|
||
|
||
# NIR (avec validation clé modulo 97)
|
||
def _repl_nir(m: re.Match) -> str:
|
||
raw = m.group(0)
|
||
if not validate_nir(raw):
|
||
return raw # faux positif, on ne masque pas
|
||
audit.append(PiiHit(page_idx, "NIR", raw, PLACEHOLDERS["NIR"]))
|
||
return PLACEHOLDERS["NIR"]
|
||
line = RE_NIR.sub(_repl_nir, line)
|
||
|
||
# DATE_NAISSANCE (plus spécifique, avant DATE générique)
|
||
def _repl_date_naissance(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "DATE_NAISSANCE", m.group(0), PLACEHOLDERS["DATE_NAISSANCE"]))
|
||
return PLACEHOLDERS["DATE_NAISSANCE"]
|
||
line = RE_DATE_NAISSANCE.sub(_repl_date_naissance, line)
|
||
|
||
# DATE générique
|
||
def _repl_date(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "DATE", m.group(0), PLACEHOLDERS["DATE"]))
|
||
return PLACEHOLDERS["DATE"]
|
||
line = RE_DATE.sub(_repl_date, line)
|
||
|
||
# ADRESSE
|
||
def _repl_adresse(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "ADRESSE", m.group(0), PLACEHOLDERS["ADRESSE"]))
|
||
return PLACEHOLDERS["ADRESSE"]
|
||
line = RE_ADRESSE.sub(_repl_adresse, line)
|
||
|
||
# CODE_POSTAL
|
||
def _repl_code_postal(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "CODE_POSTAL", m.group(0), PLACEHOLDERS["CODE_POSTAL"]))
|
||
return PLACEHOLDERS["CODE_POSTAL"]
|
||
line = RE_CODE_POSTAL.sub(_repl_code_postal, line)
|
||
|
||
# AGE
|
||
def _repl_age(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "AGE", m.group(0), PLACEHOLDERS["AGE"]))
|
||
return PLACEHOLDERS["AGE"]
|
||
line = RE_AGE.sub(_repl_age, line)
|
||
|
||
# NUMERO DOSSIER / NDA
|
||
def _repl_dossier(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "DOSSIER", m.group(0), PLACEHOLDERS["DOSSIER"]))
|
||
return PLACEHOLDERS["DOSSIER"]
|
||
line = RE_NUMERO_DOSSIER.sub(_repl_dossier, line)
|
||
|
||
# PERSON uppercase avec contexte, whitelist/acronymes courts
|
||
wl_sections = set((cfg.get("whitelist", {}) or {}).get("sections_titres", []) or [])
|
||
wl_phrases = set((cfg.get("whitelist", {}) or {}).get("noms_maj_excepts", []) or [])
|
||
|
||
def _repl_person_ctx(m: re.Match) -> str:
|
||
span = m.group(1).strip(); raw = m.group(0)
|
||
if span in wl_sections or raw in wl_phrases: return raw
|
||
tokens = [t for t in span.split() if t]
|
||
if len(tokens) == 1 and len(tokens[0]) <= 3: return raw
|
||
audit.append(PiiHit(page_idx, "NOM", span, PLACEHOLDERS["NOM"]))
|
||
return raw.replace(span, PLACEHOLDERS["NOM"]) # conserve le préfixe Dr/Mme
|
||
|
||
line = RE_PERSON_CONTEXT.sub(_repl_person_ctx, line)
|
||
return line
|
||
|
||
|
||
def _kv_value_only_mask(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict[str, Any]) -> str:
|
||
line = _mask_admin_label(line, audit, page_idx)
|
||
parts = SPLITTER.split(line, maxsplit=1)
|
||
if len(parts) == 2:
|
||
key, value = parts
|
||
masked_val = _mask_line_by_regex(value, audit, page_idx, cfg)
|
||
return f"{key.strip()} : {masked_val.strip()}"
|
||
else:
|
||
return _mask_line_by_regex(line, audit, page_idx, cfg)
|
||
|
||
# ----------------- Extraction globale de noms -----------------
|
||
|
||
def _extract_document_names(full_text: str, cfg: Dict[str, Any]) -> set:
|
||
"""Pré-scan du document brut pour extraire les noms de personnes
|
||
depuis les champs structurés (Patient, Rédigé par, etc.).
|
||
Retourne un ensemble de tokens (mots) à masquer globalement."""
|
||
wl_sections = set((cfg.get("whitelist", {}) or {}).get("sections_titres", []) or [])
|
||
wl_phrases = set((cfg.get("whitelist", {}) or {}).get("noms_maj_excepts", []) or [])
|
||
names: set = set()
|
||
|
||
def _add_tokens(match_str: str):
|
||
for token in match_str.split():
|
||
token = token.strip(" .-'")
|
||
if len(token) >= 3 and token.upper() not in wl_sections and token not in wl_phrases:
|
||
names.add(token)
|
||
|
||
for m in RE_EXTRACT_PATIENT.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_REDIGE.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_MME_MR.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_DR_DEST.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
return names
|
||
|
||
|
||
def _apply_extracted_names(text: str, names: set, audit: List[PiiHit]) -> str:
|
||
"""Remplace globalement chaque nom extrait dans le texte."""
|
||
placeholder = PLACEHOLDERS["NOM"]
|
||
for token in sorted(names, key=len, reverse=True):
|
||
pattern = re.compile(rf"\b{re.escape(token)}\b", re.IGNORECASE)
|
||
for m in pattern.finditer(text):
|
||
# Ne pas remplacer si déjà dans un placeholder
|
||
ctx_start = max(0, m.start() - 1)
|
||
ctx_end = min(len(text), m.end() + 1)
|
||
if "[" in text[ctx_start:m.start()] or "]" in text[m.end():ctx_end]:
|
||
continue
|
||
audit.append(PiiHit(-1, "NOM_EXTRACTED", m.group(0), placeholder))
|
||
text = pattern.sub(placeholder, text)
|
||
return text
|
||
|
||
|
||
# ----------------- Anonymisation (regex) -----------------
|
||
|
||
def anonymise_document_regex(pages_text: List[str], tables_lines: List[List[str]], cfg: Dict[str, Any]) -> AnonResult:
|
||
audit: List[PiiHit] = []
|
||
|
||
# Phase 0 : extraction globale des noms depuis les champs structurés
|
||
full_raw = "\n".join(pages_text) + "\n" + "\n".join(
|
||
"\n".join(rows) for rows in tables_lines
|
||
)
|
||
extracted_names = _extract_document_names(full_raw, cfg)
|
||
|
||
# Phase 1 : masquage ligne par ligne (regex classiques)
|
||
out_pages: List[str] = []
|
||
for i, page_txt in enumerate(pages_text):
|
||
lines = [ln for ln in (page_txt or "").splitlines()]
|
||
masked = [_kv_value_only_mask(ln, audit, i, cfg) for ln in lines]
|
||
out_pages.append("\n".join(masked))
|
||
table_blocks: List[str] = []
|
||
for i, rows in enumerate(tables_lines):
|
||
mbuf: List[str] = []
|
||
for r in rows:
|
||
masked = _kv_value_only_mask(r, audit, i, cfg)
|
||
mbuf.append(masked)
|
||
if mbuf:
|
||
table_blocks.append("\n".join(mbuf))
|
||
tables_block = "\n\n".join(table_blocks)
|
||
text_out = "\f".join(out_pages) # séparateur de pages
|
||
if tables_block.strip():
|
||
text_out += "\n\n[TABLES]\n" + tables_block + "\n[/TABLES]"
|
||
|
||
# Phase 2 : application globale des noms extraits (rattrapage)
|
||
if extracted_names:
|
||
text_out = _apply_extracted_names(text_out, extracted_names, audit)
|
||
|
||
return AnonResult(text_out=text_out, tables_block=tables_block, audit=audit)
|
||
|
||
# ----------------- NER ONNX sur narratif -----------------
|
||
|
||
def _mask_with_hf(text: str, ents: List[Dict[str, Any]], cfg: Dict[str, Any], audit: List[PiiHit]) -> str:
|
||
# remplace via regex sur les 'word' détectés (approche pragmatique)
|
||
keep_org_gpe = bool((cfg.get("whitelist", {}) or {}).get("org_gpe_keep", True))
|
||
def repl_once(s: str, old: str, new: str) -> str:
|
||
return re.sub(rf"\b{re.escape(old)}\b", new, s)
|
||
out = text
|
||
for e in ents:
|
||
w = e.get("word") or ""; grp = (e.get("entity_group") or e.get("entity") or "").upper()
|
||
if not w or "[" in w or "]" in w: # ignore placeholders
|
||
continue
|
||
if len(w) <= 2: # trop court
|
||
continue
|
||
if grp in {"PER", "PERSON"}:
|
||
audit.append(PiiHit(-1, "NER_PER", w, PLACEHOLDERS["NOM"]))
|
||
out = repl_once(out, w, PLACEHOLDERS["NOM"])
|
||
elif grp in {"ORG"}:
|
||
if keep_org_gpe:
|
||
continue
|
||
audit.append(PiiHit(-1, "NER_ORG", w, PLACEHOLDERS["ETAB"]))
|
||
out = repl_once(out, w, PLACEHOLDERS["ETAB"])
|
||
elif grp in {"LOC"}:
|
||
if keep_org_gpe:
|
||
continue
|
||
audit.append(PiiHit(-1, "NER_LOC", w, PLACEHOLDERS["VILLE"]))
|
||
out = repl_once(out, w, PLACEHOLDERS["VILLE"])
|
||
elif grp in {"DATE"}:
|
||
# facultatif : si vous masquez déjà les dates via règles, laissez tel quel
|
||
continue
|
||
return out
|
||
|
||
|
||
def apply_hf_ner_on_narrative(text_out: str, cfg: Dict[str, Any], manager: Optional[NerModelManager], thresholds: Optional[NerThresholds]) -> Tuple[str, List[PiiHit]]:
|
||
if manager is None or not manager.is_loaded():
|
||
return text_out, []
|
||
# isoler [TABLES]
|
||
pattern = re.compile(r"\[TABLES\](.*?)\[/TABLES\]", re.DOTALL)
|
||
tables: List[Tuple[int,int,str]] = []
|
||
keep = []
|
||
last = 0
|
||
cleaned = ""
|
||
for m in pattern.finditer(text_out):
|
||
cleaned += text_out[last:m.start()]
|
||
keep.append((len(cleaned), len(cleaned) + len(m.group(0)), m.group(0)))
|
||
cleaned += "\x00" * len(m.group(0))
|
||
last = m.end()
|
||
cleaned += text_out[last:]
|
||
|
||
# par pages (séparées par \f) → par paragraphes
|
||
pages = cleaned.split("\f")
|
||
hits: List[PiiHit] = []
|
||
rebuilt_pages: List[str] = []
|
||
for pg in pages:
|
||
paras = [p for p in re.split(r"\n\s*\n", pg) if p.strip()]
|
||
ents_per_para = manager.infer_paragraphs(paras, thresholds=thresholds)
|
||
# remplace entités
|
||
idx = 0
|
||
buf = []
|
||
for para, ents in zip(paras, ents_per_para):
|
||
masked = _mask_with_hf(para, ents, cfg, hits)
|
||
buf.append(masked)
|
||
rebuilt_pages.append("\n\n".join(buf))
|
||
rebuilt = "\f".join(rebuilt_pages)
|
||
|
||
# réinsérer [TABLES]
|
||
rebuilt_list = list(rebuilt)
|
||
for start, end, payload in keep:
|
||
rebuilt_list[start:end] = list(payload)
|
||
final = "".join(rebuilt_list)
|
||
return final, hits
|
||
|
||
# ----------------- NER EDS-Pseudo sur narratif -----------------
|
||
|
||
def _mask_with_eds_pseudo(text: str, ents: List[Dict[str, Any]], cfg: Dict[str, Any], audit: List[PiiHit]) -> str:
|
||
"""Masque les entités détectées par EDS-Pseudo en utilisant le mapping eds_mapped_key."""
|
||
def repl_once(s: str, old: str, new: str) -> str:
|
||
return re.sub(rf"\b{re.escape(old)}\b", new, s)
|
||
out = text
|
||
for e in ents:
|
||
w = e.get("word") or ""
|
||
mapped_key = e.get("eds_mapped_key", "")
|
||
if not w or "[" in w or "]" in w:
|
||
continue
|
||
if len(w) <= 2:
|
||
continue
|
||
placeholder = PLACEHOLDERS.get(mapped_key, PLACEHOLDERS["MASK"])
|
||
label = e.get("entity_group", "EDS")
|
||
audit.append(PiiHit(-1, f"EDS_{label}", w, placeholder))
|
||
out = repl_once(out, w, placeholder)
|
||
return out
|
||
|
||
|
||
def apply_eds_pseudo_on_narrative(text_out: str, cfg: Dict[str, Any], manager: "EdsPseudoManager") -> Tuple[str, List[PiiHit]]:
|
||
"""Applique EDS-Pseudo sur le narratif (même structure que apply_hf_ner_on_narrative)."""
|
||
if manager is None or not manager.is_loaded():
|
||
return text_out, []
|
||
# isoler [TABLES]
|
||
pattern = re.compile(r"\[TABLES\](.*?)\[/TABLES\]", re.DOTALL)
|
||
keep = []
|
||
last = 0
|
||
cleaned = ""
|
||
for m in pattern.finditer(text_out):
|
||
cleaned += text_out[last:m.start()]
|
||
keep.append((len(cleaned), len(cleaned) + len(m.group(0)), m.group(0)))
|
||
cleaned += "\x00" * len(m.group(0))
|
||
last = m.end()
|
||
cleaned += text_out[last:]
|
||
|
||
# par pages → par paragraphes
|
||
pages = cleaned.split("\f")
|
||
hits: List[PiiHit] = []
|
||
rebuilt_pages: List[str] = []
|
||
for pg in pages:
|
||
paras = [p for p in re.split(r"\n\s*\n", pg) if p.strip()]
|
||
ents_per_para = manager.infer_paragraphs(paras)
|
||
buf = []
|
||
for para, ents in zip(paras, ents_per_para):
|
||
masked = _mask_with_eds_pseudo(para, ents, cfg, hits)
|
||
buf.append(masked)
|
||
rebuilt_pages.append("\n\n".join(buf))
|
||
rebuilt = "\f".join(rebuilt_pages)
|
||
|
||
# réinsérer [TABLES]
|
||
rebuilt_list = list(rebuilt)
|
||
for start, end, payload in keep:
|
||
rebuilt_list[start:end] = list(payload)
|
||
final = "".join(rebuilt_list)
|
||
return final, hits
|
||
|
||
# ----------------- Selective safety rescan -----------------
|
||
|
||
def selective_rescan(text: str, cfg: Dict[str, Any] | None = None) -> str:
|
||
"""Rescan de sécurité : re-détecte les PII critiques qui auraient échappé au premier passage."""
|
||
# enlève TABLES du scope
|
||
def strip_tables(s: str):
|
||
kept = []
|
||
out = []
|
||
i = 0
|
||
pattern = re.compile(r"\[TABLES\](.*?)\[/TABLES\]", re.DOTALL)
|
||
for m in pattern.finditer(s):
|
||
out.append(s[i:m.start()])
|
||
kept.append((len("".join(out)), len("".join(out)) + len(m.group(1)), m.group(1)))
|
||
out.append("\x00" * (m.end() - m.start()))
|
||
i = m.end()
|
||
out.append(s[i:])
|
||
return "".join(out), kept
|
||
protected, kept = strip_tables(text)
|
||
# PII critiques (comme avant)
|
||
protected = RE_EMAIL.sub(PLACEHOLDERS["EMAIL"], protected)
|
||
protected = RE_TEL.sub(PLACEHOLDERS["TEL"], protected)
|
||
protected = RE_IBAN.sub(PLACEHOLDERS["IBAN"], protected)
|
||
# NIR avec validation
|
||
def _rescan_nir(m: re.Match) -> str:
|
||
return PLACEHOLDERS["NIR"] if validate_nir(m.group(0)) else m.group(0)
|
||
protected = RE_NIR.sub(_rescan_nir, protected)
|
||
# Nouvelles regex : dates de naissance, dates, adresses, codes postaux
|
||
protected = RE_DATE_NAISSANCE.sub(PLACEHOLDERS["DATE_NAISSANCE"], protected)
|
||
protected = RE_DATE.sub(PLACEHOLDERS["DATE"], protected)
|
||
protected = RE_ADRESSE.sub(PLACEHOLDERS["ADRESSE"], protected)
|
||
protected = RE_CODE_POSTAL.sub(PLACEHOLDERS["CODE_POSTAL"], protected)
|
||
# Personnes contextuelles (avec whitelist)
|
||
wl_sections = set()
|
||
wl_phrases = set()
|
||
if cfg:
|
||
wl_sections = set((cfg.get("whitelist", {}) or {}).get("sections_titres", []) or [])
|
||
wl_phrases = set((cfg.get("whitelist", {}) or {}).get("noms_maj_excepts", []) or [])
|
||
def _rescan_person(m: re.Match) -> str:
|
||
span = m.group(1).strip(); raw = m.group(0)
|
||
if span in wl_sections or raw in wl_phrases:
|
||
return raw
|
||
tokens = [t for t in span.split() if t]
|
||
if len(tokens) == 1 and len(tokens[0]) <= 3:
|
||
return raw
|
||
return raw.replace(span, PLACEHOLDERS["NOM"])
|
||
protected = RE_PERSON_CONTEXT.sub(_rescan_person, protected)
|
||
res = list(protected)
|
||
for start, end, payload in kept:
|
||
res[start:end] = list(payload)
|
||
return "".join(res)
|
||
|
||
# ----------------- PDF Redaction -----------------
|
||
|
||
def redact_pdf_vector(original_pdf: Path, audit: List[PiiHit], out_pdf: Path) -> None:
|
||
if fitz is None:
|
||
raise RuntimeError("PyMuPDF non disponible – installez pymupdf.")
|
||
doc = fitz.open(str(original_pdf))
|
||
# index hits par page; page==-1 → rechercher sur toutes pages
|
||
by_page: Dict[int, List[PiiHit]] = {}
|
||
for h in audit:
|
||
by_page.setdefault(h.page, []).append(h)
|
||
for pno in range(len(doc)):
|
||
page = doc[pno]
|
||
hits = by_page.get(pno, []) + by_page.get(-1, [])
|
||
if not hits:
|
||
continue
|
||
for h in hits:
|
||
token = h.original.strip()
|
||
if not token:
|
||
continue
|
||
rects = page.search_for(token)
|
||
if not rects and h.kind in {"NIR", "IBAN", "TEL"}:
|
||
compact = re.sub(r"\s+", "", token)
|
||
if compact != token:
|
||
rects = page.search_for(compact)
|
||
for r in rects:
|
||
page.add_redact_annot(r, fill=(0,0,0))
|
||
try:
|
||
page.apply_redactions()
|
||
except Exception:
|
||
pass
|
||
doc.save(str(out_pdf), deflate=True, garbage=4, clean=True, incremental=False)
|
||
doc.close()
|
||
|
||
|
||
def redact_pdf_raster(original_pdf: Path, audit: List[PiiHit], out_pdf: Path, dpi: int = 300) -> None:
|
||
if fitz is None:
|
||
raise RuntimeError("PyMuPDF non disponible – installez pymupdf.")
|
||
doc = fitz.open(str(original_pdf)); out = fitz.open()
|
||
all_rects: Dict[int, List["fitz.Rect"]] = {}
|
||
for pno in range(len(doc)):
|
||
page = doc[pno]
|
||
rects = []
|
||
hits = [x for x in audit if x.page in {pno, -1}]
|
||
for h in hits:
|
||
token = h.original.strip()
|
||
if not token: continue
|
||
found = page.search_for(token)
|
||
if not found and h.kind in {"NIR", "IBAN", "TEL"}:
|
||
compact = re.sub(r"\s+", "", token)
|
||
found = page.search_for(compact)
|
||
rects.extend(found)
|
||
all_rects[pno] = rects
|
||
for pno in range(len(doc)):
|
||
src = doc[pno]; rect = src.rect
|
||
zoom = dpi / 72.0; mat = fitz.Matrix(zoom, zoom)
|
||
pix = src.get_pixmap(matrix=mat, annots=False)
|
||
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
||
draw = ImageDraw.Draw(img)
|
||
for r in all_rects.get(pno, []):
|
||
draw.rectangle([r.x0 * zoom, r.y0 * zoom, r.x1 * zoom, r.y1 * zoom], fill=(0, 0, 0))
|
||
buf = io.BytesIO(); img.save(buf, format="PNG"); buf.seek(0)
|
||
dst = out.new_page(width=rect.width, height=rect.height)
|
||
dst.insert_image(rect, stream=buf.getvalue())
|
||
out.save(str(out_pdf), deflate=True, garbage=4, clean=True)
|
||
out.close(); doc.close()
|
||
|
||
# ----------------- Orchestration -----------------
|
||
|
||
def process_pdf(
|
||
pdf_path: Path,
|
||
out_dir: Path,
|
||
make_vector_redaction: bool = True,
|
||
also_make_raster_burn: bool = False,
|
||
config_path: Optional[Path] = None,
|
||
use_hf: bool = False,
|
||
ner_manager=None,
|
||
ner_thresholds=None,
|
||
) -> Dict[str, str]:
|
||
out_dir.mkdir(parents=True, exist_ok=True)
|
||
cfg = load_dictionaries(config_path)
|
||
pages_text, tables_lines, ocr_used = extract_text_with_fallback_ocr(pdf_path)
|
||
|
||
# 1) Regex rules
|
||
anon = anonymise_document_regex(pages_text, tables_lines, cfg)
|
||
|
||
# 2) NER (optionnel) — sur le narratif
|
||
final_text = anon.text_out
|
||
hf_hits: List[PiiHit] = []
|
||
if use_hf and ner_manager is not None and ner_manager.is_loaded():
|
||
# Détecter le type de manager et appeler la bonne fonction
|
||
if EdsPseudoManager is not None and isinstance(ner_manager, EdsPseudoManager):
|
||
final_text, hf_hits = apply_eds_pseudo_on_narrative(final_text, cfg, ner_manager)
|
||
else:
|
||
final_text, hf_hits = apply_hf_ner_on_narrative(final_text, cfg, ner_manager, ner_thresholds)
|
||
anon.audit.extend(hf_hits)
|
||
|
||
# 3) Rescan selectif
|
||
final_text = selective_rescan(final_text, cfg=cfg)
|
||
|
||
# Log OCR dans l'audit
|
||
if ocr_used:
|
||
anon.audit.insert(0, PiiHit(page=-1, kind="OCR_USED", original="docTR", placeholder=""))
|
||
|
||
# Sauvegardes
|
||
base = pdf_path.stem
|
||
txt_path = out_dir / f"{base}.pseudonymise.txt"
|
||
audit_path = out_dir / f"{base}.audit.jsonl"
|
||
txt_path.write_text(final_text, encoding="utf-8")
|
||
with audit_path.open("w", encoding="utf-8") as f:
|
||
for hit in anon.audit:
|
||
f.write(json.dumps(hit.__dict__, ensure_ascii=False) + "\n")
|
||
outputs = {"text": str(txt_path), "audit": str(audit_path)}
|
||
|
||
# PDFs
|
||
if make_vector_redaction and fitz is not None:
|
||
vec_path = out_dir / f"{base}.redacted_vector.pdf"
|
||
try:
|
||
redact_pdf_vector(pdf_path, anon.audit, vec_path)
|
||
outputs["pdf_vector"] = str(vec_path)
|
||
except Exception:
|
||
pass
|
||
if also_make_raster_burn and fitz is not None:
|
||
ras_path = out_dir / f"{base}.redacted_raster.pdf"
|
||
redact_pdf_raster(pdf_path, anon.audit, ras_path)
|
||
outputs["pdf_raster"] = str(ras_path)
|
||
return outputs
|
||
|
||
if __name__ == "__main__":
|
||
import argparse
|
||
ap = argparse.ArgumentParser(description="Anonymiser PDF (regex + NER ONNX optionnel)")
|
||
ap.add_argument("pdf", type=str)
|
||
ap.add_argument("--out", type=str, default="out")
|
||
ap.add_argument("--no-vector", action="store_true")
|
||
ap.add_argument("--raster", action="store_true")
|
||
ap.add_argument("--config", type=str, default=str(Path("config/dictionnaires.yml")))
|
||
ap.add_argument("--hf", action="store_true", help="Activer NER ONNX sur narratif (nécessite ner_manager_onnx)")
|
||
ap.add_argument("--model", type=str, default="cmarkea/distilcamembert-base-ner")
|
||
args = ap.parse_args()
|
||
manager = None
|
||
if args.hf and NerModelManager is not None:
|
||
manager = NerModelManager(cache_dir=Path("models"))
|
||
manager.load(args.model)
|
||
outs = process_pdf(
|
||
Path(args.pdf),
|
||
Path(args.out),
|
||
make_vector_redaction=not args.no_vector,
|
||
also_make_raster_burn=args.raster,
|
||
config_path=Path(args.config),
|
||
use_hf=bool(args.hf),
|
||
ner_manager=manager,
|
||
ner_thresholds=NerThresholds() if NerThresholds else None,
|
||
)
|
||
print(json.dumps(outs, indent=2, ensure_ascii=False))
|