Domi31tls
5ed2312d93
- Parseur trackare spécifique (détection par contenu, extraction structurée des PII) - Support format "Dr X. NOM" et "Mme X. NOM" (initiales + noms composés avec tiret) - Détection noms personnel médical (Aide, Cadre Infirmier, etc.) - Masquage RPPS, établissements (EHPAD/SSR/USLD standalone), lieux de naissance - Stop words médicaux enrichis (~270 entrées : DCI, spécialités, termes contextuels) - Détection compagnon (noms adjacents à des noms connus dans le texte brut) - Protection noms composés (JEAN-PIERRE traité comme un tout, pas JEAN + PIERRE) - Nettoyage codes postaux orphelins, téléphones fragmentés/partiels - Désactivation masquage dates génériques, AGE avec contexte obligatoire - GUI : extraction OGC depuis le nom du répertoire parent, incrustation sur les pages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1504 lines
65 KiB
Python
1504 lines
65 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
Core d'anonymisation (v2.1) + NER ONNX (optionnel, narratif uniquement)
|
||
------------------------------------------------------------------------
|
||
- Extraction 2 passes (pdfplumber -> pdfminer) + fallback 3e passe PyMuPDF si texte pauvre ou (cid:xx)
|
||
- Règles regex (PII critiques) + clé:valeur (masquer valeur seulement) + overrides YAML
|
||
- Rescan sécurité **sélectif** (EMAIL/TEL/IBAN/NIR), jamais dans [TABLES]
|
||
- Redaction PDF (vector/raster) via PyMuPDF
|
||
- NER ONNX **optionnel** (CamemBERT family) appliqué **après** les règles, sur le narratif
|
||
|
||
Dépendances : pdfplumber, pdfminer.six, pillow, pymupdf, pyyaml (optionnel), transformers, optimum, onnxruntime
|
||
"""
|
||
from __future__ import annotations
|
||
import io
|
||
import json
|
||
import re
|
||
from dataclasses import dataclass, field
|
||
from pathlib import Path
|
||
from typing import List, Dict, Tuple, Optional, Any
|
||
|
||
import pdfplumber
|
||
from pdfminer.high_level import extract_text as pdfminer_extract_text
|
||
from pdfminer.layout import LAParams
|
||
from PIL import Image, ImageDraw
|
||
|
||
try:
|
||
import fitz # PyMuPDF
|
||
except Exception:
|
||
fitz = None
|
||
|
||
try:
|
||
import yaml # PyYAML for dictionaries
|
||
except Exception:
|
||
yaml = None
|
||
|
||
try:
|
||
from doctr.models import ocr_predictor as _doctr_ocr_predictor
|
||
_DOCTR_AVAILABLE = True
|
||
except Exception:
|
||
_doctr_ocr_predictor = None # type: ignore
|
||
_DOCTR_AVAILABLE = False
|
||
|
||
# NER manager (facultatif)
|
||
try:
|
||
from ner_manager_onnx import NerModelManager, NerThresholds
|
||
except Exception:
|
||
NerModelManager = None # type: ignore
|
||
NerThresholds = None # type: ignore
|
||
|
||
# EDS-Pseudo manager (facultatif)
|
||
try:
|
||
from eds_pseudo_manager import EdsPseudoManager
|
||
except Exception:
|
||
EdsPseudoManager = None # type: ignore
|
||
|
||
# ----------------- Defaults & Config -----------------
|
||
DEFAULTS_CFG = {
|
||
"version": 1,
|
||
"encoding": "utf-8",
|
||
"normalization": "NFKC",
|
||
"whitelist": {
|
||
"sections_titres": ["DIM", "GHM", "GHS", "RUM", "COMPTE", "RENDU", "DIAGNOSTIC"],
|
||
"noms_maj_excepts": ["Médecin DIM", "Praticien conseil"],
|
||
"org_gpe_keep": True,
|
||
},
|
||
"blacklist": {
|
||
"force_mask_terms": [],
|
||
"force_mask_regex": [],
|
||
},
|
||
"kv_labels_preserve": ["FINESS", "IPP", "N° OGC", "Etablissement"],
|
||
"regex_overrides": [
|
||
{
|
||
"name": "OGC_court",
|
||
"pattern": r"\b(?:N°\s*)?OGC\s*[:\-]?\s*([A-Za-z0-9\-]{1,3})\b",
|
||
"placeholder": "[OGC]",
|
||
"flags": ["IGNORECASE"],
|
||
}
|
||
],
|
||
"flags": {
|
||
"case_insensitive": True,
|
||
"unicode_word_boundaries": True,
|
||
"regex_engine": "python",
|
||
},
|
||
}
|
||
|
||
PLACEHOLDERS = {
|
||
"EMAIL": "[EMAIL]",
|
||
"TEL": "[TEL]",
|
||
"IBAN": "[IBAN]",
|
||
"NIR": "[NIR]",
|
||
"IPP": "[IPP]",
|
||
"FINESS": "[FINESS]",
|
||
"OGC": "[OGC]",
|
||
"NOM": "[NOM]",
|
||
"VILLE": "[VILLE]",
|
||
"ETAB": "[ETABLISSEMENT]",
|
||
"MASK": "[MASK]",
|
||
"DATE": "[DATE]",
|
||
"DATE_NAISSANCE": "[DATE_NAISSANCE]",
|
||
"ADRESSE": "[ADRESSE]",
|
||
"CODE_POSTAL": "[CODE_POSTAL]",
|
||
"AGE": "[AGE]",
|
||
"DOSSIER": "[DOSSIER]",
|
||
"NDA": "[NDA]",
|
||
"EPISODE": "[EPISODE]",
|
||
"RPPS": "[RPPS]",
|
||
}
|
||
|
||
CRITICAL_PII_KEYS = {"EMAIL", "TEL", "IBAN", "NIR", "IPP", "DATE_NAISSANCE"}
|
||
|
||
# Baseline regex
|
||
RE_EMAIL = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
|
||
RE_TEL = re.compile(r"(?<!\d)(?:\+33\s?|0)\d(?:[ .-]?\d){8}(?!\d)")
|
||
RE_IBAN = re.compile(r"\b[A-Z]{2}\d{2}[A-Z0-9]{11,30}\b")
|
||
RE_IPP = re.compile(r"\bIPP\s*[:\-]?\s*([A-Za-z0-9]{6,})\b", re.IGNORECASE)
|
||
RE_FINESS = re.compile(r"\bFINESS\s*[:\-]?\s*(\d{9})\b", re.IGNORECASE)
|
||
RE_OGC = re.compile(r"\b(?:N°\s*)?OGC\s*[:\-]?\s*([A-Za-z0-9\-]{1,})\b", re.IGNORECASE)
|
||
RE_RPPS = re.compile(r"\b(?:N°\s*)?RPPS\s*[:\-]?\s*(\d{8,11})\b", re.IGNORECASE)
|
||
RE_NIR = re.compile(
|
||
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",
|
||
re.IGNORECASE,
|
||
)
|
||
|
||
|
||
def validate_nir(nir_raw: str) -> bool:
|
||
"""Vérifie la clé modulo 97 d'un NIR (13 chiffres + 2 clé). Supporte la Corse (2A/2B)."""
|
||
digits_only = re.sub(r"\s+", "", nir_raw)
|
||
if len(digits_only) < 15:
|
||
return False
|
||
body_str = digits_only[:13]
|
||
key_str = digits_only[13:15]
|
||
# Corse : 2A → 19, 2B → 18 (pour le calcul)
|
||
body_str_calc = body_str.upper().replace("2A", "19").replace("2B", "18")
|
||
try:
|
||
body_int = int(body_str_calc)
|
||
key_int = int(key_str)
|
||
except ValueError:
|
||
return False
|
||
return key_int == (97 - (body_int % 97))
|
||
|
||
# Mots médicaux/techniques/courants qui ne sont pas des noms de personnes
|
||
_MEDICAL_STOP_WORDS_SET = {
|
||
# Mots français courants (déterminants, prépositions, adverbes, etc.)
|
||
"pas", "mon", "bien", "ancien", "ancienne", "bon", "bonne", "tout", "tous",
|
||
"mais", "donc", "car", "que", "qui", "avec", "dans", "pour", "sur", "par",
|
||
"les", "des", "une", "est", "son", "ses", "nos", "aux", "cette", "ces",
|
||
"cher", "chez", "entre", "sans", "sous", "vers", "selon", "après", "avant",
|
||
"puis", "aussi", "très", "plus", "moins", "peu", "non", "oui", "quelques",
|
||
"mise", "début", "fin", "suite", "fait", "lieu", "cas", "jour", "jours",
|
||
"semaine", "semaines", "mois", "temps", "place", "nouvelle", "nouveau",
|
||
"franche", "légère", "quelque", "depuis", "comme", "encore", "votre",
|
||
"date", "note", "notes", "nom", "heure", "matin", "soir", "midi",
|
||
"signé", "réalisé", "courrier", "cabinet", "rue",
|
||
# Verbes / participes courants
|
||
"remontée", "associée", "réalisée", "débuté", "prolongé", "prolongée",
|
||
"prescrit", "prescrite", "présente", "présent", "absente", "absent",
|
||
"reprise", "introduction", "arrêt", "relais",
|
||
# Titres / rôles hospitaliers
|
||
"chef", "assistant", "assistante", "praticien", "praticienne",
|
||
"docteur", "professeur", "hospitalier", "hospitalière", "hospitaliers",
|
||
"spécialiste", "contractuel", "contractuelle", "titulaire",
|
||
"confrère", "consoeur", "coordonnateur", "coordonnatrice",
|
||
"médecin", "médical", "infirmier", "infirmière",
|
||
"praticiens", "patient", "patiente",
|
||
# Structure hospitalière
|
||
"service", "pôle", "clinique", "consultation", "secrétariat",
|
||
"hôpital", "hôpitaux", "centre", "établissement", "polyclinique",
|
||
# Villes / géographie (pas des noms de personnes)
|
||
"bordeaux", "bayonne", "paris", "lyon", "lille", "marseille",
|
||
"toulouse", "nantes", "montpellier", "pessac", "biarritz", "soustons",
|
||
"basque", "basques", "sud", "côte",
|
||
# Médicaments génériques et spécialités (DCI + noms commerciaux)
|
||
"colchicine", "aspirine", "cortancyl", "bisoprolol", "entresto",
|
||
"methotrexate", "eplerenone", "speciafoldine", "prednisone",
|
||
"corticoïdes", "cortisone",
|
||
"paracetamol", "metformine", "solupred", "novorapid", "abasaglar",
|
||
"lovenox", "methylprednisolone", "potassium", "humalog", "furosemide",
|
||
"insuline", "trulicity", "forxiga", "atorvastatine", "amlodipine",
|
||
"ondansetron", "eliquis", "nebivolol", "gaviscon", "loxen",
|
||
"morphine", "oxycodone", "kardegic", "tercian", "zopiclone",
|
||
"seresta", "tramadol", "alprazolam", "forlax", "levothyrox",
|
||
"bromazepam", "gliclazide", "zymad", "pravastatine", "spiriva",
|
||
"quetiapine", "sertraline", "crestor", "lercanidipine", "amoxicilline",
|
||
"opocalcium", "ferinject", "candesartan", "ceftriaxone", "calcidose",
|
||
"laroxyl", "brintellix", "ketoprofene", "adrenaline", "exacyl",
|
||
"terbutaline", "ipratropium", "actiskenan", "vialebex", "oxynormoro",
|
||
"lansoprazole", "perindopril", "sodium", "velmetia",
|
||
"doliprane", "dafalgan", "efferalgan", "spasfon", "vogalene",
|
||
"augmentin", "inexium", "omeprazole", "pantoprazole", "esomeprazole",
|
||
"ramipril", "lisinopril", "enalapril", "losartan", "valsartan",
|
||
"irbesartan", "olmesartan", "telmisartan", "hydrochlorothiazide",
|
||
"spironolactone", "furosemide", "lasilix", "aldactone",
|
||
"tahor", "crestor", "rosuvastatine", "simvastatine", "fluvastatine",
|
||
"xarelto", "pradaxa", "apixaban", "rivaroxaban", "dabigatran",
|
||
"plavix", "clopidogrel", "ticagrelor", "brilique",
|
||
"ventoline", "seretide", "symbicort", "salmeterol", "fluticasone",
|
||
"salbutamol", "tiotropium", "budesonide", "beclometasone",
|
||
"oxycodone", "oxynorm", "skenan", "actiskenan", "fentanyl",
|
||
"nubain", "nalbuphine", "nefopam", "acupan", "profenid",
|
||
"ibuprofene", "diclofenac", "naproxene", "celecoxib",
|
||
"gabapentine", "pregabaline", "lyrica", "neurontin",
|
||
"amitriptyline", "duloxetine", "venlafaxine", "fluoxetine",
|
||
"paroxetine", "escitalopram", "citalopram", "mirtazapine",
|
||
"olanzapine", "risperidone", "aripiprazole", "haloperidol",
|
||
"loxapine", "cyamemazine", "diazepam", "oxazepam", "lorazepam",
|
||
"clonazepam", "midazolam", "hydroxyzine", "atarax", "melatonine",
|
||
"stilnox", "zolpidem", "imovane",
|
||
"levothyroxine", "metformine", "glimepiride", "sitagliptine",
|
||
"januvia", "jardiance", "empagliflozine", "dapagliflozine",
|
||
"ozempic", "semaglutide", "dulaglutide", "liraglutide", "victoza",
|
||
"heparine", "enoxaparine", "tinzaparine", "innohep",
|
||
"warfarine", "coumadine", "fluindione", "previscan",
|
||
"ciprofloxacine", "levofloxacine", "ofloxacine", "metronidazole",
|
||
"vancomycine", "gentamicine", "tazocilline", "piperacilline",
|
||
"meropenem", "imipenem", "clindamycine", "doxycycline",
|
||
"azithromycine", "clarithromycine", "cotrimoxazole", "bactrim",
|
||
"polyionique", "propranolol", "apidra", "solostar",
|
||
# Suffixes laboratoires pharmaceutiques
|
||
"arw", "myl", "myp", "arg", "teva", "bga", "agt",
|
||
# Formes galéniques / voies d'administration
|
||
"cpr", "sachet", "orale", "oral", "sol", "buv", "stylo", "flexpen",
|
||
"flestouch", "kwikpen", "inj", "susp", "gelule", "comprime",
|
||
"unidose", "perf", "inh", "seringue", "aerosol", "sach", "pdr",
|
||
"orodisp", "capsule", "patch", "suppositoire", "gouttes",
|
||
# Termes de prescription / pharmacie
|
||
"prescription", "prescriptions", "dose", "fréquence", "statut",
|
||
"technique", "capteur", "bandelettes", "glycemiques", "glycemique",
|
||
"lancettes", "aiguilles", "fines", "micro", "pompe", "réserve",
|
||
"glycemie", "capillaire",
|
||
# Termes médicaux / cliniques
|
||
"myocardite", "myosite", "corticothérapie", "biopsie", "pathologie",
|
||
"dysimmunitaire", "récidive", "récidivante", "traitement", "diagnostic",
|
||
"antécédents", "examen", "bilan", "résultats", "analyse",
|
||
"interne", "externe", "médecine", "chirurgie", "rhumatologie",
|
||
"dermatologie", "immunologie", "cardiologie", "pneumologie",
|
||
"neurologie", "gynécologie", "radiologie", "sénologie",
|
||
"douleur", "douleurs", "douloureux", "musculaire", "musculaires",
|
||
"thoracique", "thoraciques", "membres", "supérieurs", "inférieurs",
|
||
"normale", "normaux", "habituelle", "habituelles",
|
||
"synthèse", "hospitalisation", "syndrome", "vaccination", "ophtalmo",
|
||
"pelvien", "diabétique", "sommeil", "régime", "diet",
|
||
"desinfection", "environnement", "identification", "bracelet",
|
||
"toilettes", "accompagner", "installer", "transfusion",
|
||
"signes", "vitaux", "alimentaire", "avis", "zone",
|
||
"calcémie",
|
||
# Abréviations médicales
|
||
"irm", "ett", "ecg", "mtx", "fevg", "bdc", "crp", "sfu", "hdj",
|
||
"bnp", "asat", "alat", "cpk", "ctc", "hba", "hba1c",
|
||
"saos", "tsh", "inr", "vgm", "pnn", "plq", "hb",
|
||
"poc", "bax", "act", "bic", "cfx", "acc", "ado", "acf", "vfo",
|
||
"qvl", "cci", "pse", "pca", "chl", "crt", "bbm", "pds", "ren",
|
||
"vit", "zen",
|
||
"scanner", "radio", "écho", "échographie",
|
||
# Spécialités médicales (éviter faux positifs NOM)
|
||
"hépato-gastro-entérologue", "gastro-entérologue", "gastro-entérologie",
|
||
"proctologue", "oncologue", "anesthésiste", "pneumologue", "gérontologue",
|
||
"cardiologue", "néphrologue", "urologue", "gériatre",
|
||
"hépatologue", "endocrinologue", "stomatologue",
|
||
# Mots clés de contexte document
|
||
"compétences", "maladies", "inflammatoires", "systémiques", "rares",
|
||
"fret", "fax", "contexte", "résultat", "resultat", "résultats", "resultats",
|
||
"haute", "maison", "aide", "rpps", "poste", "fonct",
|
||
"sante", "santé", "etxe", "ttipi", "gastro", "concha",
|
||
"endoscopie", "endoscopique", "fibroscopie",
|
||
"indication", "conclusion", "technique", "anesthésie",
|
||
"digestif", "digestive", "digestives", "nutritive",
|
||
}
|
||
_MEDICAL_STOP_WORDS = (
|
||
r"(?:" + "|".join(re.escape(w) for w in _MEDICAL_STOP_WORDS_SET) + r")"
|
||
)
|
||
# Un token de nom : commence par majuscule, lettres/tirets/apostrophes (PAS d'espace ni de point)
|
||
_PERSON_TOKEN = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+"
|
||
RE_PERSON_CONTEXT = re.compile(
|
||
r"(?:(?:Dr\.?|DR\.?|Docteur|Mme|MME|Madame|M\.|Mr\.?|Monsieur"
|
||
r"|Nom\s*:\s*"
|
||
r"|Rédigé\s+par|Validé\s+par|Signé\s+par|Saisi\s+par"
|
||
r")\s+)"
|
||
rf"({_PERSON_TOKEN}(?:\s+{_PERSON_TOKEN}){{0,2}})" # Max 3 mots
|
||
)
|
||
|
||
# Noms en MAJUSCULES dans des listes virgulées (ex: "le Dr X, Y, LAZARO")
|
||
RE_DR_COMMA_LIST = re.compile(
|
||
r"(?:Dr\.?|DR\.?|Docteur)\s+"
|
||
r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' .]+"
|
||
r"(?:\s*,\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' .]+)+",
|
||
re.IGNORECASE,
|
||
)
|
||
# Token nom : mot commençant par une majuscule d'au moins 3 lettres
|
||
_NAME_TOKEN_RE = re.compile(r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']{2,}")
|
||
SPLITTER = re.compile(r"\s*[:|;\t]\s*")
|
||
|
||
# --- Extraction globale de noms depuis champs structurés ---
|
||
_UC_NAME_TOKEN = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+"
|
||
RE_EXTRACT_PATIENT = re.compile(
|
||
r"Patient\(?e?\)?\s*:\s*"
|
||
rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)"
|
||
r"(?=\s+Né|\s+né|\s+N°|\s*$)",
|
||
re.MULTILINE,
|
||
)
|
||
# Champs d'identité structurés (documents trackare / DPI)
|
||
RE_EXTRACT_NOM_NAISSANCE = re.compile(
|
||
r"Nom\s+de\s+naissance\s*:\s*"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+?)(?:\s+IPP|\s*$)",
|
||
re.MULTILINE,
|
||
)
|
||
RE_EXTRACT_NOM_PRENOM = re.compile(
|
||
r"Nom\s+et\s+Pr[ée]nom\s*:\s*"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+?)(?:\s+Date|\s+Né|\s*$)",
|
||
re.MULTILINE,
|
||
)
|
||
RE_EXTRACT_LIEU_NAISSANCE = re.compile(
|
||
r"Lieu\s+de\s+naissance\s*:\s*"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+?)(?:\s*$)",
|
||
re.MULTILINE,
|
||
)
|
||
RE_EXTRACT_VILLE_RESIDENCE = re.compile(
|
||
r"Ville\s+de\s+r[ée]sidence\s*:\s*"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+?)(?:\s*$)",
|
||
re.MULTILINE,
|
||
)
|
||
# Contacts structurés : Conjoint/Concubin/Epoux/Epouse/Parent + NOM PRENOM
|
||
RE_EXTRACT_CONTACT = re.compile(
|
||
r"(?:Conjoint|Concubin|Epoux|Epouse|Parent|Père|Mère|Fils|Fille|Frère|Soeur|Tuteur)\s+"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+)"
|
||
r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\']+))?",
|
||
)
|
||
RE_EXTRACT_REDIGE = re.compile(
|
||
r"(?:Rédigé|Validé|Signé|Saisi)\s+par\s+"
|
||
rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
|
||
)
|
||
# Token nom composé : JEAN-PIERRE, CAZELLES-BOUDIER, etc.
|
||
_UC_COMPOUND = r"[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}(?:-[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,})*"
|
||
RE_EXTRACT_MME_MR = re.compile(
|
||
r"(?:MME|Mme|Madame|Monsieur|Mr?\.?)\s+"
|
||
r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*(?:-?\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*)?)?"
|
||
rf"((?:{_UC_COMPOUND})(?:\s+(?:{_UC_COMPOUND}))*)",
|
||
)
|
||
_INITIAL_OPT = r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*(?:-?\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]\.\s*)?)?"
|
||
RE_EXTRACT_DR_DEST = re.compile(
|
||
r"(?:DR\.?|Dr\.?|Docteur)\s+"
|
||
+ _INITIAL_OPT +
|
||
rf"((?:{_UC_NAME_TOKEN})(?:\s+(?:{_UC_NAME_TOKEN}))*)",
|
||
)
|
||
# Noms du personnel médical après un rôle : "Aide : Marie-Paule BORDABERRY"
|
||
RE_EXTRACT_STAFF_ROLE = re.compile(
|
||
r"(?:Aide|Infirmière?|IDE|IADE|IBODE|ASH?|Cadre\s+Infirmier)\s*:\s*"
|
||
r"((?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+(?:\s*-\s*[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][a-zéèàùâêîôûäëïöüç]+)?\s+)?"
|
||
r"(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,}[\-]?)(?:[\s\-]+[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{2,})*)",
|
||
)
|
||
|
||
CID_PATTERN = re.compile(r"\(cid:\d+\)")
|
||
|
||
# --- Nouvelles regex : dates, adresses, âges, dossiers ---
|
||
_MOIS_FR = r"(?:janvier|février|mars|avril|mai|juin|juillet|août|septembre|octobre|novembre|décembre)"
|
||
RE_DATE_NAISSANCE = re.compile(
|
||
r"(?:n[ée]+\s+le|date\s+de\s+naissance|DDN)\s*[:\-]?\s*"
|
||
r"(\d{1,2}[\s/.\-]\d{1,2}[\s/.\-]\d{2,4}|\d{1,2}\s+" + _MOIS_FR + r"\s+\d{4})",
|
||
re.IGNORECASE,
|
||
)
|
||
RE_DATE = re.compile(
|
||
r"\b(\d{1,2})\s*[/.\-]\s*(\d{1,2})\s*[/.\-]\s*(\d{4})\b"
|
||
r"|"
|
||
r"\b(\d{1,2})\s+" + _MOIS_FR + r"\s+(\d{4})\b",
|
||
re.IGNORECASE,
|
||
)
|
||
RE_ADRESSE = re.compile(
|
||
r"\b\d{1,4}[\s,]*(?:bis|ter)?\s*,?\s*"
|
||
r"(?:rue|avenue|av\.?|boulevard|bd\.?|place|chemin|all[ée]e|impasse|route|cours|passage|square|r[ée]sidence)"
|
||
r"\s+[A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûäëïöüç\s\-']{2,}",
|
||
re.IGNORECASE,
|
||
)
|
||
RE_CODE_POSTAL = re.compile(
|
||
r"(?:(?:[Cc]ode\s*[Pp]ostal|CP)\s*[:\-]?\s*(\d{5}))"
|
||
r"|"
|
||
# 5 chiffres + nom de ville (Title Case ou MAJUSCULES), pas précédé d'un chiffre (évite RPPS)
|
||
r"(?:(?<!\d)(\d{5})[ \t]+[A-ZÉÈÀÙ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû]+"
|
||
r"(?:[\s\-][A-ZÉÈÀÙ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû]+)*"
|
||
r"(?:\s+CEDEX)?)",
|
||
)
|
||
RE_BP = re.compile(
|
||
r"(?:[A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû\.\-]+\s+)?BP\s+\d+",
|
||
re.IGNORECASE,
|
||
)
|
||
RE_AGE = re.compile(
|
||
r"(?:âg[ée]+\s+de\s+|patient(?:e)?\s+de\s+)(\d{1,3})\s*ans\b",
|
||
re.IGNORECASE,
|
||
)
|
||
# Établissements de santé : avec nom (EHPAD Bayonne) ou seuls (EHPAD, SSR, USLD)
|
||
RE_ETABLISSEMENT = re.compile(
|
||
r"\b((?:EHPAD|SSR|USLD|HAD|SSR/USLD|CSAPA|CMP|CMPP|UGA)"
|
||
r"(?:\s+(?:de\s+|d['']\s*)?[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-']+)*)",
|
||
)
|
||
RE_HOPITAL_VILLE = re.compile(
|
||
r"\b((?:[Hh]ôpital|[Cc]linique|[Pp]olyclinique|[Cc]entre\s+[Hh]ospitalier)"
|
||
r"\s+(?:de\s+|d['']\s*)?(?:[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-']+)"
|
||
r"(?:\s+(?:de\s+|d['']\s*)?[A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-']+)*)",
|
||
)
|
||
RE_NUMERO_DOSSIER = re.compile(
|
||
r"(?:dossier|n°\s*dossier|NDA)\s*[:\-n°]+\s*([A-Za-z0-9\-/]{4,})"
|
||
r"|"
|
||
r"(?:référence|réf\.)\s*[:\-n°]+\s*([A-Za-z0-9\-/]{4,})",
|
||
re.IGNORECASE,
|
||
)
|
||
RE_EPISODE = re.compile(
|
||
r"N°\s*[ÉéEe]pisode\s*[:\-]?\s*([A-Za-z0-9\-]{4,})",
|
||
re.IGNORECASE,
|
||
)
|
||
|
||
@dataclass
|
||
class PiiHit:
|
||
page: int
|
||
kind: str
|
||
original: str
|
||
placeholder: str
|
||
bbox_hint: Optional[Tuple[float, float, float, float]] = None
|
||
|
||
@dataclass
|
||
class AnonResult:
|
||
text_out: str
|
||
tables_block: str
|
||
audit: List[PiiHit] = field(default_factory=list)
|
||
|
||
# ----------------- Config loader -----------------
|
||
|
||
def load_dictionaries(config_path: Optional[Path]) -> Dict[str, Any]:
|
||
cfg = DEFAULTS_CFG.copy()
|
||
if config_path and config_path.exists() and yaml is not None:
|
||
try:
|
||
user = yaml.safe_load(config_path.read_text(encoding="utf-8")) or {}
|
||
for k, v in user.items():
|
||
cfg[k] = v
|
||
except Exception:
|
||
pass
|
||
return cfg
|
||
|
||
# ----------------- Extraction -----------------
|
||
|
||
def extract_text_with_fallback_ocr(pdf_path: Path) -> Tuple[List[str], List[List[str]], bool]:
|
||
"""Extraction texte multi-passes avec fallback OCR (docTR).
|
||
Retourne (pages_text, tables_lines, ocr_used).
|
||
"""
|
||
pages_text: List[str] = []
|
||
tables_lines: List[List[str]] = []
|
||
ocr_used = False
|
||
with pdfplumber.open(pdf_path) as pdf:
|
||
for p in pdf.pages:
|
||
t = p.extract_text(x_tolerance=2.5, y_tolerance=4.0) or ""
|
||
pages_text.append(t)
|
||
rows: List[str] = []
|
||
try:
|
||
tables = p.extract_tables()
|
||
for tbl in tables or []:
|
||
for row in tbl:
|
||
clean = [c if c is not None else "" for c in row]
|
||
rows.append("\t".join(clean).strip())
|
||
except Exception:
|
||
pass
|
||
tables_lines.append(rows)
|
||
total_chars = sum(len(x or "") for x in pages_text)
|
||
need_fallback = total_chars < 500
|
||
if not need_fallback:
|
||
need_fallback = any(CID_PATTERN.search(x or "") for x in pages_text)
|
||
if need_fallback:
|
||
text_all = pdfminer_extract_text(
|
||
str(pdf_path),
|
||
laparams=LAParams(char_margin=2.0, word_margin=0.1, line_margin=0.8, boxes_flow=0.5),
|
||
)
|
||
split = [x for x in text_all.split("\f") if x]
|
||
if split:
|
||
pages_text = split
|
||
# 3e passe PyMuPDF si toujours pauvre/cid
|
||
total_chars = sum(len(x or "") for x in pages_text)
|
||
if (total_chars < 500 or any(CID_PATTERN.search(x or "") for x in pages_text)) and fitz is not None:
|
||
try:
|
||
doc = fitz.open(str(pdf_path))
|
||
pages_text = [doc[i].get_text("text") or "" for i in range(len(doc))]
|
||
doc.close()
|
||
except Exception:
|
||
pass
|
||
# 4e passe : OCR docTR si toujours très peu de texte (PDF scanné)
|
||
total_chars = sum(len(x or "") for x in pages_text)
|
||
if total_chars < 200 and _DOCTR_AVAILABLE and fitz is not None:
|
||
try:
|
||
model = _doctr_ocr_predictor(det_arch="db_resnet50", reco_arch="crnn_vgg16_bn", pretrained=True)
|
||
doc = fitz.open(str(pdf_path))
|
||
ocr_pages: List[str] = []
|
||
for i in range(len(doc)):
|
||
pix = doc[i].get_pixmap(dpi=300)
|
||
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
||
import numpy as np
|
||
result = model([np.array(img)])
|
||
page_text = ""
|
||
for block in result.pages[0].blocks:
|
||
for line in block.lines:
|
||
words = [w.value for w in line.words]
|
||
page_text += " ".join(words) + "\n"
|
||
ocr_pages.append(page_text)
|
||
doc.close()
|
||
if sum(len(p) for p in ocr_pages) > total_chars:
|
||
pages_text = ocr_pages
|
||
ocr_used = True
|
||
except Exception:
|
||
pass
|
||
return pages_text, tables_lines, ocr_used
|
||
|
||
|
||
# Alias pour compatibilité ascendante
|
||
def extract_text_three_passes(pdf_path: Path):
|
||
pages_text, tables_lines, _ = extract_text_with_fallback_ocr(pdf_path)
|
||
return pages_text, tables_lines
|
||
|
||
# ----------------- Helpers -----------------
|
||
|
||
def _compile_user_regex(pattern: str, flags_list: List[str]):
|
||
flags = 0
|
||
for f in flags_list or []:
|
||
u = f.upper()
|
||
if u == "IGNORECASE": flags |= re.IGNORECASE
|
||
if u == "MULTILINE": flags |= re.MULTILINE
|
||
if u == "DOTALL": flags |= re.DOTALL
|
||
return re.compile(pattern, flags)
|
||
|
||
|
||
def _apply_overrides(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict[str, Any]) -> str:
|
||
for ov in cfg.get("regex_overrides", []) or []:
|
||
pattern = ov.get("pattern"); placeholder = ov.get("placeholder", PLACEHOLDERS["MASK"]) ; name = ov.get("name", "override")
|
||
flags_list = ov.get("flags", [])
|
||
try:
|
||
rx = _compile_user_regex(pattern, flags_list)
|
||
except Exception:
|
||
continue
|
||
def _rep(m: re.Match):
|
||
audit.append(PiiHit(page_idx, name, m.group(0), placeholder))
|
||
return placeholder
|
||
line = rx.sub(_rep, line)
|
||
# force-mask literals
|
||
for term in (cfg.get("blacklist", {}).get("force_mask_terms", []) or []):
|
||
if not term: continue
|
||
word_rx = re.compile(rf"\b{re.escape(term)}\b", re.IGNORECASE)
|
||
if word_rx.search(line):
|
||
audit.append(PiiHit(page_idx, "force_term", term, PLACEHOLDERS["MASK"]))
|
||
line = word_rx.sub(PLACEHOLDERS["MASK"], line)
|
||
# force-mask regex
|
||
for pat in (cfg.get("blacklist", {}).get("force_mask_regex", []) or []):
|
||
try:
|
||
rx = re.compile(pat, re.IGNORECASE)
|
||
except Exception:
|
||
continue
|
||
if rx.search(line):
|
||
audit.append(PiiHit(page_idx, "force_regex", pat, PLACEHOLDERS["MASK"]))
|
||
line = rx.sub(PLACEHOLDERS["MASK"], line)
|
||
return line
|
||
|
||
|
||
def _mask_admin_label(line: str, audit: List[PiiHit], page_idx: int) -> str:
|
||
m = RE_FINESS.search(line)
|
||
if m:
|
||
val = m.group(1); audit.append(PiiHit(page_idx, "FINESS", val, PLACEHOLDERS["FINESS"]))
|
||
return RE_FINESS.sub(lambda _: f"FINESS : {PLACEHOLDERS['FINESS']}", line)
|
||
m = RE_OGC.search(line)
|
||
if m:
|
||
val = m.group(1); audit.append(PiiHit(page_idx, "OGC", val, PLACEHOLDERS["OGC"]))
|
||
return RE_OGC.sub(lambda _: f"N° OGC : {PLACEHOLDERS['OGC']}", line)
|
||
m = RE_IPP.search(line)
|
||
if m:
|
||
val = m.group(1); audit.append(PiiHit(page_idx, "IPP", val, PLACEHOLDERS["IPP"]))
|
||
return RE_IPP.sub(lambda _: f"IPP : {PLACEHOLDERS['IPP']}", line)
|
||
m = RE_RPPS.search(line)
|
||
if m:
|
||
val = m.group(1); audit.append(PiiHit(page_idx, "RPPS", val, PLACEHOLDERS["RPPS"]))
|
||
return RE_RPPS.sub(lambda _: f"RPPS : {PLACEHOLDERS['RPPS']}", line)
|
||
return line
|
||
|
||
|
||
def _mask_line_by_regex(line: str, audit: List[PiiHit], page_idx: int, cfg: Dict[str, Any]) -> str:
|
||
# user overrides & force-masks d'abord
|
||
line = _apply_overrides(line, audit, page_idx, cfg)
|
||
|
||
# EMAIL
|
||
def _repl_email(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "EMAIL", m.group(0), PLACEHOLDERS["EMAIL"]))
|
||
return PLACEHOLDERS["EMAIL"]
|
||
line = RE_EMAIL.sub(_repl_email, line)
|
||
|
||
# TEL
|
||
def _repl_tel(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "TEL", m.group(0), PLACEHOLDERS["TEL"]))
|
||
return PLACEHOLDERS["TEL"]
|
||
line = RE_TEL.sub(_repl_tel, line)
|
||
|
||
# IBAN
|
||
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 — désactivé : seules les dates de naissance sont masquées
|
||
# 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)
|
||
|
||
# BOITE POSTALE (BP)
|
||
def _repl_bp(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "ADRESSE", m.group(0), PLACEHOLDERS["ADRESSE"]))
|
||
return PLACEHOLDERS["ADRESSE"]
|
||
line = RE_BP.sub(_repl_bp, 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)
|
||
|
||
# N° EPISODE
|
||
def _repl_episode(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "EPISODE", m.group(0), PLACEHOLDERS["EPISODE"]))
|
||
return PLACEHOLDERS["EPISODE"]
|
||
line = RE_EPISODE.sub(_repl_episode, line)
|
||
|
||
# Établissements de santé (EHPAD Bayonne, SSR La Concha, Hôpital de Bayonne, etc.)
|
||
def _repl_etab(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "ETAB", m.group(0), PLACEHOLDERS["ETAB"]))
|
||
return PLACEHOLDERS["ETAB"]
|
||
line = RE_ETABLISSEMENT.sub(_repl_etab, line)
|
||
line = RE_HOPITAL_VILLE.sub(_repl_etab, line)
|
||
|
||
# Champs structurés : Lieu de naissance, Ville de résidence (masquage direct, sans filtre stop words)
|
||
_re_lieu = re.compile(r"(Lieu\s+de\s+naissance\s*:\s*)([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+)")
|
||
def _repl_lieu(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "VILLE", m.group(2).strip(), PLACEHOLDERS["VILLE"]))
|
||
return m.group(1) + PLACEHOLDERS["VILLE"]
|
||
line = _re_lieu.sub(_repl_lieu, line)
|
||
|
||
_re_ville_res = re.compile(r"(Ville\s+de\s+r[ée]sidence\s*:\s*)([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-\' ]+)")
|
||
def _repl_ville_res(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "VILLE", m.group(2).strip(), PLACEHOLDERS["VILLE"]))
|
||
return m.group(1) + PLACEHOLDERS["VILLE"]
|
||
line = _re_ville_res.sub(_repl_ville_res, 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 [])
|
||
|
||
_stop_rx = re.compile(_MEDICAL_STOP_WORDS, re.IGNORECASE)
|
||
|
||
def _clean_name_span(span: str) -> str:
|
||
"""Tronque le span au premier mot médical/stop word."""
|
||
tokens = span.split()
|
||
clean = []
|
||
for t in tokens:
|
||
if _stop_rx.fullmatch(t):
|
||
break
|
||
clean.append(t)
|
||
return " ".join(clean).strip(" .-'")
|
||
|
||
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
|
||
# Tronquer avant les mots médicaux
|
||
cleaned = _clean_name_span(span)
|
||
if not cleaned:
|
||
return raw
|
||
tokens = [t for t in cleaned.split() if t]
|
||
if len(tokens) == 1 and len(tokens[0]) <= 3: return raw
|
||
audit.append(PiiHit(page_idx, "NOM", cleaned, PLACEHOLDERS["NOM"]))
|
||
return raw.replace(cleaned, PLACEHOLDERS["NOM"])
|
||
|
||
line = RE_PERSON_CONTEXT.sub(_repl_person_ctx, line)
|
||
|
||
# Passe supplémentaire : noms dans des listes virgulées après "Dr"
|
||
# ex: "le Dr DUVAL, MACHELART, LAZARO" → masquer chaque nom
|
||
for m in RE_DR_COMMA_LIST.finditer(line):
|
||
fragment = m.group(0)
|
||
# Extraire les segments séparés par des virgules (sauf le premier qui inclut "Dr")
|
||
parts = [p.strip() for p in fragment.split(",")]
|
||
for part in parts:
|
||
# Extraire les tokens nom de chaque segment
|
||
for tok in _NAME_TOKEN_RE.findall(part):
|
||
if tok in wl_sections or len(tok) <= 2:
|
||
continue
|
||
if _stop_rx.fullmatch(tok):
|
||
continue
|
||
if tok not in line:
|
||
continue
|
||
# Vérifier qu'il n'est pas déjà masqué
|
||
if f"[{tok}]" in line or tok in {v for v in PLACEHOLDERS.values()}:
|
||
continue
|
||
audit.append(PiiHit(page_idx, "NOM", tok, PLACEHOLDERS["NOM"]))
|
||
line = re.sub(rf"\b{re.escape(tok)}\b", PLACEHOLDERS["NOM"], line)
|
||
|
||
return line
|
||
|
||
|
||
def _mask_critical_in_key(key: str, audit: List[PiiHit], page_idx: int) -> str:
|
||
"""Masque les TEL et EMAIL même dans la partie 'clé' d'une ligne clé:valeur."""
|
||
def _repl_tel(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "TEL", m.group(0), PLACEHOLDERS["TEL"]))
|
||
return PLACEHOLDERS["TEL"]
|
||
key = RE_TEL.sub(_repl_tel, key)
|
||
def _repl_email(m: re.Match) -> str:
|
||
audit.append(PiiHit(page_idx, "EMAIL", m.group(0), PLACEHOLDERS["EMAIL"]))
|
||
return PLACEHOLDERS["EMAIL"]
|
||
key = RE_EMAIL.sub(_repl_email, key)
|
||
return key
|
||
|
||
|
||
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_key = _mask_critical_in_key(key, audit, page_idx)
|
||
masked_val = _mask_line_by_regex(value, audit, page_idx, cfg)
|
||
return f"{masked_key.strip()} : {masked_val.strip()}"
|
||
else:
|
||
return _mask_line_by_regex(line, audit, page_idx, cfg)
|
||
|
||
# ----------------- Extraction globale de noms -----------------
|
||
|
||
def _is_trackare_document(text: str) -> bool:
|
||
"""Détecte si le document est un export Trackare/TrakCare (DPI structuré)."""
|
||
markers = ["Détails des patients", "Nom de naissance", "Dossier Patient"]
|
||
t = text[:3000].lower()
|
||
return sum(1 for m in markers if m.lower() in t) >= 2
|
||
|
||
|
||
def _extract_trackare_identity(full_text: str) -> Tuple[set, List[PiiHit]]:
|
||
"""Parse les champs structurés d'un document Trackare pour extraire les PII.
|
||
Retourne (name_tokens, pii_hits) avec les noms à masquer et les hits additionnels."""
|
||
names: set = set()
|
||
hits: List[PiiHit] = []
|
||
|
||
def _add_name(s: str):
|
||
for tok in s.split():
|
||
tok = tok.strip(" .-'(),")
|
||
if len(tok) >= 2 and tok[0].isupper():
|
||
names.add(tok)
|
||
|
||
# --- Identité patient ---
|
||
# Nom de naissance: DIEGO
|
||
m = re.search(r"Nom\s+de\s+naissance\s*:\s*(.+?)(?:\s+IPP\b|\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
_add_name(m.group(1).strip())
|
||
|
||
# Nom et Prénom: DIEGO PATRICIA
|
||
m = re.search(r"Nom\s+et\s+Pr[ée]nom\s*:\s*(.+?)(?:\s+Date\s+de\s+naissance|\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
_add_name(m.group(1).strip())
|
||
|
||
# Lieu de naissance: BAYONNE → masquer comme VILLE
|
||
m = re.search(r"Lieu\s+de\s+naissance\s*:\s*([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû\s\-']+?)(?:\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
val = m.group(1).strip()
|
||
hits.append(PiiHit(-1, "VILLE", val, PLACEHOLDERS["VILLE"]))
|
||
names.add(val)
|
||
|
||
# Ville de résidence: TARNOS → masquer comme VILLE
|
||
m = re.search(r"Ville\s+de\s+r[ée]sidence\s*:\s*([A-ZÉÈÀÙÂÊÎÔÛ][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû\s\-']+?)(?:\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
val = m.group(1).strip()
|
||
hits.append(PiiHit(-1, "VILLE", val, PLACEHOLDERS["VILLE"]))
|
||
names.add(val)
|
||
|
||
# Code Postal (seul sur la ligne "Nationalité: FRANCE Code Postal: 40220")
|
||
m = re.search(r"[Cc]ode\s*[Pp]ostal\s*:\s*(\d{5})", full_text)
|
||
if m:
|
||
hits.append(PiiHit(-1, "CODE_POSTAL", m.group(1), PLACEHOLDERS["CODE_POSTAL"]))
|
||
|
||
# Adresse patient
|
||
m = re.search(r"Adresse\s*:\s*(.+?)(?:\s+Ville\s+de\s+r[ée]sidence|\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
val = m.group(1).strip()
|
||
if len(val) > 3:
|
||
hits.append(PiiHit(-1, "ADRESSE", val, PLACEHOLDERS["ADRESSE"]))
|
||
|
||
# --- Pied de page : "Patient : NOM PRENOM - Date de naissance..." ---
|
||
for m in re.finditer(r"Patient\s*:\s*(.+?)\s*-\s*Date\s+de\s+naissance", full_text):
|
||
_add_name(m.group(1).strip())
|
||
|
||
# --- Médecin courant ---
|
||
m = re.search(r"Médecin\s+courant\s*:\s*(?:DR\.?\s*)?(.+?)(?:\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
_add_name(m.group(1).strip())
|
||
|
||
# --- Médecin traitant (ligne après "Nom Adresse Téléphone") ---
|
||
m = re.search(r"Médecin\s+traitant\s*\n.*?Nom\s+Adresse\s+Téléphone\s*\n\s*(?:DR\.?\s*)?(.+?)(?:\d{5}|\s*$)", full_text, re.MULTILINE)
|
||
if m:
|
||
_add_name(m.group(1).strip())
|
||
|
||
# --- Contacts structurés ---
|
||
# Pattern: Relation NOM PRENOM [ADRESSE] [TEL]
|
||
for m in re.finditer(
|
||
r"(?:Conjoint|Concubin|Epoux|Epouse|Parent|Père|Mère|Fils|Fille|Frère|Soeur|Tuteur)\s+"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûä\-']+)"
|
||
r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôû][A-ZÉÈÀÙÂÊÎÔÛa-zéèàùâêîôûä\-']+))?",
|
||
full_text,
|
||
):
|
||
_add_name(m.group(1))
|
||
if m.group(2):
|
||
_add_name(m.group(2))
|
||
|
||
# --- Médecins urgences (IAO, prise en charge, décision) ---
|
||
for m in re.finditer(r"IAO\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+)", full_text):
|
||
_add_name(m.group(1))
|
||
for m in re.finditer(
|
||
r"Médecin\s+de\s+la\s+(?:prise\s+en\s+charge|décision)\s+médicale\s+"
|
||
r"([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+)"
|
||
r"(?:\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ][A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇa-zéèàùâêîôûäëïöüç\-]+))?",
|
||
full_text,
|
||
):
|
||
_add_name(m.group(1))
|
||
if m.group(2):
|
||
_add_name(m.group(2))
|
||
|
||
# --- Noms soignants dans les Notes d'évolution ---
|
||
# Pattern: "Note d'évolution PRENOM NOM" ou "NOM HH:MM texte..."
|
||
for m in re.finditer(r"Note\s+d'[ée]volution\s+([A-ZÉÈÀÙÂÊÎÔÛ][a-zéèàùâêîôû]+)\s+([A-ZÉÈÀÙÂÊÎÔÛ]{2,})", full_text):
|
||
_add_name(m.group(1))
|
||
_add_name(m.group(2))
|
||
|
||
# Filtrer les tokens trop courts ou stop words (sauf noms de villes extraits explicitement)
|
||
city_tokens = {h.original for h in hits if h.kind == "VILLE"}
|
||
filtered = set()
|
||
for tok in names:
|
||
if tok in city_tokens:
|
||
filtered.add(tok)
|
||
continue
|
||
if len(tok) < 3:
|
||
continue
|
||
if tok.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
filtered.add(tok)
|
||
|
||
return filtered, hits
|
||
|
||
|
||
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:
|
||
continue
|
||
if token.upper() in wl_sections or token in wl_phrases:
|
||
continue
|
||
if token.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
names.add(token)
|
||
|
||
def _add_tokens_force_first(match_str):
|
||
"""Comme _add_tokens mais force le 1er token (contexte Dr/Mme fort)."""
|
||
tokens = match_str.split()
|
||
for i, token in enumerate(tokens):
|
||
token = token.strip(" .-'")
|
||
if len(token) < 2:
|
||
continue
|
||
if i == 0:
|
||
# Premier token après Dr/Mme : toujours un nom, bypass stop words
|
||
if token.upper() not in wl_sections:
|
||
names.add(token)
|
||
else:
|
||
if len(token) < 3:
|
||
continue
|
||
if token.upper() in wl_sections or token in wl_phrases:
|
||
continue
|
||
if token.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
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_force_first(m.group(1))
|
||
for m in RE_EXTRACT_DR_DEST.finditer(full_text):
|
||
_add_tokens_force_first(m.group(1))
|
||
# Champs d'identité structurés (trackare / DPI)
|
||
for m in RE_EXTRACT_NOM_NAISSANCE.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_NOM_PRENOM.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_LIEU_NAISSANCE.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
for m in RE_EXTRACT_VILLE_RESIDENCE.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
# Contacts structurés (conjoint, concubin, etc.)
|
||
for m in RE_EXTRACT_CONTACT.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
if m.group(2):
|
||
_add_tokens(m.group(2))
|
||
# Personnel médical avec rôle (Aide, Cadre Infirmier, etc.)
|
||
for m in RE_EXTRACT_STAFF_ROLE.finditer(full_text):
|
||
_add_tokens(m.group(1))
|
||
|
||
# Extraction des noms dans les listes virgulées après Dr/Docteur
|
||
# ex: "le Dr DUVAL, MACHELART, CHARLANNE, LAZARO, il a été proposé"
|
||
for m in RE_DR_COMMA_LIST.finditer(full_text):
|
||
fragment = m.group(0)
|
||
parts = [p.strip() for p in fragment.split(",")]
|
||
for part in parts:
|
||
for tok in _NAME_TOKEN_RE.findall(part):
|
||
tok = tok.strip(" .-'")
|
||
if len(tok) < 3:
|
||
continue
|
||
if tok.upper() in wl_sections or tok in wl_phrases:
|
||
continue
|
||
if tok.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
names.add(tok)
|
||
|
||
# Retirer les sous-parties de noms composés avec tiret
|
||
# Si "JEAN-PIERRE" est dans names, retirer "JEAN" et "PIERRE" individuels
|
||
compound_names = {n for n in names if "-" in n}
|
||
parts_to_remove = set()
|
||
for compound in compound_names:
|
||
for part in compound.split("-"):
|
||
part = part.strip()
|
||
if len(part) >= 2 and part in names:
|
||
parts_to_remove.add(part)
|
||
names -= parts_to_remove
|
||
|
||
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)
|
||
new_text = []
|
||
last_end = 0
|
||
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
|
||
# Ne pas remplacer si le token fait partie d'un mot composé (tiret)
|
||
if m.start() > 0 and text[m.start() - 1] == "-":
|
||
continue
|
||
if m.end() < len(text) and text[m.end()] == "-":
|
||
continue
|
||
audit.append(PiiHit(-1, "NOM_EXTRACTED", m.group(0), placeholder))
|
||
new_text.append(text[last_end:m.start()])
|
||
new_text.append(placeholder)
|
||
last_end = m.end()
|
||
new_text.append(text[last_end:])
|
||
text = "".join(new_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 0b : si document Trackare, extraction renforcée des PII structurés
|
||
if _is_trackare_document(full_raw):
|
||
trackare_names, trackare_hits = _extract_trackare_identity(full_raw)
|
||
extracted_names.update(trackare_names)
|
||
audit.extend(trackare_hits)
|
||
|
||
# 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) # désactivé
|
||
protected = RE_ADRESSE.sub(PLACEHOLDERS["ADRESSE"], protected)
|
||
protected = RE_BP.sub(PLACEHOLDERS["ADRESSE"], protected)
|
||
protected = RE_CODE_POSTAL.sub(PLACEHOLDERS["CODE_POSTAL"], protected)
|
||
# N° Episode
|
||
protected = RE_EPISODE.sub(PLACEHOLDERS["EPISODE"], protected)
|
||
# N° RPPS
|
||
protected = RE_RPPS.sub(PLACEHOLDERS["RPPS"], protected)
|
||
# Établissements
|
||
protected = RE_ETABLISSEMENT.sub(PLACEHOLDERS["ETAB"], protected)
|
||
protected = RE_HOPITAL_VILLE.sub(PLACEHOLDERS["ETAB"], 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)
|
||
# Fallback : chercher chaque mot individuellement (uniquement pour les NOM)
|
||
if not rects and " " in token and h.kind in {"NOM", "NOM_EXTRACTED", "NER_PER", "EDS_NOM"}:
|
||
for word in token.split():
|
||
word = word.strip(" .-'")
|
||
if len(word) < 3 or word.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
if not word[0].isupper():
|
||
continue
|
||
rects.extend(page.search_for(word))
|
||
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, ogc_label: Optional[str] = None) -> 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)
|
||
# Fallback : si la chaîne complète n'est pas trouvée,
|
||
# chercher chaque mot individuellement (uniquement pour les NOM)
|
||
if not found and " " in token and h.kind in {"NOM", "NOM_EXTRACTED", "NER_PER", "EDS_NOM"}:
|
||
for word in token.split():
|
||
word = word.strip(" .-'")
|
||
if len(word) < 3:
|
||
continue
|
||
if word.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
# Ne garder que les mots qui ressemblent à des noms propres
|
||
if not word[0].isupper():
|
||
continue
|
||
found.extend(page.search_for(word))
|
||
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))
|
||
# Incrustation OGC en haut à droite
|
||
if ogc_label:
|
||
from PIL import ImageFont
|
||
font_size = int(14 * zoom)
|
||
try:
|
||
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", font_size)
|
||
except Exception:
|
||
font = ImageFont.load_default()
|
||
text = f"OGC: {ogc_label}"
|
||
bbox = draw.textbbox((0, 0), text, font=font)
|
||
tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
|
||
margin = int(10 * zoom)
|
||
x = img.width - tw - margin
|
||
y = margin
|
||
# Fond blanc + texte noir
|
||
draw.rectangle([x - 4, y - 2, x + tw + 4, y + th + 2], fill=(255, 255, 255))
|
||
draw.text((x, y), text, fill=(0, 0, 0), font=font)
|
||
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,
|
||
ogc_label: Optional[str] = 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)
|
||
|
||
# 3b) Nettoyage post-masquage : codes postaux orphelins (5 chiffres collés à un placeholder)
|
||
# et téléphones fragmentés sur plusieurs lignes
|
||
_re_cp_orphan = re.compile(r"(\[(?:ADRESSE|NOM|VILLE)\])\s*(\d{5})\b")
|
||
def _clean_cp_orphan(m):
|
||
anon.audit.append(PiiHit(-1, "CODE_POSTAL", m.group(2), PLACEHOLDERS["CODE_POSTAL"]))
|
||
return m.group(1) + PLACEHOLDERS["CODE_POSTAL"]
|
||
final_text = _re_cp_orphan.sub(_clean_cp_orphan, final_text)
|
||
|
||
# Téléphones fragmentés : "0X XX XX XX\nXX" coupé en fin de ligne (ligne suivante immédiate)
|
||
_re_tel_frag = re.compile(r"((?:\+33\s?|0)\d(?:[ .-]?\d){6,7})\s*\n\s*(\d{2}(?!\d))")
|
||
def _clean_tel_frag(m):
|
||
full = m.group(1).replace(" ", "").replace(".", "").replace("-", "") + m.group(2)
|
||
if len(full.replace("+33", "0")) == 10:
|
||
anon.audit.append(PiiHit(-1, "TEL", m.group(0).strip(), PLACEHOLDERS["TEL"]))
|
||
return PLACEHOLDERS["TEL"] + "\n"
|
||
return m.group(0)
|
||
final_text = _re_tel_frag.sub(_clean_tel_frag, final_text)
|
||
|
||
# Téléphones incomplets en fin de ligne (8 ou 9 chiffres au format 0X XX XX XX) : masquer la partie visible
|
||
_re_tel_partial = re.compile(r"(?<!\d)((?:\+33\s?|0)\d(?:[ .-]?\d){5,7})(?!\d)\s*$", re.MULTILINE)
|
||
def _clean_tel_partial(m):
|
||
digits = re.sub(r"[ .\-]", "", m.group(1))
|
||
if 8 <= len(digits) <= 9:
|
||
anon.audit.append(PiiHit(-1, "TEL", m.group(0).strip(), PLACEHOLDERS["TEL"]))
|
||
return PLACEHOLDERS["TEL"]
|
||
return m.group(0)
|
||
final_text = _re_tel_partial.sub(_clean_tel_partial, final_text)
|
||
|
||
# 4) Consolidation : propager les PII détectés sur toutes les pages (page=-1)
|
||
# pour que la redaction PDF les cherche partout (sidebar répété, etc.)
|
||
|
||
# 4a) Noms : extraire les tokens individuels
|
||
_nom_kinds = {"NOM", "NOM_EXTRACTED", "NER_PER", "EDS_NOM"}
|
||
_global_name_tokens: set = set()
|
||
for h in anon.audit:
|
||
if h.kind not in _nom_kinds:
|
||
continue
|
||
for word in h.original.split():
|
||
word = word.strip(" .-'")
|
||
if len(word) < 3:
|
||
continue
|
||
if word.lower() in _MEDICAL_STOP_WORDS_SET:
|
||
continue
|
||
if not word[0].isupper():
|
||
continue
|
||
_global_name_tokens.add(word)
|
||
# 4a-bis) Noms compagnons : si un token connu est suivi/précédé d'un mot majuscule inconnu
|
||
# dans le texte brut, c'est aussi un nom (ex: "Diego OLIVER" → OLIVER est un nom)
|
||
raw_full = "\n\n".join(pages_text)
|
||
_companion_tokens: set = set()
|
||
for token in _global_name_tokens:
|
||
# Token connu suivi d'un mot ALL-CAPS
|
||
for m in re.finditer(rf"\b{re.escape(token)}\s+([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{{3,}})\b", raw_full):
|
||
candidate = m.group(1)
|
||
if candidate.lower() not in _MEDICAL_STOP_WORDS_SET and candidate not in _global_name_tokens:
|
||
_companion_tokens.add(candidate)
|
||
# Mot ALL-CAPS suivi du token connu
|
||
for m in re.finditer(rf"\b([A-ZÉÈÀÙÂÊÎÔÛÄËÏÖÜÇ]{{3,}})\s+{re.escape(token)}\b", raw_full):
|
||
candidate = m.group(1)
|
||
if candidate.lower() not in _MEDICAL_STOP_WORDS_SET and candidate not in _global_name_tokens:
|
||
_companion_tokens.add(candidate)
|
||
_global_name_tokens.update(_companion_tokens)
|
||
|
||
# Retirer les sous-parties de noms composés (JEAN, PIERRE si JEAN-PIERRE existe)
|
||
_compound = {t for t in _global_name_tokens if "-" in t}
|
||
_parts_to_drop = set()
|
||
for comp in _compound:
|
||
for part in comp.split("-"):
|
||
part = part.strip()
|
||
if len(part) >= 2 and part in _global_name_tokens:
|
||
_parts_to_drop.add(part)
|
||
_global_name_tokens -= _parts_to_drop
|
||
|
||
for token in _global_name_tokens:
|
||
anon.audit.append(PiiHit(page=-1, kind="NOM_GLOBAL", original=token, placeholder=PLACEHOLDERS["NOM"]))
|
||
|
||
# 4b) TEL, EMAIL, ADRESSE, CODE_POSTAL : propager les valeurs uniques sur toutes les pages
|
||
_global_pii: Dict[str, set] = {}
|
||
for h in anon.audit:
|
||
if h.kind in {"TEL", "EMAIL", "ADRESSE", "CODE_POSTAL", "EPISODE", "RPPS", "VILLE", "ETAB"}:
|
||
_global_pii.setdefault(h.kind, set()).add(h.original.strip())
|
||
for kind, values in _global_pii.items():
|
||
placeholder = PLACEHOLDERS.get(kind, PLACEHOLDERS["MASK"])
|
||
for val in values:
|
||
anon.audit.append(PiiHit(page=-1, kind=f"{kind}_GLOBAL", original=val, placeholder=placeholder))
|
||
|
||
# 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, ogc_label=ogc_label)
|
||
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))
|