Sécurité VLM : format JSON forcé, modèle local uniquement, fix logging critique

- vlm_manager: ajout format:json dans payload Ollama (élimine hallucinations JSON)
- vlm_manager: retour modèle local qwen2.5vl:7b (sécurité données médicales)
- anonymizer_core: ajout import logging (fix NameError silencieux qui tuait le VLM)
- anonymizer_core: masquage direct pages manuscrites (suppression rotation inutile)
- GUI: intégration checkbox VLM + auto-load EDS-Pseudo prioritaire

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

anonymizer_core_refactored_onnx.py

@@ -14,9 +14,12 @@ Dépendances : pdfplumber, pdfminer.six, pillow, pymupdf, pyyaml (optionnel), tr
 from __future__ import annotations
 import io
 import json
+import logging
 import os
 import re
 from concurrent.futures import ProcessPoolExecutor
+
+log = logging.getLogger(__name__)
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import List, Dict, Tuple, Optional, Any
@@ -1687,23 +1690,21 @@ def _apply_vlm_on_scanned_pdf(pdf_path: Path, anon: AnonResult, ocr_word_map: Oc
         # Détection page manuscrite : peu de mots OCR = scan manuscrit / mal orienté
         ocr_count = len(ocr_word_map.get(pno, []))
         is_handwritten_page = ocr_count < 100
-        rotations_to_try = [0]
-        if is_handwritten_page:
-            rotations_to_try = [0, 270, 90, 180]
 
+        # Pages manuscrites : masquage total direct (VLM trop lent/hallucinatoire)
+        if is_handwritten_page and ocr_count > 0:
+            anon.audit.append(PiiHit(page=pno, kind="FULL_PAGE_MASK", original="page manuscrite",
+                                      placeholder=PLACEHOLDERS["MASK"]))
+            log.info("VLM page %d : masquage total direct (OCR=%d mots)", pno, ocr_count)
+            continue
+
+        # Pages lisibles : analyse VLM
         best_entities = []
-        for rot in rotations_to_try:
-            img_rot = img.rotate(rot, expand=True) if rot else img
-            try:
-                ents = vlm_manager.analyze_page_image(img_rot, page_number=pno,
-                                                       existing_pii=existing_pii[:20])
-            except Exception:
-                ents = []
-            if len(ents) > len(best_entities):
-                best_entities = ents
-            # Si on a trouvé assez d'entités, pas besoin d'essayer d'autres rotations
-            if len(ents) >= 5:
-                break
+        try:
+            best_entities = vlm_manager.analyze_page_image(img, page_number=pno,
+                                                            existing_pii=existing_pii[:20])
+        except Exception:
+            best_entities = []
 
         for ent in best_entities:
             cat = ent.get("categorie", "").upper()
@@ -1737,16 +1738,6 @@ def _apply_vlm_on_scanned_pdf(pdf_path: Path, anon: AnonResult, ocr_word_map: Oc
             except re.error:
                 anon.text_out = anon.text_out.replace(texte, placeholder)
 
-        # Masquage total : page manuscrite avec PII confirmées OU VLM en échec
-        vlm_pii_count = sum(1 for e in best_entities
-                            if e.get("categorie", "").upper() in VLM_CATEGORY_MAP
-                            and e.get("confiance", 0) >= 0.3)
-        if is_handwritten_page and (vlm_pii_count >= 3 or (len(best_entities) == 0 and ocr_count > 0)):
-            anon.audit.append(PiiHit(page=pno, kind="FULL_PAGE_MASK", original="page manuscrite",
-                                      placeholder=PLACEHOLDERS["MASK"]))
-            log.info("VLM page %d : masquage total (OCR=%d mots, VLM=%d PII, handwritten=%s)",
-                     pno, ocr_count, vlm_pii_count, is_handwritten_page)
-
     doc.close()