version à 86%
This commit is contained in:
oussi
10
.gitignore
vendored
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10
.gitignore
vendored
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__pycache__/
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*.pyc
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.venv/
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venv/
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data/config.json
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data/alerts.json
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*.log
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scanOgc/
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CLAUDE.md
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.claude/
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124
PLAN_EXTRACTION.md
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124
PLAN_EXTRACTION.md
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# Plan extraction OGC → Excel
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## Contexte
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Extraction des fiches OGC (Organismes de Gestion du Contrôle) scanées.
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Chaque PDF = 1 dossier patient, ~6 pages.
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## Modèles utilisés
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- **Vision** : `qwen3-vl:2b` via Ollama (identifie le type de page + extrait les champs)
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- **Texte** : `qwen3:4b` (optionnel, structuration secondaire si besoin)
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## Architecture
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```
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PDF → pdf2image (images par page)
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↓
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Pour chaque page :
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→ qwen3-vl:2b : identifier le type de page
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→ Si "Séjour d'hospitalisation complète" : SKIP
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→ Sinon : extraction structurée JSON via qwen3-vl:2b
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↓
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Assemblage des champs par dossier
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↓
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Sortie Excel (openpyxl / pandas)
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```
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## Types de pages
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| Titre détecté | Action |
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|---|---|
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| FICHE MEDICALE DE RECUEIL DU PRATICIEN CONSEIL | Extraire (page principale) |
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| FICHE MEDICALE DE CONCERTATION | Skip (vide ou manuscrit léger) |
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| Séjour d'hospitalisation complète | **SKIP** (gros manuscrit) |
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| Eléments de preuve tracés au dossier | Extraire |
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| FICHE ADMINISTRATIVE DE CONCERTATION 2/2 | Extraire |
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| FICHE ADMINISTRATIVE DE CONCERTATION 1/2 | Extraire |
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## Champs à extraire
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### En-tête (commun)
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- N° OGC, Etablissement, FINESS, Date début contrôle
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- N° Champ, Libellé champ de contrôle
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- Dossier manquant, Date début séjour, Date fin séjour
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### Page 1 — Données du séjour (Etablissement + Recodage)
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- Age (ans), Age (jours), Sexe
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- Poids d'entrée, Durée de séjour
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- Mode d'entrée, Provenance, Mode de sortie, Destination
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- Nb séances, Nb RUM, Nb j EXH, Type EXB, Nb j EXB
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### Page 1 — Données du RUM (Etablissement + Recodage)
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- N° RUM, Lits dédiés SP, UM, IGS II
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- Durée RUM (date_debut / date_fin)
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- Nature suppl., Nb suppl.
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### Page 1 — Codages (listes)
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- DP_etab_code, DP_etab_libelle
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- DR_etab_code, DR_etab_libelle
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- DAS_etab : liste de {code, rang, libelle}
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- Actes_etab : liste de {code, rang, libelle}
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- DP_recodage, DR_recodage
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- DAS_recodage : liste de {code, rang}
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- Actes_recodage : liste de {code, rang}
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### Page 1 — GHM/GHS + décision
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- GHM_etab, GHS_etab, GHM_recodage, GHS_recodage
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- Recodage_impactant_facturation (0/1)
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- GHS_injustifie (0/1)
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- SE_coche (1/2/3/4), ATU, FFM, FSD
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- Accord_Desaccord (accord/désaccord)
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- Nom_praticien_conseil
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### Page Eléments de preuve (tableau 17 lignes)
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Pour chaque type de document :
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- present (oui/non), photocopie (nombre), absent_date_1ere_demande, date_obtention
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- Date_elements_preuve
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- Medecin_controleur_signataire, Medecin_DIM_signataire
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### Page Fiche Administrative 2/2
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- GHS_initial, GHS_avant_concertation, GHS_final_apres_concertation
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- Decision_finale (maintien_avis_controleur / retour_groupage_DIM / autre_groupage)
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- Avis_DIM_final (accord/désaccord)
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- Date_concertation
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- Nom_medecin_responsable_controle, Nom_medecin_DIM
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### Page Fiche Administrative 1/2
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- Date_concertation_1_2
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- Argumentaire_medecin_controleur (texte long imprimé)
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## Structure Excel de sortie
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### Onglet 1 : "Données principales"
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1 ligne par OGC, colonnes = tous les champs scalaires
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### Onglet 2 : "Diagnostics"
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1 ligne par code diagnostique (DP/DR/DAS), avec colonne N°OGC
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### Onglet 3 : "Actes"
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1 ligne par acte, avec colonne N°OGC
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### Onglet 4 : "Eléments de preuve"
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1 ligne par type de document × N°OGC
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## Fichiers
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- `extraction.py` : script principal
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- `scanOgc/` : dossier des PDFs source
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- `output/` : dossier de sortie Excel
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## Pour relancer
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```bash
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cd "/Users/oussi/Documents/Documents - MacBook Pro de oussi/EttaSante/T2A/ScanOGC/testExtraction2"
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python3 extraction.py
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```
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## Dépendances
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```bash
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pip install pdf2image pillow pandas openpyxl requests
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# Ollama doit tourner : ollama serve
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# Modèle requis : qwen3-vl:2b
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```
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## Notes importantes
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- L'ordre des pages varie selon les fichiers → identification par titre, pas par numéro
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- Page manuscrite identifiée par le titre "Séjour d'hospitalisation complète"
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- qwen3-vl:2b : paramètre `think` désactivé pour JSON pur (plus rapide)
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- Timeout Ollama : 120s par page (pages denses)
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82
README.md
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82
README.md
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# Extraction OGC — Qwen3-VL 235B
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||||||
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Extraction automatique des fiches OGC (Organisme de Gestion du Contrôle) vers Excel.
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Modèle : **qwen3-vl:235b-cloud** (vision multimodal via Ollama).
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---
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## Structure du dossier
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```
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testExtraction2/
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├── extraction.py ← script principal
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├── README.md ← ce fichier
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├── PLAN_EXTRACTION.md ← documentation technique détaillée
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├── scanOgc/ ← 12 PDFs source (OGC 358 à 429)
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└── output/
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├── extraction_ogc.xlsx ← résultat (4 onglets)
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└── extraction_ogc_raw.json ← données brutes pour debug
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```
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---
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## Lancement
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```bash
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cd testExtraction2
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# Tous les fichiers
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python3 extraction.py
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# Un seul fichier (relance partielle — fusionne avec le cache)
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python3 extraction.py "358"
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```
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---
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## Ce que fait le script
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1. Convertit chaque page PDF en image (200 DPI)
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2. Envoie l'image à **qwen3-vl:235b-cloud** pour identifier le type de page
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3. Ignore la page "Séjour d'hospitalisation complète" (bloc manuscrit — phase 2)
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4. Pour chaque autre page, extrait les données en JSON via un prompt dédié
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5. Assemble tout en Excel (4 onglets)
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|
### Types de pages traités
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| Type | Action |
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|
|---|---|
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|
| FICHE_RECUEIL | Extrait (données séjour, codages, GHM/GHS) |
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| ELEMENTS_PREUVE | Extrait (tableau 17 documents) |
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| FICHE_ADMIN_2_2 | Extrait (GHS final, décision concertation) |
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| FICHE_ADMIN_1_2 | Extrait (argumentaire imprimé complet) |
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| SEJOUR_MANUSCRIT | **Ignoré** — commentaires manuscrits (phase 2) |
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| FICHE_CONCERTATION_VIDE | Ignoré — page vide |
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---
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## Sortie Excel (4 onglets)
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||||||
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**Données principales** — 1 ligne par OGC, tous les champs scalaires
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**Diagnostics** — 1 ligne par code (DP/DR/DAS), établissement + recodage
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**Actes** — 1 ligne par acte CCAM, établissement + recodage
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||||||
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**Eléments de preuve** — 1 ligne par type de document × OGC
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||||||
|
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||||||
|
---
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|
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||||||
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## Particularités techniques
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||||||
|
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||||||
|
- **Thinking mode** : qwen3-vl génère ~4000 tokens de réflexion interne avant la réponse. `num_predict=8192` est obligatoire pour avoir assez de budget tokens.
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- **Rate limit** : pause de 2s entre chaque requête + retry automatique sur 429 (attente 60s × tentative).
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- **Ordre des pages variable** : identification par titre, pas par numéro de page.
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||||||
|
- **Relance partielle** : `python3 extraction.py "XXX"` charge le cache JSON existant et ne réécrase que le fichier relancé.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Prérequis
|
||||||
|
|
||||||
|
```bash
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||||||
|
pip install pdf2image pillow pandas openpyxl requests
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||||||
|
# Ollama doit tourner (ollama serve)
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||||||
|
# Modèle requis : qwen3-vl:235b-cloud
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||||||
|
```
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1129
extraction.py
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1129
extraction.py
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File diff suppressed because it is too large
Load Diff
683
generate_report.py
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683
generate_report.py
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"""
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|
Génération du bilan d'extraction OGC — QWEN
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|
Usage : python3 generate_report.py
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|
"""
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|
||||||
|
import json
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||||||
|
import re
|
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|
import sys
|
||||||
|
from datetime import datetime
|
||||||
|
from pathlib import Path
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||||||
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|
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|
from reportlab.lib import colors
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from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
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from reportlab.lib.pagesizes import A4
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from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
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||||||
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from reportlab.lib.units import cm
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||||||
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from reportlab.platypus import (
|
||||||
|
HRFlowable, PageBreak, Paragraph, SimpleDocTemplate, Spacer, Table,
|
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TableStyle,
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||||||
|
)
|
||||||
|
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||||||
|
# ─── Config ───────────────────────────────────────────────────────────────────
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||||||
|
|
||||||
|
BASE = Path(__file__).parent
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||||||
|
OUTPUT = BASE / "output"
|
||||||
|
|
||||||
|
MODEL = "qwen3-vl:235b-cloud"
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||||||
|
LABEL = "QWEN"
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||||||
|
ACC = colors.HexColor("#1a5276")
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||||||
|
|
||||||
|
JSON_PATH = OUTPUT / "extraction_ogc_raw.json"
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||||||
|
CORRECTION_PATH = OUTPUT / "extraction_ogc_raw_Correction.json"
|
||||||
|
TIMING_PATH = OUTPUT / "timing_stats.json"
|
||||||
|
REPORT_PATH = OUTPUT / "bilan_extraction_qwen_ogc.pdf"
|
||||||
|
|
||||||
|
# ─── Styles ───────────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
def make_styles(acc):
|
||||||
|
base = getSampleStyleSheet()
|
||||||
|
return {
|
||||||
|
"title": ParagraphStyle("title", parent=base["Title"],
|
||||||
|
fontSize=22, textColor=colors.white, alignment=TA_LEFT),
|
||||||
|
"subtitle": ParagraphStyle("subtitle", parent=base["Normal"],
|
||||||
|
fontSize=10, textColor=colors.HexColor("#aaaaaa"), alignment=TA_LEFT),
|
||||||
|
"section": ParagraphStyle("section", parent=base["Heading2"],
|
||||||
|
fontSize=13, textColor=acc, spaceBefore=16, spaceAfter=6),
|
||||||
|
"body": ParagraphStyle("body", parent=base["Normal"], fontSize=9, leading=14),
|
||||||
|
"small": ParagraphStyle("small", parent=base["Normal"], fontSize=8,
|
||||||
|
textColor=colors.HexColor("#444444")),
|
||||||
|
"right": ParagraphStyle("right", parent=base["Normal"], fontSize=7,
|
||||||
|
textColor=colors.HexColor("#888888"), alignment=TA_RIGHT),
|
||||||
|
"kpi_num": ParagraphStyle("kpi_num", parent=base["Normal"], fontSize=36,
|
||||||
|
fontName="Helvetica-Bold", alignment=TA_CENTER),
|
||||||
|
"kpi_lbl": ParagraphStyle("kpi_lbl", parent=base["Normal"], fontSize=8,
|
||||||
|
textColor=colors.HexColor("#777777"), alignment=TA_CENTER),
|
||||||
|
"warn": ParagraphStyle("warn", parent=base["Normal"], fontSize=8,
|
||||||
|
textColor=colors.HexColor("#c0392b")),
|
||||||
|
"footnote": ParagraphStyle("footnote", parent=base["Normal"], fontSize=7,
|
||||||
|
textColor=colors.HexColor("#888888")),
|
||||||
|
"center": ParagraphStyle("center", parent=base["Normal"], fontSize=9, alignment=TA_CENTER),
|
||||||
|
"bold": ParagraphStyle("bold", parent=base["Normal"], fontSize=9,
|
||||||
|
fontName="Helvetica-Bold"),
|
||||||
|
"th": ParagraphStyle("th", parent=base["Normal"], fontSize=8,
|
||||||
|
textColor=colors.white, fontName="Helvetica-Bold"),
|
||||||
|
}
|
||||||
|
|
||||||
|
# ─── Utilitaires ──────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
def _fmt_s(s):
|
||||||
|
if s is None:
|
||||||
|
return "—"
|
||||||
|
s = int(s)
|
||||||
|
h, r = divmod(s, 3600)
|
||||||
|
m, sec = divmod(r, 60)
|
||||||
|
if h:
|
||||||
|
return f"{h}h{m:02d}m{sec:02d}s"
|
||||||
|
if m:
|
||||||
|
return f"{m}m{sec:02d}s"
|
||||||
|
return f"{sec}s"
|
||||||
|
|
||||||
|
|
||||||
|
def _prec_color(p: float):
|
||||||
|
if p >= 90:
|
||||||
|
return colors.HexColor("#27ae60")
|
||||||
|
if p >= 75:
|
||||||
|
return colors.HexColor("#e67e22")
|
||||||
|
return colors.HexColor("#e74c3c")
|
||||||
|
|
||||||
|
|
||||||
|
def _gravite_color(g: str):
|
||||||
|
return {
|
||||||
|
"Critique": colors.HexColor("#e74c3c"),
|
||||||
|
"Haute": colors.HexColor("#e67e22"),
|
||||||
|
"Moyenne": colors.HexColor("#f1c40f"),
|
||||||
|
"Faible": colors.HexColor("#27ae60"),
|
||||||
|
}.get(g, colors.black)
|
||||||
|
|
||||||
|
|
||||||
|
_TS = TableStyle
|
||||||
|
|
||||||
|
def _base_table_style(acc):
|
||||||
|
return [
|
||||||
|
("BACKGROUND", (0, 0), (-1, 0), acc),
|
||||||
|
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
|
||||||
|
("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
|
||||||
|
("FONTSIZE", (0, 0), (-1, -1), 8),
|
||||||
|
("ROWBACKGROUNDS",(0, 1), (-1, -1), [colors.HexColor("#f8f9fa"), colors.white]),
|
||||||
|
("GRID", (0, 0), (-1, -1), 0.3, colors.HexColor("#cccccc")),
|
||||||
|
("LEFTPADDING", (0, 0), (-1, -1), 6),
|
||||||
|
("RIGHTPADDING", (0, 0), (-1, -1), 6),
|
||||||
|
("TOPPADDING", (0, 0), (-1, -1), 4),
|
||||||
|
("BOTTOMPADDING", (0, 0), (-1, -1), 4),
|
||||||
|
]
|
||||||
|
|
||||||
|
# ─── Comparaison orig vs correction ───────────────────────────────────────────
|
||||||
|
|
||||||
|
def _flatten(d, prefix=""):
|
||||||
|
items = {}
|
||||||
|
if isinstance(d, dict):
|
||||||
|
for k, v in d.items():
|
||||||
|
key = f"{prefix}.{k}" if prefix else k
|
||||||
|
if isinstance(v, (dict, list)):
|
||||||
|
items.update(_flatten(v, key))
|
||||||
|
else:
|
||||||
|
items[key] = str(v).strip()
|
||||||
|
elif isinstance(d, list):
|
||||||
|
for i, v in enumerate(d):
|
||||||
|
key = f"{prefix}[{i}]"
|
||||||
|
if isinstance(v, (dict, list)):
|
||||||
|
items.update(_flatten(v, key))
|
||||||
|
else:
|
||||||
|
items[key] = str(v).strip()
|
||||||
|
return items
|
||||||
|
|
||||||
|
|
||||||
|
def _normalize_keys(flat: dict) -> dict:
|
||||||
|
"""Normalise les anciens noms de clés pour compatibilité avec les fichiers de correction antérieurs.
|
||||||
|
rang → niveau (renommage effectué en avril 2026).
|
||||||
|
"""
|
||||||
|
return {k.replace(".rang", ".niveau"): v for k, v in flat.items()}
|
||||||
|
|
||||||
|
|
||||||
|
def _get_cat(key: str, ptype: str = "") -> str:
|
||||||
|
k = key.lower()
|
||||||
|
if ptype == "ELEMENTS_PREUVE":
|
||||||
|
if any(x in k for x in ("medecin", "signataire", "date")):
|
||||||
|
return "Signataires / Dates"
|
||||||
|
return "Éléments de preuve"
|
||||||
|
if ptype in ("FICHE_ADMIN_2_2", "FICHE_ADMIN_1_2"):
|
||||||
|
if any(x in k for x in ("nom_medecin", "date_conc", "medecin")):
|
||||||
|
return "Signataires / Dates"
|
||||||
|
return "Concertation (2/2)"
|
||||||
|
if any(x in k for x in ("das_etab", "das_reco")):
|
||||||
|
return "DAS"
|
||||||
|
if any(x in k for x in ("sejour_etab", "sejour_reco")):
|
||||||
|
return "Données séjour"
|
||||||
|
if any(x in k for x in ("dp_etab", "dr_etab", "dp_reco", "dr_reco")):
|
||||||
|
return "DP / DR"
|
||||||
|
if any(x in k for x in ("rum_etab", "rum_reco")):
|
||||||
|
return "Données RUM"
|
||||||
|
if any(x in k for x in ("actes_etab", "actes_reco")):
|
||||||
|
return "Actes"
|
||||||
|
if any(x in k for x in ("ghm_", "ghs_")):
|
||||||
|
return "GHM / GHS"
|
||||||
|
if any(x in k for x in ("accord_desaccord", "se_coche", "atu", "ffm", "fsd")):
|
||||||
|
return "Accord / SE"
|
||||||
|
if any(x in k for x in ("date_debut", "date_fin", "nom_praticien")):
|
||||||
|
return "Signataires / Dates"
|
||||||
|
return "Métadonnées"
|
||||||
|
|
||||||
|
|
||||||
|
def compare_extractions(orig_list, corr_list):
|
||||||
|
orig_map = {r["fichier"]: r for r in orig_list}
|
||||||
|
corr_map = {r["fichier"]: r for r in corr_list}
|
||||||
|
|
||||||
|
total_g = correct_g = 0
|
||||||
|
per_dossier = []
|
||||||
|
per_cat = {}
|
||||||
|
per_type = {}
|
||||||
|
|
||||||
|
ep_counters = {k: {"occ": 0, "dossiers": set()} for k in [
|
||||||
|
"dr_confondu_das", "annee_mal_lue", "se_coche_halluc", "maintien_X",
|
||||||
|
"provenance_halluc", "acte_dans_das", "das_manquant", "das_code_wrong",
|
||||||
|
"json_non_parsable",
|
||||||
|
]}
|
||||||
|
|
||||||
|
for fichier in sorted(orig_map):
|
||||||
|
if fichier not in corr_map:
|
||||||
|
continue
|
||||||
|
o = orig_map[fichier]
|
||||||
|
c = corr_map[fichier]
|
||||||
|
|
||||||
|
o_pages = {(p["page"], p.get("type", "")): p for p in o.get("pages_traitees", [])}
|
||||||
|
c_pages = {(p["page"], p.get("type", "")): p for p in c.get("pages_traitees", [])}
|
||||||
|
structural_error = "raw_response" in json.dumps(o)
|
||||||
|
|
||||||
|
dos_total = dos_correct = 0
|
||||||
|
|
||||||
|
for page_key in sorted(set(o_pages) & set(c_pages)):
|
||||||
|
op = o_pages[page_key]
|
||||||
|
cp = c_pages[page_key]
|
||||||
|
ptype = op.get("type", "UNKNOWN")
|
||||||
|
od = op.get("data", {})
|
||||||
|
cd = cp.get("data", {})
|
||||||
|
|
||||||
|
if not isinstance(od, dict) or not isinstance(cd, dict):
|
||||||
|
continue
|
||||||
|
if "raw_response" in od or "raw_response" in cd:
|
||||||
|
ep_counters["json_non_parsable"]["occ"] += 1
|
||||||
|
ep_counters["json_non_parsable"]["dossiers"].add(fichier)
|
||||||
|
continue
|
||||||
|
|
||||||
|
o_flat = _normalize_keys(_flatten(od))
|
||||||
|
c_flat = _normalize_keys(_flatten(cd))
|
||||||
|
all_keys = set(o_flat) | set(c_flat)
|
||||||
|
|
||||||
|
for k in all_keys:
|
||||||
|
ov = o_flat.get(k, "")
|
||||||
|
cv = c_flat.get(k, "")
|
||||||
|
cat = _get_cat(k, ptype)
|
||||||
|
per_cat.setdefault(cat, {"total": 0, "correct": 0})
|
||||||
|
per_type.setdefault(ptype, {"total": 0, "correct": 0})
|
||||||
|
per_cat[cat]["total"] += 1
|
||||||
|
per_type[ptype]["total"] += 1
|
||||||
|
dos_total += 1
|
||||||
|
if ov == cv:
|
||||||
|
per_cat[cat]["correct"] += 1
|
||||||
|
per_type[ptype]["correct"] += 1
|
||||||
|
dos_correct += 1
|
||||||
|
|
||||||
|
if ptype == "FICHE_RECUEIL":
|
||||||
|
dr = (od.get("dr_etab") or {}).get("code", "")
|
||||||
|
cdr = (cd.get("dr_etab") or {}).get("code", "")
|
||||||
|
if dr and not cdr:
|
||||||
|
ep_counters["dr_confondu_das"]["occ"] += 1
|
||||||
|
ep_counters["dr_confondu_das"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
prov = str((od.get("sejour_etab") or {}).get("provenance", "")).strip()
|
||||||
|
cprov = str((cd.get("sejour_etab") or {}).get("provenance", "")).strip()
|
||||||
|
if prov and not cprov:
|
||||||
|
ep_counters["provenance_halluc"]["occ"] += 1
|
||||||
|
ep_counters["provenance_halluc"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
se = str(od.get("se_coche", "")).strip()
|
||||||
|
cse = str(cd.get("se_coche", "")).strip()
|
||||||
|
if se and not cse:
|
||||||
|
ep_counters["se_coche_halluc"]["occ"] += 1
|
||||||
|
ep_counters["se_coche_halluc"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
das = od.get("das_etab") or []
|
||||||
|
cdas = cd.get("das_etab") or []
|
||||||
|
dp = (od.get("dp_etab") or {}).get("code", "")
|
||||||
|
if dp and not [x for x in das if isinstance(x, dict) and x.get("code")]:
|
||||||
|
ep_counters["das_manquant"]["occ"] += 1
|
||||||
|
ep_counters["das_manquant"]["dossiers"].add(fichier)
|
||||||
|
for od2, cd2 in zip(das, cdas):
|
||||||
|
if isinstance(od2, dict) and isinstance(cd2, dict):
|
||||||
|
if od2.get("code") != cd2.get("code") and cd2.get("code"):
|
||||||
|
oc = od2.get("code", "")
|
||||||
|
if len(oc) >= 7 and oc[:4].isalpha():
|
||||||
|
ep_counters["acte_dans_das"]["occ"] += 1
|
||||||
|
ep_counters["acte_dans_das"]["dossiers"].add(fichier)
|
||||||
|
else:
|
||||||
|
ep_counters["das_code_wrong"]["occ"] += 1
|
||||||
|
ep_counters["das_code_wrong"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
if ptype == "FICHE_ADMIN_2_2":
|
||||||
|
m = str(od.get("maintien_avis_controleur", "")).strip()
|
||||||
|
cm_ = str(cd.get("maintien_avis_controleur", "")).strip().lower()
|
||||||
|
if m.upper() == "X" and cm_ == "oui":
|
||||||
|
ep_counters["maintien_X"]["occ"] += 1
|
||||||
|
ep_counters["maintien_X"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
for k in od:
|
||||||
|
if "date" in k.lower():
|
||||||
|
ov = str(od.get(k, "")).strip()
|
||||||
|
cv = str(cd.get(k, "")).strip()
|
||||||
|
if ov != cv:
|
||||||
|
oy = re.findall(r"1[6-9]", ov)
|
||||||
|
cy = re.findall(r"1[6-9]", cv)
|
||||||
|
if oy and cy and oy != cy:
|
||||||
|
ep_counters["annee_mal_lue"]["occ"] += 1
|
||||||
|
ep_counters["annee_mal_lue"]["dossiers"].add(fichier)
|
||||||
|
|
||||||
|
prec = round(dos_correct / dos_total * 100) if dos_total else 0
|
||||||
|
per_dossier.append({
|
||||||
|
"fichier": fichier.replace(".pdf", ""),
|
||||||
|
"total": dos_total, "correct": dos_correct,
|
||||||
|
"errors": dos_total - dos_correct,
|
||||||
|
"precision": prec, "structural_error": structural_error,
|
||||||
|
})
|
||||||
|
total_g += dos_total
|
||||||
|
correct_g += dos_correct
|
||||||
|
|
||||||
|
prec_g = round(correct_g / total_g * 100, 1) if total_g else 0
|
||||||
|
n_total = len(orig_list)
|
||||||
|
error_patterns = []
|
||||||
|
for desc, key, gravite in [
|
||||||
|
("DR confondu avec DAS", "dr_confondu_das", "Critique"),
|
||||||
|
("Année mal lue (ex : 2017 au lieu de 2018)", "annee_mal_lue", "Haute"),
|
||||||
|
("se_coche inventé ('1' ou '4' au lieu de vide)", "se_coche_halluc", "Haute"),
|
||||||
|
("maintien_avis = 'X' au lieu de 'oui'", "maintien_X", "Haute"),
|
||||||
|
("provenance inventé ('8' au lieu de vide)", "provenance_halluc", "Haute"),
|
||||||
|
("Code acte mis dans DAS", "acte_dans_das", "Haute"),
|
||||||
|
("DAS entier manquant", "das_manquant", "Critique"),
|
||||||
|
("DAS code mauvais", "das_code_wrong", "Critique"),
|
||||||
|
("JSON non parsable", "json_non_parsable", "Critique"),
|
||||||
|
]:
|
||||||
|
e = ep_counters[key]
|
||||||
|
if e["occ"] > 0:
|
||||||
|
error_patterns.append({
|
||||||
|
"desc": desc, "occ": e["occ"],
|
||||||
|
"dossiers": len(e["dossiers"]), "n_total": n_total,
|
||||||
|
"gravite": gravite,
|
||||||
|
})
|
||||||
|
|
||||||
|
return {
|
||||||
|
"total": total_g, "correct": correct_g,
|
||||||
|
"errors": total_g - correct_g, "precision": prec_g,
|
||||||
|
"per_dossier": per_dossier, "per_cat": per_cat,
|
||||||
|
"per_type": per_type, "error_patterns": error_patterns,
|
||||||
|
}
|
||||||
|
|
||||||
|
# ─── Sections PDF ─────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
def _section_header(story, S, acc, text):
|
||||||
|
story.append(Paragraph(text, S["section"]))
|
||||||
|
story.append(HRFlowable(width="100%", thickness=0.5, color=acc, spaceAfter=6))
|
||||||
|
|
||||||
|
|
||||||
|
def _build_header(story, S, acc, meta):
|
||||||
|
hdr = Table(
|
||||||
|
[[Paragraph(f"BILAN D'EXTRACTION —\nMODÈLE {LABEL}", S["title"]),
|
||||||
|
Paragraph(meta, S["subtitle"])]],
|
||||||
|
colWidths=[10*cm, 7*cm],
|
||||||
|
)
|
||||||
|
hdr.setStyle(_TS([
|
||||||
|
("BACKGROUND", (0, 0), (-1, -1), acc),
|
||||||
|
("LEFTPADDING", (0, 0), (-1, -1), 16),
|
||||||
|
("RIGHTPADDING", (0, 0), (-1, -1), 12),
|
||||||
|
("TOPPADDING", (0, 0), (-1, -1), 16),
|
||||||
|
("BOTTOMPADDING", (0, 0), (-1, -1), 16),
|
||||||
|
("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
|
||||||
|
]))
|
||||||
|
story.append(hdr)
|
||||||
|
story.append(Spacer(1, 0.5*cm))
|
||||||
|
|
||||||
|
|
||||||
|
def _build_kpis(story, S, acc, cmp):
|
||||||
|
GREEN = colors.HexColor("#27ae60")
|
||||||
|
RED = colors.HexColor("#e74c3c")
|
||||||
|
kpi_num_style = ParagraphStyle("kpi_num2", parent=S["kpi_num"], fontSize=28, leading=32)
|
||||||
|
|
||||||
|
def kpi_cell(num, lbl, color=colors.HexColor("#333333")):
|
||||||
|
return [Paragraph(f'<font color="{color.hexval()}">{num}</font>', kpi_num_style),
|
||||||
|
Paragraph(lbl, S["kpi_lbl"])]
|
||||||
|
|
||||||
|
cells = [
|
||||||
|
kpi_cell(str(len(cmp["per_dossier"])), "Dossiers analysés", acc),
|
||||||
|
kpi_cell(str(cmp["total"]), "Champs comparés", colors.HexColor("#333333")),
|
||||||
|
kpi_cell(str(cmp["correct"]), "Champs corrects", GREEN),
|
||||||
|
kpi_cell(str(cmp["errors"]), "Champs en erreur", RED),
|
||||||
|
kpi_cell(f"{cmp['precision']}%", "Précision globale",
|
||||||
|
GREEN if cmp["precision"] >= 85 else RED),
|
||||||
|
]
|
||||||
|
kpi_t = Table([[c[0] for c in cells], [c[1] for c in cells]], colWidths=[3.4*cm]*5)
|
||||||
|
kpi_t.setStyle(_TS([
|
||||||
|
("BACKGROUND", (0, 0), (-1, -1), colors.HexColor("#f8f9fa")),
|
||||||
|
("BOX", (0, 0), (-1, -1), 0.5, colors.HexColor("#dddddd")),
|
||||||
|
("INNERGRID", (0, 0), (-1, -1), 0.3, colors.HexColor("#eeeeee")),
|
||||||
|
("TOPPADDING", (0, 0), (-1, 0), 14),
|
||||||
|
("BOTTOMPADDING", (0, 0), (-1, 0), 6),
|
||||||
|
("TOPPADDING", (0, 1), (-1, 1), 4),
|
||||||
|
("BOTTOMPADDING", (0, 1), (-1, 1), 12),
|
||||||
|
("ALIGN", (0, 0), (-1, -1), "CENTER"),
|
||||||
|
("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
|
||||||
|
]))
|
||||||
|
story.append(kpi_t)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
|
||||||
|
def _build_per_dossier(story, S, acc, W, cmp):
|
||||||
|
header = ["N° OGC", "Champs\ntotaux", "Champs\ncorrects", "Erreurs",
|
||||||
|
"Précision", "Err. structurelle"]
|
||||||
|
rows = [header]
|
||||||
|
style_extra = []
|
||||||
|
|
||||||
|
for i, d in enumerate(cmp["per_dossier"], start=1):
|
||||||
|
prec = d["precision"]
|
||||||
|
rows.append([d["fichier"], str(d["total"]), str(d["correct"]),
|
||||||
|
str(d["errors"]), f"{prec}%",
|
||||||
|
"■ Oui" if d["structural_error"] else "—"])
|
||||||
|
pc = _prec_color(prec)
|
||||||
|
style_extra += [
|
||||||
|
("TEXTCOLOR", (4, i), (4, i), pc),
|
||||||
|
("FONTNAME", (4, i), (4, i), "Helvetica-Bold"),
|
||||||
|
("TEXTCOLOR", (3, i), (3, i),
|
||||||
|
colors.HexColor("#e74c3c") if d["errors"] > 0 else colors.HexColor("#27ae60")),
|
||||||
|
("FONTNAME", (3, i), (3, i), "Helvetica-Bold"),
|
||||||
|
("TEXTCOLOR", (2, i), (2, i), colors.HexColor("#27ae60")),
|
||||||
|
("FONTNAME", (2, i), (2, i), "Helvetica-Bold"),
|
||||||
|
]
|
||||||
|
if d["structural_error"]:
|
||||||
|
style_extra += [("TEXTCOLOR", (5, i), (5, i), colors.HexColor("#e74c3c")),
|
||||||
|
("FONTNAME", (5, i), (5, i), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
tot_prec = round(cmp["correct"] / cmp["total"] * 100, 1) if cmp["total"] else 0
|
||||||
|
n_struct = sum(1 for d in cmp["per_dossier"] if d["structural_error"])
|
||||||
|
rows.append(["TOTAL", str(cmp["total"]), str(cmp["correct"]),
|
||||||
|
str(cmp["errors"]), f"{tot_prec}%", f"{n_struct} dossier(s)"])
|
||||||
|
n = len(rows)
|
||||||
|
style_extra += [("BACKGROUND", (0, n-1), (-1, n-1), colors.HexColor("#eaf0fb")),
|
||||||
|
("FONTNAME", (0, n-1), (-1, n-1), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
col_w = [W*0.16, W*0.12, W*0.14, W*0.11, W*0.13, W*0.34]
|
||||||
|
t = Table([[Paragraph(str(c), S["th"] if i == 0 else S["small"]) for c in row]
|
||||||
|
for i, row in enumerate(rows)], colWidths=col_w)
|
||||||
|
t.setStyle(_TS(_base_table_style(acc) + style_extra))
|
||||||
|
story.append(t)
|
||||||
|
|
||||||
|
|
||||||
|
def _build_per_cat(story, S, acc, W, cmp):
|
||||||
|
cat_order = [
|
||||||
|
"DAS", "Données séjour", "DP / DR", "Données RUM",
|
||||||
|
"Signataires / Dates", "Métadonnées", "Concertation (2/2)",
|
||||||
|
"Éléments de preuve", "Accord / SE", "GHM / GHS", "Actes",
|
||||||
|
]
|
||||||
|
rows = [["Catégorie", "Champs\ntotaux", "Champs\ncorrects", "Erreurs", "Précision"]]
|
||||||
|
style_extra = []
|
||||||
|
for i, cat in enumerate(cat_order, start=1):
|
||||||
|
d = cmp["per_cat"].get(cat)
|
||||||
|
if not d:
|
||||||
|
continue
|
||||||
|
prec = round(d["correct"] / d["total"] * 100) if d["total"] else 0
|
||||||
|
rows.append([cat, str(d["total"]), str(d["correct"]),
|
||||||
|
str(d["total"] - d["correct"]), f"{prec}%"])
|
||||||
|
style_extra += [("TEXTCOLOR", (4, i), (4, i), _prec_color(prec)),
|
||||||
|
("FONTNAME", (4, i), (4, i), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
col_w = [W*0.40, W*0.15, W*0.15, W*0.15, W*0.15]
|
||||||
|
t = Table([[Paragraph(str(c), S["th"] if i == 0 else S["small"]) for c in row]
|
||||||
|
for i, row in enumerate(rows)], colWidths=col_w)
|
||||||
|
t.setStyle(_TS(_base_table_style(acc) + style_extra))
|
||||||
|
story.append(t)
|
||||||
|
|
||||||
|
|
||||||
|
def _build_per_type(story, S, acc, W, cmp):
|
||||||
|
rows = [["Type de page", "Champs\ntotaux", "Champs\ncorrects", "Erreurs", "Précision"]]
|
||||||
|
style_extra = []
|
||||||
|
for i, (ptype, d) in enumerate(sorted(cmp["per_type"].items(),
|
||||||
|
key=lambda x: -x[1]["total"]), start=1):
|
||||||
|
prec = round(d["correct"] / d["total"] * 100) if d["total"] else 0
|
||||||
|
label = (ptype.replace("FICHE_", "Fiche ").replace("_RECUEIL", "de recueil")
|
||||||
|
.replace("_ADMIN_", " administrative ").replace("_2_2", "2/2")
|
||||||
|
.replace("_1_2", "1/2").replace("ELEMENTS_PREUVE", "Éléments de preuve"))
|
||||||
|
rows.append([label, str(d["total"]), str(d["correct"]),
|
||||||
|
str(d["total"] - d["correct"]), f"{prec}%"])
|
||||||
|
style_extra += [("TEXTCOLOR", (4, i), (4, i), _prec_color(prec)),
|
||||||
|
("FONTNAME", (4, i), (4, i), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
col_w = [W*0.40, W*0.15, W*0.15, W*0.15, W*0.15]
|
||||||
|
t = Table([[Paragraph(str(c), S["th"] if i == 0 else S["small"]) for c in row]
|
||||||
|
for i, row in enumerate(rows)], colWidths=col_w)
|
||||||
|
t.setStyle(_TS(_base_table_style(acc) + style_extra))
|
||||||
|
story.append(t)
|
||||||
|
|
||||||
|
|
||||||
|
def _build_error_patterns(story, S, acc, W, cmp):
|
||||||
|
rows = [["Ce que le modèle a raté — catégorie d'erreur",
|
||||||
|
"Occur-\nrences", "Sur combien\nde dossiers", "Gravité"]]
|
||||||
|
style_extra = []
|
||||||
|
for i, p in enumerate(cmp["error_patterns"], start=1):
|
||||||
|
gc = _gravite_color(p["gravite"])
|
||||||
|
rows.append([p["desc"], str(p["occ"]),
|
||||||
|
f"{p['dossiers']} / {p['n_total']}", p["gravite"]])
|
||||||
|
style_extra += [("TEXTCOLOR", (3, i), (3, i), gc),
|
||||||
|
("FONTNAME", (3, i), (3, i), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
col_w = [W*0.56, W*0.10, W*0.17, W*0.17]
|
||||||
|
t = Table([[Paragraph(str(c), S["th"] if i == 0 else S["small"]) for c in row]
|
||||||
|
for i, row in enumerate(rows)], colWidths=col_w)
|
||||||
|
t.setStyle(_TS(_base_table_style(acc) + style_extra))
|
||||||
|
story.append(t)
|
||||||
|
|
||||||
|
|
||||||
|
def _build_timing(story, S, acc, W, timing_data):
|
||||||
|
if not timing_data:
|
||||||
|
story.append(Paragraph(
|
||||||
|
"Aucune donnée temporelle disponible. "
|
||||||
|
"Relancez l'extraction pour générer timing_stats.json.", S["small"]))
|
||||||
|
return
|
||||||
|
|
||||||
|
total_s = sum(t.get("duree_totale_s") or 0 for t in timing_data)
|
||||||
|
total_pages= sum(t.get("nb_pages_total") or 0 for t in timing_data)
|
||||||
|
total_err = sum(len(t.get("erreurs", [])) for t in timing_data)
|
||||||
|
total_429 = sum(len(t.get("blocages_429", [])) for t in timing_data)
|
||||||
|
total_wait = sum(b["attente_s"] for t in timing_data for b in t.get("blocages_429", []))
|
||||||
|
n_dos = len(timing_data)
|
||||||
|
|
||||||
|
story.append(Paragraph("Résumé global", S["bold"]))
|
||||||
|
story.append(Spacer(1, 0.2*cm))
|
||||||
|
|
||||||
|
kpi_rows = [
|
||||||
|
["Durée totale d'extraction", _fmt_s(total_s)],
|
||||||
|
["Durée moyenne / dossier", _fmt_s(total_s / n_dos) if n_dos else "—"],
|
||||||
|
["Durée moyenne / page", _fmt_s(total_s / total_pages) if total_pages else "—"],
|
||||||
|
["Pages traitées", str(total_pages)],
|
||||||
|
["Erreurs totales", str(total_err)],
|
||||||
|
["Blocages rate limit (429)", str(total_429)],
|
||||||
|
["Temps perdu en attentes 429", _fmt_s(total_wait)],
|
||||||
|
["Temps utile (hors 429)", _fmt_s(total_s - total_wait)],
|
||||||
|
]
|
||||||
|
style_kpi = _base_table_style(acc) + [
|
||||||
|
("ALIGN", (1, 0), (1, -1), "CENTER"),
|
||||||
|
("TEXTCOLOR", (1, 0), (1, -1), acc),
|
||||||
|
("FONTNAME", (1, 0), (1, -1), "Helvetica-Bold"),
|
||||||
|
]
|
||||||
|
t_kpi = Table([[Paragraph(k, S["small"]), Paragraph(v, S["small"])]
|
||||||
|
for k, v in kpi_rows], colWidths=[W*0.6, W*0.4])
|
||||||
|
t_kpi.setStyle(_TS(style_kpi))
|
||||||
|
story.append(t_kpi)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
story.append(Paragraph("Détail par dossier", S["bold"]))
|
||||||
|
story.append(Spacer(1, 0.2*cm))
|
||||||
|
|
||||||
|
header = ["Dossier", "Début", "Fin", "Durée", "Pages",
|
||||||
|
"Erreurs", "Blocages\n429", "Attente\n429"]
|
||||||
|
rows = [header]
|
||||||
|
style_dos = _base_table_style(acc)
|
||||||
|
|
||||||
|
for i, t in enumerate(timing_data, start=1):
|
||||||
|
debut = (t.get("debut") or "")[:16].replace("T", " ")
|
||||||
|
fin = (t.get("fin") or "")[:16].replace("T", " ")
|
||||||
|
n_err = len(t.get("erreurs", []))
|
||||||
|
n_b = len(t.get("blocages_429", []))
|
||||||
|
att = sum(b["attente_s"] for b in t.get("blocages_429", []))
|
||||||
|
rows.append([
|
||||||
|
t["fichier"].replace(".pdf", ""), debut, fin,
|
||||||
|
_fmt_s(t.get("duree_totale_s")), str(t.get("nb_pages_total", "—")),
|
||||||
|
str(n_err), str(n_b), _fmt_s(att) if att else "—",
|
||||||
|
])
|
||||||
|
if n_err > 0:
|
||||||
|
style_dos += [("TEXTCOLOR", (5, i), (5, i), colors.HexColor("#e74c3c")),
|
||||||
|
("FONTNAME", (5, i), (5, i), "Helvetica-Bold")]
|
||||||
|
if n_b > 0:
|
||||||
|
style_dos += [("TEXTCOLOR", (6, i), (6, i), colors.HexColor("#e67e22")),
|
||||||
|
("FONTNAME", (6, i), (6, i), "Helvetica-Bold")]
|
||||||
|
|
||||||
|
col_w = [W*0.18, W*0.14, W*0.14, W*0.10, W*0.08, W*0.09, W*0.10, W*0.17]
|
||||||
|
t_dos = Table([[Paragraph(str(c), S["th"] if i == 0 else S["small"]) for c in row]
|
||||||
|
for i, row in enumerate(rows)], colWidths=col_w)
|
||||||
|
t_dos.setStyle(_TS(style_dos))
|
||||||
|
story.append(t_dos)
|
||||||
|
|
||||||
|
has_issues = any(t.get("erreurs") or t.get("blocages_429") for t in timing_data)
|
||||||
|
if has_issues:
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
story.append(Paragraph("Erreurs et blocages détaillés", S["bold"]))
|
||||||
|
story.append(Spacer(1, 0.2*cm))
|
||||||
|
for t in timing_data:
|
||||||
|
if not t.get("erreurs") and not t.get("blocages_429"):
|
||||||
|
continue
|
||||||
|
story.append(Paragraph(t["fichier"].replace(".pdf", ""), S["bold"]))
|
||||||
|
for err in t.get("erreurs", []):
|
||||||
|
story.append(Paragraph(
|
||||||
|
f" ⚠ Page {err['page']} — {err['phase']} : {err['message'][:100]}",
|
||||||
|
S["warn"]))
|
||||||
|
for b in t.get("blocages_429", []):
|
||||||
|
story.append(Paragraph(
|
||||||
|
f" ⏳ Blocage 429 — tentative {b['tentative']}, "
|
||||||
|
f"attente {b['attente_s']}s à {str(b.get('ts',''))[:16].replace('T',' ')}",
|
||||||
|
ParagraphStyle("b429", parent=S["small"],
|
||||||
|
textColor=colors.HexColor("#e67e22"))))
|
||||||
|
story.append(Spacer(1, 0.1*cm))
|
||||||
|
|
||||||
|
|
||||||
|
# ─── Main builder ─────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
def build_pdf():
|
||||||
|
W = A4[0] - 4*cm
|
||||||
|
|
||||||
|
if not JSON_PATH.exists():
|
||||||
|
print(f"⚠ JSON introuvable : {JSON_PATH}")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
with open(JSON_PATH, encoding="utf-8") as f:
|
||||||
|
orig_data = json.load(f)
|
||||||
|
|
||||||
|
cmp = None
|
||||||
|
if CORRECTION_PATH.exists():
|
||||||
|
with open(CORRECTION_PATH, encoding="utf-8") as f:
|
||||||
|
corr_data = json.load(f)
|
||||||
|
cmp = compare_extractions(orig_data, corr_data)
|
||||||
|
|
||||||
|
timing_data = None
|
||||||
|
if TIMING_PATH.exists():
|
||||||
|
with open(TIMING_PATH, encoding="utf-8") as f:
|
||||||
|
timing_data = json.load(f)
|
||||||
|
|
||||||
|
S = make_styles(ACC)
|
||||||
|
story = []
|
||||||
|
|
||||||
|
if cmp:
|
||||||
|
etabl = finess = controle = ""
|
||||||
|
for r in orig_data:
|
||||||
|
for pt in r.get("pages_traitees", []):
|
||||||
|
d = pt.get("data", {})
|
||||||
|
if d.get("etablissement"):
|
||||||
|
etabl = d["etablissement"]
|
||||||
|
if d.get("finess"):
|
||||||
|
finess = d["finess"]
|
||||||
|
if d.get("date_debut_controle"):
|
||||||
|
controle = d["date_debut_controle"]
|
||||||
|
if etabl and finess and controle:
|
||||||
|
break
|
||||||
|
if etabl:
|
||||||
|
break
|
||||||
|
meta = (f"{etabl} · FINESS {finess}\n"
|
||||||
|
f"{len(orig_data)} dossiers OGC · Contrôle {controle} · "
|
||||||
|
f"{datetime.now().strftime('%B %Y').capitalize()}")
|
||||||
|
else:
|
||||||
|
meta = (f"{len(orig_data)} dossiers OGC\n"
|
||||||
|
f"Généré le {datetime.now().strftime('%d/%m/%Y à %H:%M')}")
|
||||||
|
|
||||||
|
_build_header(story, S, ACC, meta)
|
||||||
|
|
||||||
|
if cmp:
|
||||||
|
_section_header(story, S, ACC, "1. Indicateurs globaux")
|
||||||
|
_build_kpis(story, S, ACC, cmp)
|
||||||
|
|
||||||
|
_section_header(story, S, ACC, "2. Résultats par dossier OGC")
|
||||||
|
_build_per_dossier(story, S, ACC, W, cmp)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
_section_header(story, S, ACC, "3. Précision par catégorie de champ")
|
||||||
|
_build_per_cat(story, S, ACC, W, cmp)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
_section_header(story, S, ACC, "4. Précision par type de page")
|
||||||
|
_build_per_type(story, S, ACC, W, cmp)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
if cmp["error_patterns"]:
|
||||||
|
_section_header(story, S, ACC, "5. Patterns d'erreurs récurrents")
|
||||||
|
_build_error_patterns(story, S, ACC, W, cmp)
|
||||||
|
story.append(Spacer(1, 0.4*cm))
|
||||||
|
|
||||||
|
sec_timing = 6
|
||||||
|
else:
|
||||||
|
sec_timing = 1
|
||||||
|
|
||||||
|
story.append(PageBreak())
|
||||||
|
_section_header(story, S, ACC, f"{sec_timing}. Analyse temporelle")
|
||||||
|
_build_timing(story, S, ACC, W, timing_data)
|
||||||
|
|
||||||
|
story.append(Spacer(1, 0.5*cm))
|
||||||
|
note = (
|
||||||
|
"Rapport généré par comparaison automatique de extraction_ogc_raw.json "
|
||||||
|
"vs extraction_ogc_raw_Correction.json · "
|
||||||
|
f"Périmètre : {len(orig_data)} dossiers OGC · "
|
||||||
|
"Les pourcentages de précision sont calculés champ par champ."
|
||||||
|
if cmp else
|
||||||
|
f"Rapport généré automatiquement · {len(orig_data)} dossiers OGC · "
|
||||||
|
"Aucun fichier de correction disponible — métriques de précision non calculées."
|
||||||
|
)
|
||||||
|
story.append(HRFlowable(width="100%", thickness=0.3, color=colors.grey))
|
||||||
|
story.append(Paragraph(note, S["footnote"]))
|
||||||
|
|
||||||
|
doc = SimpleDocTemplate(
|
||||||
|
str(REPORT_PATH), pagesize=A4,
|
||||||
|
leftMargin=2*cm, rightMargin=2*cm,
|
||||||
|
topMargin=2*cm, bottomMargin=2*cm,
|
||||||
|
title=f"Bilan extraction OGC — {LABEL}",
|
||||||
|
author="EttaSanté / T2A",
|
||||||
|
)
|
||||||
|
doc.build(story)
|
||||||
|
print(f"✓ {REPORT_PATH}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print(f"Génération bilan {LABEL}...")
|
||||||
|
build_pdf()
|
||||||
137
output/bilan_extraction_qwen_ogc (2).pdf
Normal file
137
output/bilan_extraction_qwen_ogc (2).pdf
Normal file
@@ -0,0 +1,137 @@
|
|||||||
|
%PDF-1.4
|
||||||
|
%“Œ‹ž ReportLab Generated PDF document http://www.reportlab.com
|
||||||
|
1 0 obj
|
||||||
|
<<
|
||||||
|
/F1 2 0 R /F2 3 0 R /F3 4 0 R
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
2 0 obj
|
||||||
|
<<
|
||||||
|
/BaseFont /Helvetica /Encoding /WinAnsiEncoding /Name /F1 /Subtype /Type1 /Type /Font
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
3 0 obj
|
||||||
|
<<
|
||||||
|
/BaseFont /Helvetica-Bold /Encoding /WinAnsiEncoding /Name /F2 /Subtype /Type1 /Type /Font
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
4 0 obj
|
||||||
|
<<
|
||||||
|
/BaseFont /ZapfDingbats /Name /F3 /Subtype /Type1 /Type /Font
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
5 0 obj
|
||||||
|
<<
|
||||||
|
/Contents 12 0 R /MediaBox [ 0 0 595.2756 841.8898 ] /Parent 11 0 R /Resources <<
|
||||||
|
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||||
|
>> /Rotate 0 /Trans <<
|
||||||
|
|
||||||
|
>>
|
||||||
|
/Type /Page
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
6 0 obj
|
||||||
|
<<
|
||||||
|
/Contents 13 0 R /MediaBox [ 0 0 595.2756 841.8898 ] /Parent 11 0 R /Resources <<
|
||||||
|
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||||
|
>> /Rotate 0 /Trans <<
|
||||||
|
|
||||||
|
>>
|
||||||
|
/Type /Page
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
7 0 obj
|
||||||
|
<<
|
||||||
|
/Contents 14 0 R /MediaBox [ 0 0 595.2756 841.8898 ] /Parent 11 0 R /Resources <<
|
||||||
|
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||||
|
>> /Rotate 0 /Trans <<
|
||||||
|
|
||||||
|
>>
|
||||||
|
/Type /Page
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
8 0 obj
|
||||||
|
<<
|
||||||
|
/Contents 15 0 R /MediaBox [ 0 0 595.2756 841.8898 ] /Parent 11 0 R /Resources <<
|
||||||
|
/Font 1 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ]
|
||||||
|
>> /Rotate 0 /Trans <<
|
||||||
|
|
||||||
|
>>
|
||||||
|
/Type /Page
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
9 0 obj
|
||||||
|
<<
|
||||||
|
/PageMode /UseNone /Pages 11 0 R /Type /Catalog
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
10 0 obj
|
||||||
|
<<
|
||||||
|
/Author (EttaSant\351 / T2A) /CreationDate (D:20260420204759+02'00') /Creator (\(unspecified\)) /Keywords () /ModDate (D:20260420204759+02'00') /Producer (ReportLab PDF Library - www.reportlab.com)
|
||||||
|
/Subject (\(unspecified\)) /Title (Bilan extraction OGC \204 QWEN) /Trapped /False
|
||||||
|
>>
|
||||||
|
endobj
|
||||||
|
11 0 obj
|
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]
|
||||||
Reference in New Issue
Block a user