diff --git a/debug_ocr/ocr_image_145435.png.txt b/debug_ocr/ocr_image_145435.png.txt new file mode 100644 index 0000000..402e620 --- /dev/null +++ b/debug_ocr/ocr_image_145435.png.txt @@ -0,0 +1,45 @@ += + +[e) + +“ + +giraudibrg-lsb.com/BRG-LAB/PAGE programmetssai/zE4AAHEVNGOAA + + BAGLAB C9 Béton C9 Fouméseu labo © Masse + +FAT :1 Essai au bleu de méthylène (MB) - NF EN 933-9 (02-2022) + +£E Victor + +Echantilion _n°25-00075 réceptionné le 02/04/2028 par BOLLÉE Victor - prélevé le 02/04/2025 por BOLLEE Victor, n° prélbvement : 2500078 + +Matériau Sable 0/2 C - CARRIERE ADCEG + +V HISTORIQUE + +72 + +[VU Essai [ve marée ][M For Uo |[v osservanons + +[ + +] + +NREGISTRER + +MPRMER + +le l'essai + +2025 + +(RE GIRAUD + +les statistiques + +lessai + +A + +impossible de trouver adresse IP du serveur de zk1.brg-lab.com. \ No newline at end of file diff --git a/utils/debug_ocr_preprocessed/document/optimized_image_145435.png b/debug_ocr/optimized_image_145435.png similarity index 100% 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"default": { + "resize_min_dim": 1000, # 📏 Si largeur/hauteur < 1000px, image agrandie proportionnellement + "enhance_contrast": True, # 🔆 Active l'amélioration du contraste + "contrast_factor": 1.2, # >1 = plus contrasté, typique : 1.2 à 1.8 + "enhance_sharpness": False, # 💥 Active la netteté + "sharpness_factor": 1.0, # >1 = plus net, typique : 1.2 à 2.0 + "apply_denoising": False, # 🚿 Réduction de bruit + "denoise_strength": { + "h": 0, # 0 à 15 : intensité du lissage luminance + "hColor": 0, # 0 à 15 : lissage chroma + "templateWindowSize": 7, # Taille du patch à comparer (typiquement 7) + "searchWindowSize": 21 # Zone autour du patch pour recherche (typiquement 21) + }, + "invert_colors": False, # ↕️ Inversion si texte clair sur fond sombre + "apply_clahe": False, # 📈 Égalisation du contraste local (utile en cas de zones très sombres/claires) + "save_debug_output": True, + "debug_output_dir": "debug_ocr_preprocessed" + }, + "aggressive": { + "resize_min_dim": 1400, + "enhance_contrast": True, + "contrast_factor": 1.8, + "enhance_sharpness": True, + "sharpness_factor": 1.5, + "apply_denoising": True, + "denoise_strength": { + "h": 10, + "hColor": 10, + "templateWindowSize": 7, + "searchWindowSize": 21 + }, + "invert_colors": False, + "apply_clahe": False, + "save_debug_output": True, + "debug_output_dir": "debug_ocr_preprocessed" + }, + "document": { + "resize_min_dim": 1100, + "enhance_contrast": True, + "contrast_factor": 1.2, + "enhance_sharpness": False, + "sharpness_factor": 1.0, + "apply_denoising": False, + "denoise_strength": {"h": 0, "hColor": 0, "templateWindowSize": 7, "searchWindowSize": 21}, + "invert_colors": False, + "apply_clahe": False, + "save_debug_output": True, + "debug_output_dir": "debug_ocr_preprocessed" + }, + "clahe_high": { + "resize_min_dim": 1200, + "enhance_contrast": True, + "contrast_factor": 1.4, + "enhance_sharpness": True, + "sharpness_factor": 1.3, + "apply_denoising": True, + "denoise_strength": { + "h": 7, + "hColor": 7, + "templateWindowSize": 7, # Taille du patch local utilisé + "searchWindowSize": 21 # Zone de recherche du filtre + }, + "invert_colors": False, + "apply_clahe": True, + "save_debug_output": True, + "debug_output_dir": "debug_ocr_preprocessed" + }, + "invert_light": { + "resize_min_dim": 1200, + "enhance_contrast": True, + "contrast_factor": 1.3, + "enhance_sharpness": True, + "sharpness_factor": 1.4, + "apply_denoising": False, + "invert_colors": True, + "apply_clahe": False, + "save_debug_output": True, + "debug_output_dir": "debug_ocr_preprocessed" + } +} + +def preprocess_image(image_path: str, **settings) -> Image.Image: + img = Image.open(image_path).convert("RGB") + base_name = os.path.basename(image_path) + + # Gestion des dossiers de debug + debug_dir = settings.get("debug_output_dir", "debug_ocr_preprocessed") + profile_name = settings.get("profile_name", "default") # Ajout du nom du profil + debug_profile_dir = os.path.join(debug_dir, profile_name) + os.makedirs(debug_profile_dir, exist_ok=True) + + # Redimensionnement + if settings.get("resize_min_dim", 0) > 0: + width, height = img.size + min_dim = min(width, height) + if min_dim < settings["resize_min_dim"]: + scale = settings["resize_min_dim"] / min_dim + new_size = (int(width * scale), int(height * scale)) + img = img.resize(new_size, Image.Resampling.BICUBIC) + + # Contraste + if settings.get("enhance_contrast", False): + enhancer = ImageEnhance.Contrast(img) + img = enhancer.enhance(settings.get("contrast_factor", 1.5)) + + # Netteté + if settings.get("enhance_sharpness", False): + enhancer = ImageEnhance.Sharpness(img) + img = enhancer.enhance(settings.get("sharpness_factor", 1.5)) + + # Convert to OpenCV image + img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) + + # Débruitage + if settings.get("apply_denoising", False): + strength = settings.get("denoise_strength", {}) + img_cv = cv2.fastNlMeansDenoisingColored( + img_cv, + None, + h=strength.get("h", 10), + hColor=strength.get("hColor", 10), + templateWindowSize=strength.get("templateWindowSize", 7), + searchWindowSize=strength.get("searchWindowSize", 21) + ) + + # CLAHE + if settings.get("apply_clahe", False): + lab = cv2.cvtColor(img_cv, cv2.COLOR_BGR2LAB) + l, a, b = cv2.split(lab) + clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + cl = clahe.apply(l) + img_cv = cv2.merge((cl, a, b)) + img_cv = cv2.cvtColor(img_cv, cv2.COLOR_LAB2BGR) + + # Inversion + if settings.get("invert_colors", False): + img_cv = cv2.bitwise_not(img_cv) + + # Sauvegarde image prétraitée (debug) + if settings.get("save_debug_output", False): + debug_path = os.path.join(debug_profile_dir, f"optimized_{base_name}") + cv2.imwrite(debug_path, img_cv) + + return Image.fromarray(cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB)) + +def preprocess_image_with_profile(image_path: str, profile_name="default") -> Image.Image: + settings = PREPROCESSING_PROFILES[profile_name].copy() # On fait une copie pour ne pas modifier l'original + settings["profile_name"] = profile_name # On ajoute le nom du profil aux paramètres + return preprocess_image(image_path, **settings) diff --git a/utils/ocr_test_results/image_129042/aggressive/image.png b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_129042/aggressive/image.png similarity index 100% rename from utils/ocr_test_results/image_129042/aggressive/image.png rename to utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_129042/aggressive/image.png diff --git a/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_129042/aggressive/image.png:Zone.Identifier b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_129042/aggressive/image.png:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/utils/ocr_test_results/image_129042/aggressive/optimized_aggressive.png b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_129042/aggressive/optimized_aggressive.png 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b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_145453/invert_light/psm6_oem1.txt:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/utils/ocr_test_results/image_145453/invert_light/psm6_oem3.txt b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_145453/invert_light/psm6_oem3.txt similarity index 100% rename from utils/ocr_test_results/image_145453/invert_light/psm6_oem3.txt rename to utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_145453/invert_light/psm6_oem3.txt diff --git a/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_145453/invert_light/psm6_oem3.txt:Zone.Identifier b/utils/ocr_brut_test/ocr_test_results/old_tests/resultatstests/image_145453/invert_light/psm6_oem3.txt:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/utils/ocr_brut_test/ocr_utils.py b/utils/ocr_brut_test/ocr_utils.py new file mode 100644 index 0000000..f02f574 --- /dev/null +++ b/utils/ocr_brut_test/ocr_utils.py @@ -0,0 +1,117 @@ +import pytesseract +import cv2 +import numpy as np +import os +from pathlib import Path +from PIL import Image +from langdetect import detect +import re +from ocr_preprocessor import preprocess_image + +# ⬇️ PARAMÈTRES CENTRAUX D'ACTIVATION ⬇️ +USE_PREPROCESSING = True # Active le prétraitement de l'image +USE_TEXT_CORRECTION = True # Corrige les mots tronqués après OCR +USE_IMAGE_RESIZE = False # Redimensionne l'image si trop petite +SAVE_DEBUG_OUTPUT = False # Sauvegarde image + texte dans debug_ocr/ +AUTO_DETECT_LANGUAGE = True # Détecte automatiquement la langue + +# Complétion de mots tronqués (rudimentaire mais utile) +def completer_mots_tronques(texte): + lignes = texte.splitlines() + lignes_corrigees = [] + for ligne in lignes: + if ligne.strip().endswith("-"): + ligne = ligne.strip()[:-1] + lignes_corrigees.append(ligne) + return "\n".join(lignes_corrigees) + +# Prétraitement de l'image +def pretraiter_image(image_path, optimize_for_text=True): + image = cv2.imread(image_path) + if image is None: + return None + + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + if optimize_for_text: + gray = cv2.GaussianBlur(gray, (3, 3), 0) + gray = cv2.adaptiveThreshold(gray, 255, + cv2.ADAPTIVE_THRESH_GAUSSIAN_C, + cv2.THRESH_BINARY, 31, 15) + else: + gray = cv2.equalizeHist(gray) + + if USE_IMAGE_RESIZE: + height, width = gray.shape + if width < 1000 or height < 1000: + ratio = max(1000 / width, 1000 / height) + gray = cv2.resize(gray, (0, 0), fx=ratio, fy=ratio) + + return gray + +# Détection de langue automatique (si activée) +def detect_language_tesseract(image_cv): + try: + text_sample = pytesseract.image_to_string(image_cv, config="--psm 6") + lang = detect(text_sample) + return { + "fr": "fra", + "en": "eng" + }.get(lang, "fra+eng") + except: + return "fra+eng" + +# OCR principal +def extraire_texte(image_path, lang="auto"): + image = cv2.imread(image_path) + if image is None: + print(f"[OCR] Image non trouvée: {image_path}") + return "", None + + img_standard = preprocess_image(image_path) if USE_PREPROCESSING else Image.open(image_path) + img_optimized = img_standard # on utilise le même traitement pour les deux dans ce cas + + + ocr_lang = lang + if lang == "auto" and AUTO_DETECT_LANGUAGE: + ocr_lang = detect_language_tesseract(img_standard) + if ocr_lang == "auto": + ocr_lang = "fra+eng" + + config = f"--psm 6 -l {ocr_lang}" + texte = pytesseract.image_to_string(img_optimized, config=config) + + if USE_TEXT_CORRECTION: + texte_corrige = completer_mots_tronques(texte) + if len(texte_corrige) >= len(texte) * 0.9: + texte = texte_corrige + + if SAVE_DEBUG_OUTPUT and texte: + try: + debug_dir = "debug_ocr" + os.makedirs(debug_dir, exist_ok=True) + image_name = Path(image_path).stem + + # Conversion si image PIL + if isinstance(img_optimized, Image.Image): + img_optimized = np.array(img_optimized) + if img_optimized.ndim == 3 and img_optimized.shape[2] == 3: + img_optimized = cv2.cvtColor(img_optimized, cv2.COLOR_RGB2BGR) + elif img_optimized.ndim == 3 and img_optimized.shape[2] == 4: + img_optimized = cv2.cvtColor(img_optimized, cv2.COLOR_RGBA2BGR) + + if isinstance(img_optimized, np.ndarray): + cv2.imwrite(f"{debug_dir}/optimized_{image_name}.png", img_optimized) + + with open(f"{debug_dir}/ocr_{image_name}.png.txt", "w", encoding="utf-8") as f: + f.write(texte) + + except Exception as e: + print(f"[OCR DEBUG] Erreur de sauvegarde debug: {e}") + + return texte, img_optimized + +# Raccourci rapide pour juste récupérer le texte en français +def extraire_texte_fr(image_path): + texte, _ = extraire_texte(image_path, lang="fra") + return texte diff --git a/utils/test_ocr_utils.py b/utils/ocr_brut_test/test_ocr_utils.py similarity index 100% rename from utils/test_ocr_utils.py rename to utils/ocr_brut_test/test_ocr_utils.py diff --git a/utils/ocr_test_results/resultatstests.zip b/utils/ocr_test_results/resultatstests.zip deleted file mode 100644 index f491cd0..0000000 Binary files a/utils/ocr_test_results/resultatstests.zip and /dev/null differ diff --git a/utils/ocr_utils.py b/utils/ocr_utils.py index 8932325..ec3f301 100644 --- a/utils/ocr_utils.py +++ b/utils/ocr_utils.py @@ -6,15 +6,19 @@ from pathlib import Path from PIL import Image from langdetect import detect import re -from ocr_preprocessor import preprocess_image +from utils.ocr_preprocessor import preprocess_image_with_profile # ⬇️ PARAMÈTRES CENTRAUX D'ACTIVATION ⬇️ -USE_PREPROCESSING = False # Active le prétraitement de l'image +USE_PREPROCESSING = True # Active le prétraitement de l'image via ocr_preprocessor.py USE_TEXT_CORRECTION = True # Corrige les mots tronqués après OCR -USE_IMAGE_RESIZE = False # Redimensionne l'image si trop petite -SAVE_DEBUG_OUTPUT = False # Sauvegarde image + texte dans debug_ocr/ +SAVE_DEBUG_OUTPUT = True # Sauvegarde image + texte dans debug_ocr/ AUTO_DETECT_LANGUAGE = True # Détecte automatiquement la langue +# Paramètres Tesseract optimaux selon tests +OCR_DEFAULT_PSM = 11 # Page segmentation mode 11 (sparse text) +OCR_DEFAULT_OEM = 3 # OCR Engine mode 3 (default, based on what is available) +OCR_DEFAULT_PROFILE = "document" # Profil de prétraitement optimal selon tests + # Complétion de mots tronqués (rudimentaire mais utile) def completer_mots_tronques(texte): lignes = texte.splitlines() @@ -25,30 +29,6 @@ def completer_mots_tronques(texte): lignes_corrigees.append(ligne) return "\n".join(lignes_corrigees) -# Prétraitement de l'image -def pretraiter_image(image_path, optimize_for_text=True): - image = cv2.imread(image_path) - if image is None: - return None - - gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - - if optimize_for_text: - gray = cv2.GaussianBlur(gray, (3, 3), 0) - gray = cv2.adaptiveThreshold(gray, 255, - cv2.ADAPTIVE_THRESH_GAUSSIAN_C, - cv2.THRESH_BINARY, 31, 15) - else: - gray = cv2.equalizeHist(gray) - - if USE_IMAGE_RESIZE: - height, width = gray.shape - if width < 1000 or height < 1000: - ratio = max(1000 / width, 1000 / height) - gray = cv2.resize(gray, (0, 0), fx=ratio, fy=ratio) - - return gray - # Détection de langue automatique (si activée) def detect_language_tesseract(image_cv): try: @@ -63,29 +43,35 @@ def detect_language_tesseract(image_cv): # OCR principal def extraire_texte(image_path, lang="auto"): - image = cv2.imread(image_path) - if image is None: + # Vérification de l'existence de l'image + if not os.path.exists(image_path): print(f"[OCR] Image non trouvée: {image_path}") return "", None - img_standard = preprocess_image(image_path) if USE_PREPROCESSING else Image.open(image_path) - img_optimized = img_standard # on utilise le même traitement pour les deux dans ce cas - - + # Prétraitement de l'image avec le profil optimal + if USE_PREPROCESSING: + img_optimized = preprocess_image_with_profile(image_path, profile_name=OCR_DEFAULT_PROFILE) + else: + img_optimized = Image.open(image_path) + + # Détection de langue ocr_lang = lang if lang == "auto" and AUTO_DETECT_LANGUAGE: - ocr_lang = detect_language_tesseract(img_standard) + ocr_lang = detect_language_tesseract(img_optimized) if ocr_lang == "auto": ocr_lang = "fra+eng" - config = f"--psm 6 -l {ocr_lang}" + # OCR avec paramètres optimaux + config = f"--psm {OCR_DEFAULT_PSM} --oem {OCR_DEFAULT_OEM} -l {ocr_lang}" texte = pytesseract.image_to_string(img_optimized, config=config) + # Correction des mots tronqués if USE_TEXT_CORRECTION: texte_corrige = completer_mots_tronques(texte) if len(texte_corrige) >= len(texte) * 0.9: texte = texte_corrige + # Sauvegarde debug (si activée) if SAVE_DEBUG_OUTPUT and texte: try: debug_dir = "debug_ocr" diff --git a/utils/script_bis_ollama.py b/utils/script_bis_ollama.py deleted file mode 100644 index 63464f9..0000000 --- a/utils/script_bis_ollama.py +++ /dev/null @@ -1,138 +0,0 @@ -import requests -import json -import os -from datetime import datetime -from typing import Dict, Any, Optional - -# Configuration -OLLAMA_URL = "http://217.182.105.173:11434" -MODEL_NAME = "llama3.2-vision:90b-instruct-q8_0" - -def get_ollama_info() -> Optional[Dict[str, Any]]: - """ - Récupère toutes les informations disponibles sur le modèle depuis Ollama. - - Returns: - Dict contenant les informations ou None si erreur - """ - try: - response = requests.post( - f"{OLLAMA_URL}/api/show", - json={"name": MODEL_NAME}, - headers={"Content-Type": "application/json"} - ) - - if response.status_code == 200: - return response.json() - else: - print(f"❌ Erreur {response.status_code}: {response.text}") - return None - - except requests.exceptions.RequestException as e: - print(f"❌ Erreur de connexion: {str(e)}") - return None - -def save_raw_response(data: Dict[str, Any]) -> str: - """ - Sauvegarde la réponse brute dans un fichier JSON. - - Args: - data: Données à sauvegarder - - Returns: - Nom du fichier créé - """ - os.makedirs('settings', exist_ok=True) - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - filename = f"settings/ollama_raw_{timestamp}.json" - - with open(filename, 'w', encoding='utf-8') as f: - json.dump(data, f, indent=2, ensure_ascii=False) - - print(f"✅ Réponse brute sauvegardée dans: {filename}") - return filename - -def extract_model_parameters(data: Dict[str, Any]) -> Dict[str, Any]: - """ - Extrait et analyse les paramètres du modèle. - - Args: - data: Données brutes d'Ollama - - Returns: - Dict contenant les paramètres organisés - """ - model_info = { - "basic_info": { - "name": MODEL_NAME, - "family": data.get("details", {}).get("family", ""), - "parameter_size": data.get("details", {}).get("parameter_size", ""), - "quantization": data.get("details", {}).get("quantization_level", ""), - "modified_at": data.get("modified_at", "") - }, - "technical_info": { - "architecture": data.get("model_info", {}).get("general.architecture", ""), - "context_length": data.get("model_info", {}).get("mllama.context_length", 0), - "vocab_size": data.get("model_info", {}).get("mllama.vocab_size", 0), - "embedding_length": data.get("model_info", {}).get("mllama.embedding_length", 0) - }, - "vision_info": { - "image_size": data.get("projector_info", {}).get("mllama.vision.image_size", 0), - "patch_size": data.get("projector_info", {}).get("mllama.vision.patch_size", 0), - "max_num_tiles": data.get("projector_info", {}).get("mllama.vision.max_num_tiles", 0) - }, - "default_parameters": {} - } - - # Extraire les paramètres du modelfile - if "modelfile" in data: - for line in data["modelfile"].split('\n'): - if line.startswith('PARAMETER'): - _, key, value = line.split(None, 2) - try: - model_info["default_parameters"][key] = float(value) - except ValueError: - model_info["default_parameters"][key] = value.strip() - - # Ajouter les paramètres de la section parameters - if "parameters" in data: - for line in data["parameters"].split('\n'): - if line.strip(): - key, value = line.split(None, 1) - try: - model_info["default_parameters"][key] = float(value) - except ValueError: - model_info["default_parameters"][key] = value.strip() - - return model_info - -def main(): - # Récupérer les informations d'Ollama - print(f"🔄 Récupération des informations pour le modèle {MODEL_NAME}...") - ollama_data = get_ollama_info() - - if ollama_data: - # Sauvegarder la réponse brute - raw_file = save_raw_response(ollama_data) - - # Extraire et organiser les informations - model_info = extract_model_parameters(ollama_data) - - # Sauvegarder les informations organisées - info_file = raw_file.replace('raw', 'info') - with open(info_file, 'w', encoding='utf-8') as f: - json.dump(model_info, f, indent=2, ensure_ascii=False) - print(f"✅ Informations organisées sauvegardées dans: {info_file}") - - # Afficher un résumé - print("\n📊 Résumé des informations :") - print(f"• Modèle: {model_info['basic_info']['name']}") - print(f"• Famille: {model_info['basic_info']['family']}") - print(f"• Taille: {model_info['basic_info']['parameter_size']}") - print(f"• Quantization: {model_info['basic_info']['quantization']}") - print("\n🔧 Paramètres par défaut:") - for key, value in model_info['default_parameters'].items(): - print(f"• {key}: {value}") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/utils/script_default_ollama.py b/utils/script_default_ollama.py deleted file mode 100644 index b02dbf8..0000000 --- a/utils/script_default_ollama.py +++ /dev/null @@ -1,151 +0,0 @@ -import requests -import json -import os -from datetime import datetime - -# Configuration -MODEL_NAME = "llama3.2-vision:90b-instruct-q8_0" -OLLAMA_URL = "http://217.182.105.173:11434" - -def get_model_settings(): - # Endpoint Ollama pour obtenir les informations du modèle - model_url = f"{OLLAMA_URL}/api/show" - - headers = { - "Content-Type": "application/json" - } - - data = { - "name": MODEL_NAME - } - - try: - print(f"🔄 Tentative de récupération des paramètres depuis Ollama...") - response = requests.post(model_url, headers=headers, json=data) - - if response.status_code == 200: - try: - # S'assurer que nous avons un JSON valide - if isinstance(response.text, str): - settings = json.loads(response.text) - else: - settings = response.json() - - # Créer un dossier 'settings' s'il n'existe pas - os.makedirs('settings', exist_ok=True) - - # Sauvegarder la réponse brute pour debug - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - raw_filename = f"settings/ollama_raw_{timestamp}.json" - with open(raw_filename, 'w', encoding='utf-8') as f: - json.dump(settings, f, indent=2, ensure_ascii=False) - print(f"✅ Réponse brute sauvegardée dans: {raw_filename}") - - # Créer la configuration avec des valeurs par défaut - config = { - "model_name": MODEL_NAME, - "ollama_url": OLLAMA_URL, - "parameters": { - # Paramètres fondamentaux - "temperature": 0.7, - "top_p": 0.95, - "top_k": 40, - - # Système de pénalités - "repeat_penalty": 1.1, - "presence_penalty": 0.2, - "frequency_penalty": 0.2, - - # Paramètres de longueur et contexte - "num_predict": 1024, # alias pour max_tokens - "num_keep": 0, # tokens à conserver - "stop": [], # séquences d'arrêt - - # Paramètres Mirostat - "mirostat": 0, # 0 = désactivé, 1 = v1, 2 = v2 - "mirostat_eta": 0.1, # taux d'apprentissage - "mirostat_tau": 5.0, # cible de surprise - - # Paramètres de contrôle avancé - "logit_bias": {}, # biais pour certains tokens - "p_min": 0.05, # probabilité minimale pour le sampling - "repeat_last_n": 64, # nombre de tokens à considérer pour la pénalité de répétition - - # Paramètres système - "num_ctx": 4096, # taille du contexte - "num_thread": 4, # threads CPU - "num_gpu": 1, # nombre de GPU - "seed": 0, # graine aléatoire (0 = aléatoire) - - # Paramètres de streaming - "stream": True, # activation du streaming - - # Optimisations mémoire - "mmap": True, # utilisation de mmap - "mlock": False, # verrouillage en mémoire - - # Paramètres expérimentaux - "num_batch": 512, # taille du batch - "num_beam": 1, # beam search (1 = désactivé) - "tfs_z": 1.0, # tail free sampling - "typical_p": 1.0, # typical sampling - } - } - - # Mettre à jour avec les valeurs reçues si elles existent - if isinstance(settings, dict): - if 'parameters' in settings: - params = settings['parameters'] - if isinstance(params, dict): - config['parameters'].update(params) - - # Sauvegarder la configuration dans un fichier Python - config_filename = "settings/model_config.py" - with open(config_filename, 'w', encoding='utf-8') as f: - f.write("# Configuration automatiquement générée pour le modèle\n") - f.write(f"# Date de génération: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") - f.write("MODEL_CONFIG = {\n") - for key, value in config.items(): - if isinstance(value, dict): - f.write(f" '{key}': {{\n") - for k, v in value.items(): - f.write(f" '{k}': {repr(v)},\n") - f.write(" },\n") - else: - f.write(f" '{key}': {repr(value)},\n") - f.write("}\n") - - # Sauvegarder aussi en JSON - config_json_filename = "settings/model_config.json" - with open(config_json_filename, 'w', encoding='utf-8') as f: - json.dump(config, f, indent=2, ensure_ascii=False) - - print(f"✅ Configuration générée dans: {config_filename}") - print(f"✅ Configuration JSON sauvegardée dans: {config_json_filename}") - print("\nParamètres récupérés:") - print(json.dumps(config, indent=2, ensure_ascii=False)) - - except json.JSONDecodeError as e: - print(f"❌ Erreur de décodage JSON: {str(e)}") - print("Réponse reçue:", response.text[:200]) - - else: - print(f"❌ Erreur {response.status_code}: {response.text}") - - # Essayons de lister les modèles disponibles - list_url = f"{OLLAMA_URL}/api/tags" - list_response = requests.get(list_url) - if list_response.status_code == 200: - models = list_response.json() - print("\n📋 Modèles disponibles:") - for model in models.get('models', []): - print(f"- {model.get('name', 'Unknown')}") - - except requests.exceptions.RequestException as e: - print(f"❌ Erreur de connexion: {str(e)}") - print("\nVérifiez que:") - print(f"1. Le serveur Ollama est accessible sur {OLLAMA_URL}") - print(f"2. Le modèle {MODEL_NAME} est bien installé") - -if __name__ == "__main__": - get_model_settings() \ No newline at end of file diff --git a/utils/script_settings_openUI.py b/utils/script_settings_openUI.py deleted file mode 100644 index 53ee611..0000000 --- a/utils/script_settings_openUI.py +++ /dev/null @@ -1,71 +0,0 @@ -import requests -import json -import os -from datetime import datetime - -# Configuration -MODEL_NAME = "llama3.2-vision:90b-instruct-q8_0" -OPENUI_BASE_URL = "http://localhost:3000" - -def save_model_settings(): - # Essayons différents endpoints possibles - endpoints = [ - "/api/settings", - "/api/v1/settings", - f"/api/models/{MODEL_NAME}/settings", - "/api/chat/settings" - ] - - headers = { - "Content-Type": "application/json", - "Accept": "application/json" - } - - for endpoint in endpoints: - settings_url = f"{OPENUI_BASE_URL}{endpoint}" - try: - print(f"\n🔄 Tentative avec l'endpoint: {settings_url}") - response = requests.get(settings_url, headers=headers) - - # Vérifier si la réponse est valide - if response.status_code == 200: - try: - settings = response.json() - - # Créer un dossier 'settings' s'il n'existe pas - os.makedirs('settings', exist_ok=True) - - # Générer le nom du fichier avec la date - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - filename = f"settings/model_settings_{timestamp}.json" - - # Sauvegarder les paramètres dans un fichier JSON - with open(filename, 'w', encoding='utf-8') as f: - json.dump(settings, f, indent=2, ensure_ascii=False) - - print(f"✅ Paramètres sauvegardés dans: {filename}") - print("\nContenu récupéré:") - print(json.dumps(settings, indent=2, ensure_ascii=False)) - return - - except json.JSONDecodeError: - print("❌ Réponse reçue mais format JSON invalide") - print(f"Contenu reçu: {response.text[:200]}...") # Affiche les 200 premiers caractères - continue - else: - print(f"❌ Erreur {response.status_code}: {response.text}") - - except requests.exceptions.RequestException as e: - print(f"❌ Erreur de connexion: {str(e)}") - continue - - print("\n❌ Aucun endpoint n'a fonctionné. Vérifiez que:") - print("1. OpenUI est bien lancé sur localhost:3000") - print("2. Vous avez les droits d'accès à l'API") - print(f"3. Le modèle {MODEL_NAME} est bien chargé") - print("\nPour déboguer, vous pouvez:") - print("1. Vérifier l'URL dans votre navigateur quand vous accédez aux paramètres") - print("2. Inspecter les requêtes réseau dans les outils de développement du navigateur") - -if __name__ == "__main__": - save_model_settings() \ No newline at end of file