import gradio as gr import os from PIL import Image, ImageEnhance import requests import io import gc import json from typing import Tuple, Optional, Dict, Any import logging import numpy as np import cv2 from dotenv import load_dotenv # Configuration du logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # Chargement des variables d'environnement load_dotenv() # Styles artistiques enrichis ART_STYLES = { "Ultra Réaliste": { "prompt_prefix": "ultra realistic photograph, stunning photorealistic quality, unreal engine 5 quality, cinema quality, masterpiece, perfect composition, award winning photography, professional lighting, 8k UHD", "negative_prompt": "artificial, digital art, illustration, painting, drawing, artistic, cartoon, anime, unreal, fake, low quality, blurry, soft, deformed", "quality_boost": 1.2 }, "Photoréaliste": { "prompt_prefix": "hyperrealistic studio photograph, extremely detailed, professional photography, perfect lighting, high-end camera, 8k uhd", "negative_prompt": "artificial, illustration, painting, animated, cartoon, artistic", "quality_boost": 1.1 }, "Art Moderne": { "prompt_prefix": "modern art style, professional design, contemporary aesthetic, trending artwork, perfect composition", "negative_prompt": "old style, vintage, traditional, amateur, low quality", "quality_boost": 1.0 }, "Minimaliste": { "prompt_prefix": "minimalist design, clean composition, elegant simplicity, refined aesthetic", "negative_prompt": "complex, cluttered, busy, ornate, detailed", "quality_boost": 1.0 } } class ImageGenerator: def __init__(self): self.API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0" token = os.getenv('HUGGINGFACE_TOKEN') if not token: logger.error("HUGGINGFACE_TOKEN non trouvé!") self.headers = {"Authorization": f"Bearer {token}"} logger.info("ImageGenerator initialisé") def _enhance_image(self, image: Image.Image, params: Dict[str, Any]) -> Image.Image: """Amélioration avancée de la qualité d'image""" try: # Conversion en CV2 pour traitement avancé cv2_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) # Débruitage adaptatif if params.get("quality", 35) > 40: cv2_image = cv2.fastNlMeansDenoisingColored(cv2_image, None, 10, 10, 7, 21) # Amélioration des détails kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]]) cv2_image = cv2.filter2D(cv2_image, -1, kernel) # Reconversion en PIL image = Image.fromarray(cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)) # Ajustements fins avec PIL enhancers = [ (ImageEnhance.Sharpness, params.get("detail_level", 7) / 5), (ImageEnhance.Contrast, params.get("contrast", 5) / 5), (ImageEnhance.Color, params.get("saturation", 5) / 5) ] for enhancer_class, factor in enhancers: if factor != 1.0: image = enhancer_class(image).enhance(factor) return image except Exception as e: logger.error(f"Erreur traitement image: {str(e)}") return image def _build_prompt(self, params: Dict[str, Any]) -> str: """Construction optimisée du prompt""" try: style_info = ART_STYLES.get(params["style"], ART_STYLES["Art Moderne"]) # Construction du prompt principal base_prompt = f"{params['subject']}" if params.get('title'): base_prompt += f", with text '{params['title']}'" # Ajout des éléments de style prompt = f"{base_prompt}, {style_info['prompt_prefix']}" return prompt except Exception as e: logger.error(f"Erreur prompt: {str(e)}") return params['subject'] def generate(self, params: Dict[str, Any]) -> Tuple[Optional[Image.Image], str]: try: if 'Bearer None' in self.headers['Authorization']: return None, "⚠️ Erreur: Token Hugging Face non configuré" # Optimisation des paramètres style_info = ART_STYLES.get(params["style"], ART_STYLES["Art Moderne"]) quality_boost = style_info.get("quality_boost", 1.0) # Préparation de la requête prompt = self._build_prompt(params) payload = { "inputs": prompt, "parameters": { "negative_prompt": style_info["negative_prompt"], "num_inference_steps": min(int(40 * quality_boost), 50), "guidance_scale": min(8.0 * quality_boost, 12.0), "width": 1024 if params.get("quality", 35) > 40 else 768, "height": 1024 if params["orientation"] == "Portrait" else 768 } } response = requests.post( self.API_URL, headers=self.headers, json=payload, timeout=45 ) if response.status_code == 200: image = Image.open(io.BytesIO(response.content)) # Application des améliorations de qualité enhanced_image = self._enhance_image(image, params) return enhanced_image, "✨ Création réussie!" else: error_msg = f"⚠️ Erreur API {response.status_code}: {response.text}" logger.error(error_msg) return None, error_msg except Exception as e: error_msg = f"⚠️ Erreur: {str(e)}" logger.exception("Erreur génération:") return None, error_msg finally: gc.collect() def create_interface(): generator = ImageGenerator() with gr.Blocks(css="style.css") as app: gr.HTML("""

🎨 Equity Artisan 3.0

Assistant de création d'affiches professionnelles

""") with gr.Column(): # Contrôles principaux with gr.Group(): gr.Markdown("### 📐 Format et Style") with gr.Row(): format_size = gr.Dropdown( choices=["A4", "A3", "A2", "A1"], value="A4", label="Format" ) orientation = gr.Radio( choices=["Portrait", "Paysage"], value="Portrait", label="Orientation" ) style = gr.Dropdown( choices=list(ART_STYLES.keys()), value="Art Moderne", label="Style artistique" ) # Description with gr.Group(): gr.Markdown("### 📝 Description") subject = gr.Textbox( label="Description", placeholder="Décrivez votre vision...", lines=3 ) title = gr.Textbox( label="Titre (optionnel)", placeholder="Titre à inclure..." ) # Paramètres avancés with gr.Group(): gr.Markdown("### ⚙️ Paramètres") with gr.Row(): quality = gr.Slider( minimum=30, maximum=50, value=35, label="Qualité" ) detail_level = gr.Slider( minimum=1, maximum=10, value=7, step=1, label="Niveau de Détail" ) creativity = gr.Slider( minimum=5, maximum=15, value=7.5, label="Créativité" ) # Boutons with gr.Row(): generate_btn = gr.Button("✨ Générer", variant="primary") clear_btn = gr.Button("🗑️ Effacer") # Résultat image_output = gr.Image(label="Résultat") status = gr.Textbox(label="Status", interactive=False) def generate(*args): params = { "format_size": args[0], "orientation": args[1], "style": args[2], "subject": args[3], "title": args[4], "quality": args[5], "detail_level": args[6], "creativity": args[7] } return generator.generate(params) generate_btn.click( generate, inputs=[format_size, orientation, style, subject, title, quality, detail_level, creativity], outputs=[image_output, status] ) clear_btn.click( lambda: (None, "🗑️ Image effacée"), outputs=[image_output, status] ) return app if __name__ == "__main__": app = create_interface() app.launch()