prompt_enhancer.py

from typing import Dict, List, Optional
import logging
import re

logger = logging.getLogger(__name__)

class PromptEnhancer:
    def __init__(self):
        self.context_keywords = {
            # Éléments de design
            "moderne": "modern clean professional design",
            "vintage": "vintage retro classic design",
            "minimaliste": "minimalist clean simple design",
            "luxe": "luxury elegant premium design",
            
            # Types d'ambiance
            "professionnel": "professional corporate business-like",
            "créatif": "creative artistic innovative",
            "dynamique": "dynamic energetic vibrant",
            "élégant": "elegant sophisticated refined",
            
            # Éléments visuels
            "logo": "prominent logo design professional branding",
            "texte": "clear readable text typography",
            "image": "main visual focal point image",
            "photo": "photographic image realistic",
            
            # Caractéristiques techniques
            "haute qualité": "high quality professional grade",
            "détaillé": "highly detailed intricate",
            "net": "sharp crisp clear",
            "flou": "soft focus gentle blur",
            
            # Styles spécifiques
            "3D": "three dimensional depth realistic",
            "plat": "flat design 2D clean",
            "graphique": "graphic design vector-style",
            "illustré": "illustrated hand-drawn artistic"
        }
        
        self.composition_patterns = {
            # Structure de l'affiche
            "haut": "top aligned composition with {element}",
            "bas": "bottom aligned composition with {element}",
            "centre": "centered composition with {element}",
            "gauche": "left aligned composition with {element}",
            "droite": "right aligned composition with {element}",
            
            # Relations spatiales
            "au-dessus": "{element1} positioned above {element2}",
            "en-dessous": "{element1} positioned below {element2}",
            "à côté": "{element1} next to {element2}",
            "autour": "{element1} surrounding {element2}"
        }
        
        self.emphasis_patterns = {
            "important": "(({})),",  # Double emphase
            "normal": "({}),",       # Emphase simple
            "subtil": "[{}],"        # Emphase légère
        }
        
        self.common_improvements = {
            "faire": "create",
            "mettre": "place",
            "avec": "featuring",
            "contenant": "containing",
            "il y a": "featuring",
            "je veux": "",
            "je souhaite": "",
            "il faut": ""
        }def enhance_prompt(self, user_input: str, style_context: Dict) -> str:
        """Améliore le prompt utilisateur avec un contexte enrichi"""
        enhanced_prompt = user_input.lower()
        logger.debug(f"Prompt initial: {enhanced_prompt}")

        # Nettoyage initial
        enhanced_prompt = self._clean_prompt(enhanced_prompt)
        
        # Détection et amélioration du contexte
        enhanced_prompt = self._add_context(enhanced_prompt)
        
        # Ajout des éléments de style
        enhanced_prompt = self._add_style_elements(enhanced_prompt, style_context)
        
        # Structure et emphase
        enhanced_prompt = self._structure_prompt(enhanced_prompt)
        
        # Ajout des qualificatifs techniques
        enhanced_prompt = self._add_technical_qualifiers(enhanced_prompt)
        
        logger.debug(f"Prompt amélioré: {enhanced_prompt}")
        return enhanced_prompt

    def _clean_prompt(self, prompt: str) -> str:
        """Nettoie et normalise le prompt"""
        # Remplace les expressions communes par leurs versions optimisées
        for old, new in self.common_improvements.items():
            prompt = prompt.replace(old, new)
        
        # Supprime les espaces multiples
        prompt = " ".join(prompt.split())
        
        return prompt

    def _add_context(self, prompt: str) -> str:
        """Ajoute du contexte basé sur les mots-clés détectés"""
        enhanced_parts = []
        words = prompt.split()
        
        for word in words:
            if word in self.context_keywords:
                enhanced_parts.append(self.context_keywords[word])
            else:
                enhanced_parts.append(word)
        
        return " ".join(enhanced_parts)

    def _add_style_elements(self, prompt: str, style_context: Dict) -> str:
        """Ajoute les éléments de style au prompt"""
        style_elements = [
            style_context.get("prompt_prefix", ""),
            prompt,
            style_context.get("layout", ""),
            style_context.get("ambiance", ""),
            style_context.get("palette", ""),
            "professional poster design",
            "high quality"
        ]
        
        return ", ".join(filter(None, style_elements))

    def _structure_prompt(self, prompt: str) -> str:
        """Structure le prompt avec une emphase appropriée"""
        # Identifie les éléments clés
        main_elements = self._identify_main_elements(prompt)
        
        structured_parts = []
        for element, importance in main_elements.items():
            pattern = self.emphasis_patterns.get(importance, "{}") 
            structured_parts.append(pattern.format(element))
        
        return " ".join(structured_parts)

    def _identify_main_elements(self, prompt: str) -> Dict[str, str]:
        """Identifie les éléments principaux et leur importance"""
        elements = {}
        
        # Analyse basique des éléments clés
        words = prompt.split()
        for word in words:
            if len(word) > 3:  # Ignore les mots très courts
                if word in self.context_keywords:
                    elements[word] = "important"
                else:
                    elements[word] = "normal"
                    
        return elements

    def _add_technical_qualifiers(self, prompt: str) -> str:
        """Ajoute des qualificatifs techniques pour améliorer la qualité"""
        technical_qualifiers = [
            "professional quality",
            "highly detailed",
            "masterful composition",
            "perfect lighting",
            "sharp focus",
            "8k resolution"
        ]
        
        return f"{prompt}, {', '.join(technical_qualifiers)}"

    def analyze_prompt_effectiveness(self, prompt: str) -> Dict:
        """Analyse l'efficacité du prompt"""
        return {
            "length": len(prompt),
            "key_elements": len(self._identify_main_elements(prompt)),
            "has_context": any(keyword in prompt for keyword in self.context_keywords),
            "has_composition": any(pattern in prompt for pattern in self.composition_patterns),
            "technical_quality": len([q for q in ["detailed", "quality", "professional"] if q in prompt])
        }