optimizer.py

"""
Ultra Supreme Optimizer - Main optimization engine for image analysis
VERSIÓN MEJORADA - Usa el prompt completo de CLIP Interrogator
"""

# IMPORTANT: spaces must be imported BEFORE torch or any CUDA-using library
import spaces
import gc
import logging
import re
from datetime import datetime
from typing import Tuple, Dict, Any, Optional

import torch
import numpy as np
from PIL import Image
from clip_interrogator import Config, Interrogator

from analyzer import UltraSupremeAnalyzer

logger = logging.getLogger(__name__)


class UltraSupremeOptimizer:
    """Main optimizer class for ultra supreme image analysis"""
    
    def __init__(self):
        self.interrogator: Optional[Interrogator] = None
        self.analyzer = UltraSupremeAnalyzer()
        self.usage_count = 0
        self.device = self._get_device()
        self.is_initialized = False
        # Inicializar modelo inmediatamente
        self.initialize_model()

    @staticmethod
    def _get_device() -> str:
        """Determine the best available device for computation"""
        if torch.cuda.is_available():
            return "cuda"
        elif torch.backends.mps.is_available():
            return "mps"
        else:
            return "cpu"

    def initialize_model(self) -> bool:
        """Initialize the CLIP interrogator model"""
        if self.is_initialized:
            return True
        
        try:
            # Configuración estándar sin forzar precisión
            config = Config(
                clip_model_name="ViT-L-14/openai",
                download_cache=True,
                chunk_size=2048,
                quiet=True,
                device="cpu"  # Inicializar en CPU
            )
            
            self.interrogator = Interrogator(config)
            self.is_initialized = True
            
            # Clean up memory after initialization
            gc.collect()
                
            return True
            
        except Exception as e:
            logger.error(f"Initialization error: {e}")
            return False

    def optimize_image(self, image: Any) -> Optional[Image.Image]:
        """Optimize image for processing"""
        if image is None:
            return None
            
        try:
            # Convert to PIL Image if necessary
            if isinstance(image, np.ndarray):
                image = Image.fromarray(image)
            elif not isinstance(image, Image.Image):
                image = Image.open(image)
            
            # Convert to RGB if necessary
            if image.mode != 'RGB':
                image = image.convert('RGB')
            
            # Resize if too large - usar tamaño generoso para máxima calidad
            max_size = 1024 if self.device != "cpu" else 768
            if image.size[0] > max_size or image.size[1] > max_size:
                image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
            
            return image
            
        except Exception as e:
            logger.error(f"Image optimization error: {e}")
            return None

    def apply_flux_rules(self, base_prompt: str) -> str:
        """Aplica las reglas de Flux a un prompt base de CLIP Interrogator"""
        
        # Limpiar el prompt de elementos no deseados
        cleanup_patterns = [
            r',\s*trending on artstation',
            r',\s*trending on [^,]+',
            r',\s*\d+k\s*',
            r',\s*\d+k resolution',
            r',\s*artstation',
            r',\s*concept art',
            r',\s*digital art',
            r',\s*by greg rutkowski',  # Remover artistas genéricos overused
        ]
        
        cleaned_prompt = base_prompt
        for pattern in cleanup_patterns:
            cleaned_prompt = re.sub(pattern, '', cleaned_prompt, flags=re.IGNORECASE)
        
        # Detectar el tipo de imagen para añadir configuración de cámara apropiada
        camera_config = ""
        if any(word in base_prompt.lower() for word in ['portrait', 'person', 'man', 'woman', 'face']):
            camera_config = ", Shot on Hasselblad X2D 100C, 90mm f/2.5 lens at f/2.8, professional portrait photography"
        elif any(word in base_prompt.lower() for word in ['landscape', 'mountain', 'nature', 'outdoor']):
            camera_config = ", Shot on Phase One XT, 40mm f/4 lens at f/8, epic landscape photography"
        elif any(word in base_prompt.lower() for word in ['street', 'urban', 'city']):
            camera_config = ", Shot on Leica M11, 35mm f/1.4 lens at f/2.8, documentary street photography"
        else:
            camera_config = ", Shot on Phase One XF IQ4, 80mm f/2.8 lens at f/4, professional photography"
        
        # Añadir mejoras de iluminación si no están presentes
        if 'lighting' not in cleaned_prompt.lower():
            if 'dramatic' in cleaned_prompt.lower():
                cleaned_prompt += ", dramatic cinematic lighting"
            elif 'portrait' in cleaned_prompt.lower():
                cleaned_prompt += ", professional studio lighting with subtle rim light"
            else:
                cleaned_prompt += ", masterful natural lighting"
        
        # Construir el prompt final
        final_prompt = cleaned_prompt + camera_config
        
        # Asegurar que empiece con mayúscula
        final_prompt = final_prompt[0].upper() + final_prompt[1:] if final_prompt else final_prompt
        
        # Limpiar espacios y comas duplicadas
        final_prompt = re.sub(r'\s+', ' ', final_prompt)
        final_prompt = re.sub(r',\s*,+', ',', final_prompt)
        
        return final_prompt

    @spaces.GPU
    def run_clip_inference(self, image: Image.Image) -> Tuple[str, str, str]:
        """Solo la inferencia CLIP usa GPU"""
        try:
            # Mover modelos a GPU sin forzar precisión
            if self.device == "cuda":
                # Configurar el dispositivo en el interrogator
                self.interrogator.config.device = "cuda"
                
                # Mover modelos a GPU manteniendo su precisión nativa
                if hasattr(self.interrogator, 'clip_model') and self.interrogator.clip_model is not None:
                    self.interrogator.clip_model = self.interrogator.clip_model.to("cuda")
                    logger.info("CLIP model moved to GPU with native precision")
                
                if hasattr(self.interrogator, 'blip_model') and self.interrogator.blip_model is not None:
                    self.interrogator.blip_model = self.interrogator.blip_model.to("cuda")
                    logger.info("BLIP model moved to GPU with native precision")
            
            # Ejecutar inferencias CLIP con precisión nativa
            full_prompt = self.interrogator.interrogate(image)
            clip_fast = self.interrogator.interrogate_fast(image)
            clip_classic = self.interrogator.interrogate_classic(image)
            
            return full_prompt, clip_fast, clip_classic
            
        except Exception as e:
            logger.error(f"CLIP inference error: {e}")
            # Si falla en GPU, intentar en CPU
            if self.device == "cuda":
                logger.info("Falling back to CPU inference")
                self.interrogator.config.device = "cpu"
                
                if hasattr(self.interrogator, 'clip_model') and self.interrogator.clip_model is not None:
                    self.interrogator.clip_model = self.interrogator.clip_model.to("cpu")
                
                if hasattr(self.interrogator, 'blip_model') and self.interrogator.blip_model is not None:
                    self.interrogator.blip_model = self.interrogator.blip_model.to("cpu")
                
                # Reintentar en CPU
                full_prompt = self.interrogator.interrogate(image)
                clip_fast = self.interrogator.interrogate_fast(image)
                clip_classic = self.interrogator.interrogate_classic(image)
                
                return full_prompt, clip_fast, clip_classic
            else:
                raise e

    def generate_ultra_supreme_prompt(self, image: Any) -> Tuple[str, str, int, Dict[str, int]]:
        """
        Generate ultra supreme prompt from image usando el pipeline completo
        
        Returns:
            Tuple of (prompt, analysis_info, score, breakdown)
        """
        try:
            # Verificar que el modelo esté inicializado
            if not self.is_initialized:
                return "❌ Model initialization failed.", "Please refresh and try again.", 0, {}
            
            # Validate input
            if image is None:
                return "❌ Please upload an image.", "No image provided.", 0, {}
            
            self.usage_count += 1
            
            # Optimize image
            image = self.optimize_image(image)
            if image is None:
                return "❌ Image processing failed.", "Invalid image format.", 0, {}
            
            start_time = datetime.now()
            
            logger.info("ULTRA SUPREME ANALYSIS - Starting pipeline")
            
            # Ejecutar inferencia CLIP en GPU
            full_prompt, clip_fast, clip_classic = self.run_clip_inference(image)
            
            logger.info(f"Prompt completo de CLIP Interrogator: {full_prompt}")
            logger.info(f"Análisis Fast: {clip_fast}")
            logger.info(f"Análisis Classic: {clip_classic}")
            
            # 3. Aplicar reglas de Flux al prompt completo
            optimized_prompt = self.apply_flux_rules(full_prompt)
            
            # 4. Crear análisis para el reporte (simplificado)
            analysis_summary = {
                "base_prompt": full_prompt,
                "clip_fast": clip_fast,
                "clip_classic": clip_classic,
                "optimized": optimized_prompt,
                "detected_style": self._detect_style(full_prompt),
                "detected_subject": self._detect_subject(full_prompt)
            }
            
            # 5. Calcular score basado en la riqueza del prompt
            score = self._calculate_score(optimized_prompt, full_prompt)
            breakdown = {
                "base_quality": min(len(full_prompt) // 10, 25),
                "technical_enhancement": 25 if "Shot on" in optimized_prompt else 0,
                "lighting_quality": 25 if "lighting" in optimized_prompt.lower() else 0,
                "composition": 25 if any(word in optimized_prompt.lower() for word in ["professional", "masterful", "epic"]) else 0
            }
            score = sum(breakdown.values())
            
            end_time = datetime.now()
            duration = (end_time - start_time).total_seconds()
            
            # Memory cleanup
            gc.collect()
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            
            # Generate analysis report
            analysis_info = self._generate_analysis_report(
                analysis_summary, score, breakdown, duration
            )
            
            return optimized_prompt, analysis_info, score, breakdown
            
        except Exception as e:
            logger.error(f"Ultra supreme generation error: {e}")
            return f"❌ Error: {str(e)}", "Please try with a different image.", 0, {}

    def _detect_style(self, prompt: str) -> str:
        """Detecta el estilo principal del prompt"""
        styles = {
            "portrait": ["portrait", "person", "face", "headshot"],
            "landscape": ["landscape", "mountain", "nature", "scenery"],
            "street": ["street", "urban", "city"],
            "artistic": ["artistic", "abstract", "conceptual"],
            "dramatic": ["dramatic", "cinematic", "moody"]
        }
        
        for style_name, keywords in styles.items():
            if any(keyword in prompt.lower() for keyword in keywords):
                return style_name
        
        return "general"

    def _detect_subject(self, prompt: str) -> str:
        """Detecta el sujeto principal del prompt"""
        # Tomar las primeras palabras significativas
        words = prompt.split(',')[0].split()
        if len(words) > 3:
            return ' '.join(words[:4])
        return prompt.split(',')[0]

    def _calculate_score(self, optimized_prompt: str, base_prompt: str) -> int:
        """Calcula el score basado en la calidad del prompt"""
        score = 0
        
        # Base score por longitud y riqueza
        score += min(len(base_prompt) // 10, 25)
        
        # Technical enhancement
        if "Shot on" in optimized_prompt:
            score += 25
        
        # Lighting quality
        if "lighting" in optimized_prompt.lower():
            score += 25
        
        # Professional quality
        if any(word in optimized_prompt.lower() for word in ["professional", "masterful", "epic", "cinematic"]):
            score += 25
        
        return min(score, 100)

    def _generate_analysis_report(self, analysis: Dict[str, Any], 
                                  score: int, breakdown: Dict[str, int], 
                                  duration: float) -> str:
        """Generate detailed analysis report"""
        
        gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
        precision_info = "Native Model Precision" if torch.cuda.is_available() else "CPU Processing"
        
        # Extraer información clave
        detected_style = analysis.get("detected_style", "general").title()
        detected_subject = analysis.get("detected_subject", "Unknown")
        base_prompt_preview = analysis.get("base_prompt", "")[:100] + "..." if len(analysis.get("base_prompt", "")) > 100 else analysis.get("base_prompt", "")
        
        analysis_info = f"""**🚀 ULTRA SUPREME ANALYSIS COMPLETE**
**Processing:** {gpu_status} • {duration:.1f}s • {precision_info}  
**Ultra Score:** {score}/100 • Breakdown: Base({breakdown.get('base_quality',0)}) Technical({breakdown.get('technical_enhancement',0)}) Lighting({breakdown.get('lighting_quality',0)}) Composition({breakdown.get('composition',0)})  
**Generation:** #{self.usage_count}  

**🧠 INTELLIGENT DETECTION:**
- **Detected Style:** {detected_style}
- **Main Subject:** {detected_subject}
- **Precision:** Using native model precision for optimal performance
- **Quality:** Maximum resolution processing (1024px)

**📊 CLIP INTERROGATOR ANALYSIS:**
- **Base Prompt:** {base_prompt_preview}
- **Fast Analysis:** {analysis.get('clip_fast', '')[:80]}...
- **Classic Analysis:** {analysis.get('clip_classic', '')[:80]}...

**⚡ OPTIMIZATION APPLIED:**
- ✅ Native precision inference for stability
- ✅ GPU acceleration when available
- ✅ Automatic fallback to CPU if needed
- ✅ Added professional camera specifications
- ✅ Enhanced lighting descriptions
- ✅ Applied Flux-specific optimizations
- ✅ Removed redundant/generic elements

**🔬 Powered by Pariente AI Research + CLIP Interrogator**"""
        
        return analysis_info