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
        # NO inicializar modelo aquí - hacerlo lazy
        
    @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 para CPU inicialmente
            config = Config(
                clip_model_name="ViT-L-14/openai",
                download_cache=True,
                chunk_size=2048,
                quiet=True,
                device="cpu"  # Siempre inicializar en CPU
            )
            
            self.interrogator = Interrogator(config)
            self.is_initialized = True
            
            # Clean up memory after initialization
            gc.collect()
            
            logger.info("Model initialized successfully on CPU")
            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
            max_size = 768  # Reducir tamaño para evitar problemas de memoria
            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',
        ]
        
        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

    def _prepare_models_for_gpu(self):
        """Prepara los modelos para GPU con la precisión correcta"""
        try:
            if hasattr(self.interrogator, 'caption_model'):
                self.interrogator.caption_model = self.interrogator.caption_model.half().to("cuda")
                
            if hasattr(self.interrogator, 'clip_model'):
                self.interrogator.clip_model = self.interrogator.clip_model.half().to("cuda")
                
            if hasattr(self.interrogator, 'blip_model'):
                self.interrogator.blip_model = self.interrogator.blip_model.half().to("cuda")
                
            self.interrogator.config.device = "cuda"
            logger.info("Models prepared for GPU with FP16")
            
        except Exception as e:
            logger.error(f"Error preparing models for GPU: {e}")
            raise

    def _prepare_models_for_cpu(self):
        """Prepara los modelos para CPU con float32"""
        try:
            if hasattr(self.interrogator, 'caption_model'):
                self.interrogator.caption_model = self.interrogator.caption_model.float().to("cpu")
                
            if hasattr(self.interrogator, 'clip_model'):
                self.interrogator.clip_model = self.interrogator.clip_model.float().to("cpu")
                
            if hasattr(self.interrogator, 'blip_model'):
                self.interrogator.blip_model = self.interrogator.blip_model.float().to("cpu")
                
            self.interrogator.config.device = "cpu"
            logger.info("Models prepared for CPU with FP32")
            
        except Exception as e:
            logger.error(f"Error preparing models for CPU: {e}")
            raise

    @spaces.GPU(duration=60)
    def run_clip_inference(self, image: Image.Image) -> Tuple[str, str, str]:
        """Solo la inferencia CLIP usa GPU"""
        try:
            # Preparar modelos para GPU
            self._prepare_models_for_gpu()
            
            # Usar autocast para manejar precisión mixta
            with torch.cuda.amp.autocast(enabled=True, dtype=torch.float16):
                # Convertir imagen a tensor y asegurar que esté en half precision
                from torchvision import transforms
                preprocess = transforms.Compose([
                    transforms.Resize((224, 224)),
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], 
                                       std=[0.26862954, 0.26130258, 0.27577711]),
                ])
                
                # Procesar imagen manualmente para controlar la precisión
                image_tensor = preprocess(image).unsqueeze(0).half().to("cuda")
                
                # Ejecutar inferencias con manejo especial
                full_prompt = self._safe_interrogate(image, 'interrogate')
                clip_fast = self._safe_interrogate(image, 'interrogate_fast')
                clip_classic = self._safe_interrogate(image, 'interrogate_classic')
            
            return full_prompt, clip_fast, clip_classic
            
        except Exception as e:
            logger.error(f"GPU inference error: {e}")
            # Intentar en CPU como fallback
            return self._run_cpu_inference(image)
    
    def _safe_interrogate(self, image: Image.Image, method: str) -> str:
        """Ejecuta interrogate de forma segura manejando precisión"""
        try:
            # Temporalmente parchear el método de procesamiento de imagen
            original_method = getattr(self.interrogator, method)
            
            # Ejecutar el método
            result = original_method(image)
            
            return result
            
        except Exception as e:
            logger.error(f"Error in {method}: {e}")
            return f"Error processing with {method}"
    
    def _run_cpu_inference(self, image: Image.Image) -> Tuple[str, str, str]:
        """Ejecuta inferencia en CPU como fallback"""
        try:
            logger.info("Running CPU inference as fallback")
            
            # Preparar modelos para CPU
            self._prepare_models_for_cpu()
            
            # Ejecutar en CPU sin autocast
            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"CPU inference also failed: {e}")
            return "Error: Failed to process image", "Error", "Error"

    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:
            # Inicializar modelo si no está inicializado
            if not self.is_initialized:
                if not self.initialize_model():
                    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 complete pipeline with multi-model analysis")
            
            # Ejecutar inferencia CLIP
            full_prompt, clip_fast, clip_classic = self.run_clip_inference(image)
            
            # Verificar si hubo errores
            if "Error" in full_prompt:
                logger.warning("Using fallback prompt due to inference error")
                full_prompt = "A photograph"
                clip_fast = "image"
                clip_classic = "picture"
            
            logger.info(f"CLIP complete prompt: {full_prompt[:100]}...")
            
            # NUEVO: Ejecutar análisis ultra supremo con múltiples modelos
            logger.info("Running multi-model ultra supreme analysis...")
            ultra_analysis = self.analyzer.ultra_supreme_analysis(
                image, clip_fast, clip_classic, full_prompt
            )
            
            # Construir prompt mejorado basado en análisis completo
            enhanced_prompt_parts = []
            
            # Base prompt de CLIP
            enhanced_prompt_parts.append(full_prompt)
            
            # Agregar información demográfica si está disponible
            if ultra_analysis["demographic"]["gender"] and ultra_analysis["demographic"]["gender_confidence"] > 0.7:
                gender = ultra_analysis["demographic"]["gender"]
                age_cat = ultra_analysis["demographic"]["age_category"]
                if age_cat:
                    enhanced_prompt_parts.append(f"{age_cat} {gender}")
            
            # Agregar estado emocional principal
            if ultra_analysis["emotional_state"]["primary_emotion"] and ultra_analysis["emotional_state"]["emotion_confidence"] > 0.6:
                emotion = ultra_analysis["emotional_state"]["primary_emotion"]
                enhanced_prompt_parts.append(f"{emotion} expression")
            
            # Agregar información de pose si está disponible
            if ultra_analysis["pose_composition"]["posture"]:
                enhanced_prompt_parts.append(ultra_analysis["pose_composition"]["posture"][0])
            
            # Combinar y aplicar reglas de Flux
            combined_prompt = ", ".join(enhanced_prompt_parts)
            optimized_prompt = self.apply_flux_rules(combined_prompt)
            
            # Si el analyzer enriqueció el prompt, úsalo
            analyzer_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [full_prompt])
            if len(analyzer_prompt) > len(optimized_prompt):
                optimized_prompt = self.apply_flux_rules(analyzer_prompt)
            
            # Calcular score usando el analyzer
            score, breakdown = self.analyzer.calculate_ultra_supreme_score(optimized_prompt, ultra_analysis)
            
            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 enhanced analysis report con datos de múltiples modelos
            analysis_info = self._generate_ultra_analysis_report(
                ultra_analysis, score, breakdown, duration
            )
            
            return optimized_prompt, analysis_info, score, breakdown
            
        except Exception as e:
            logger.error(f"Ultra supreme generation error: {e}", exc_info=True)
            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"]
        }
        
        prompt_lower = prompt.lower()
        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"""
        if not prompt:
            return "Unknown"
            
        # Tomar las primeras palabras significativas
        words = prompt.split(',')[0].split()
        if len(words) > 3:
            return ' '.join(words[:4])
        return prompt.split(',')[0] if prompt else "Unknown"

    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_ultra_analysis_report(self, analysis: Dict[str, Any], 
                                       score: int, breakdown: Dict[str, int], 
                                       duration: float) -> str:
        """Generate ultra detailed analysis report with multi-model results"""
        
        device_used = "cuda" if torch.cuda.is_available() else "cpu"
        gpu_status = "⚡ ZeroGPU" if device_used == "cuda" else "💻 CPU"
        
        # Demographic info
        demo_info = ""
        if analysis["demographic"]["age_category"]:
            age = analysis["demographic"]["age_category"].replace("_", " ").title()
            gender = analysis["demographic"]["gender"] or "person"
            confidence = analysis["demographic"]["age_confidence"]
            demo_info = f"**Detected:** {age} {gender} (confidence: {confidence:.0%})"
        
        # Emotion info
        emotion_info = ""
        if analysis["emotional_state"]["primary_emotion"]:
            emotion = analysis["emotional_state"]["primary_emotion"]
            confidence = analysis["emotional_state"]["emotion_confidence"]
            emotion_info = f"**Primary Emotion:** {emotion} ({confidence:.0%})"
            
            # Add emotion distribution if available
            if analysis["emotional_state"]["emotion_distribution"]:
                top_emotions = sorted(
                    analysis["emotional_state"]["emotion_distribution"].items(),
                    key=lambda x: x[1], reverse=True
                )[:3]
                emotion_details = ", ".join([f"{e[0]}: {e[1]:.0%}" for e in top_emotions])
                emotion_info += f"\n**Emotion Distribution:** {emotion_details}"
        
        # Face analysis info
        face_info = f"**Faces Detected:** {analysis['facial_ultra']['face_count']}"
        if analysis['facial_ultra']['face_count'] > 0:
            features = []
            for feature_type in ['eyes', 'mouth', 'facial_hair', 'skin']:
                if analysis['facial_ultra'].get(feature_type):
                    features.extend(analysis['facial_ultra'][feature_type])
            if features:
                face_info += f"\n**Facial Features:** {', '.join(features[:5])}"
        
        # Pose info
        pose_info = ""
        if analysis["pose_composition"].get("pose_confidence", 0) > 0:
            confidence = analysis["pose_composition"]["pose_confidence"]
            pose_info = f"**Pose Analysis:** Body detected ({confidence:.0%} confidence)"
            if analysis["pose_composition"]["posture"]:
                pose_info += f"\n**Posture:** {', '.join(analysis['pose_composition']['posture'])}"
        
        # Environment info
        env_info = ""
        if analysis["environmental"]["setting_type"]:
            env_info = f"**Setting:** {analysis['environmental']['setting_type'].replace('_', ' ').title()}"
        if analysis["environmental"]["lighting_analysis"]:
            env_info += f"\n**Lighting:** {', '.join(analysis['environmental']['lighting_analysis'])}"
        
        # Intelligence metrics
        metrics = analysis["intelligence_metrics"]
        
        analysis_info = f"""**🚀 ULTRA SUPREME MULTI-MODEL ANALYSIS COMPLETE**
**Processing:** {gpu_status} • {duration:.1f}s • Multi-Model Pipeline
**Ultra Score:** {score}/100 • Models: CLIP + DeepFace + MediaPipe + Transformers

**📊 BREAKDOWN:**
• Prompt Quality: {breakdown.get('prompt_quality', 0)}/25
• Analysis Depth: {breakdown.get('analysis_depth', 0)}/25  
• Model Confidence: {breakdown.get('model_confidence', 0)}/25
• Feature Richness: {breakdown.get('feature_richness', 0)}/25

**🧠 DEEP ANALYSIS RESULTS:**

**👤 DEMOGRAPHICS & IDENTITY:**
{demo_info or "No face detected for demographic analysis"}

**😊 EMOTIONAL ANALYSIS:**
{emotion_info or "No emotional data available"}

**👁️ FACIAL ANALYSIS:**
{face_info}

**🚶 POSE & BODY LANGUAGE:**
{pose_info or "No pose data available"}

**🏞️ ENVIRONMENT & SCENE:**
{env_info or "No environmental data detected"}

**📊 INTELLIGENCE METRICS:**
• **Total Features Detected:** {metrics['total_features_detected']}
• **Analysis Depth Score:** {metrics['analysis_depth_score']}/100
• **Model Confidence Average:** {metrics['model_confidence_average']:.0%}
• **Technical Optimization:** {metrics['technical_optimization_score']}/100

**✨ MULTI-MODEL ADVANTAGES:**
✅ DeepFace: Accurate age, gender, emotion detection
✅ MediaPipe: Body pose and gesture analysis
✅ CLIP: Semantic understanding and context
✅ Transformers: Advanced emotion classification
✅ OpenCV: Robust face detection

**🔬 Powered by Pariente AI Research • Ultra Supreme Intelligence Engine**"""
        
        return analysis_info