app.py

import spaces
import gradio as gr
import torch
from PIL import Image
import numpy as np
from clip_interrogator import Config, Interrogator
import logging
import os
import warnings
from datetime import datetime
import gc
import re
import math

warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
os.environ["TOKENIZERS_PARALLELISM"] = "false"
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def get_device():
    if torch.cuda.is_available():
        return "cuda"
    elif torch.backends.mps.is_available():
        return "mps"
    else:
        return "cpu"

DEVICE = get_device()

class UltraSupremeAnalyzer:
    """
    ULTRA SUPREME ANALYSIS ENGINE - ABSOLUTE MAXIMUM INTELLIGENCE
    """
    def __init__(self):
        self.forbidden_elements = ["++", "weights", "white background [en dev]"]
        # ULTRA COMPREHENSIVE VOCABULARIES - MAXIMUM DEPTH
        self.micro_age_indicators = {
            "infant": ["baby", "infant", "newborn", "toddler"],
            "child": ["child", "kid", "young", "little", "small", "youth"],
            "teen": ["teenager", "teen", "adolescent", "young adult", "student"],
            "young_adult": ["young adult", "twenties", "thirty", "youthful", "fresh"],
            "middle_aged": ["middle-aged", "forties", "fifties", "mature", "experienced"],
            "senior": ["senior", "older", "elderly", "aged", "vintage", "seasoned"],
            "elderly": ["elderly", "old", "ancient", "weathered", "aged", "gray", "grey", "white hair", "silver", "wrinkled", "lined", "creased", "time-worn", "distinguished by age"]
        }
        self.ultra_facial_analysis = {
            "eye_features": {
                "shape": ["round eyes", "almond eyes", "narrow eyes", "wide eyes", "deep-set eyes", "prominent eyes"],
                "expression": ["intense gaze", "piercing stare", "gentle eyes", "wise eyes", "tired eyes", "alert eyes", "contemplative stare", "focused gaze", "distant look"],
                "color": ["brown eyes", "blue eyes", "green eyes", "hazel eyes", "dark eyes", "light eyes"],
                "condition": ["clear eyes", "bloodshot", "bright eyes", "dull eyes", "sparkling eyes"]
            },
            "eyebrow_analysis": ["thick eyebrows", "thin eyebrows", "bushy eyebrows", "arched eyebrows", "straight eyebrows", "gray eyebrows"],
            "nose_features": ["prominent nose", "straight nose", "aquiline nose", "small nose", "wide nose", "narrow nose"],
            "mouth_expression": {
                "shape": ["thin lips", "full lips", "small mouth", "wide mouth"],
                "expression": ["slight smile", "serious expression", "frown", "neutral expression", "contemplative look", "stern look", "gentle expression"]
            },
            "facial_hair_ultra": {
                "beard_types": ["full beard", "goatee", "mustache", "stubble", "clean-shaven", "five o'clock shadow"],
                "beard_texture": ["thick beard", "thin beard", "coarse beard", "fine beard", "well-groomed beard", "unkempt beard"],
                "beard_color": ["black beard", "brown beard", "gray beard", "grey beard", "silver beard", "white beard", "salt-and-pepper beard", "graying beard"],
                "beard_length": ["long beard", "short beard", "trimmed beard", "full-length beard"]
            },
            "skin_analysis": ["smooth skin", "weathered skin", "wrinkled skin", "clear skin", "rough skin", "aged skin", "youthful skin", "tanned skin", "pale skin", "olive skin"],
            "facial_structure": ["angular face", "round face", "oval face", "square jaw", "defined cheekbones", "high cheekbones", "strong jawline", "soft features", "sharp features"]
        }
        self.emotion_micro_expressions = {
            "primary_emotions": ["happy", "sad", "angry", "fearful", "surprised", "disgusted", "contemptuous"],
            "complex_emotions": ["contemplative", "melancholic", "serene", "intense", "peaceful", "troubled", "confident", "uncertain", "wise", "stern", "gentle", "authoritative"],
            "emotional_indicators": ["furrowed brow", "raised eyebrows", "squinted eyes", "pursed lips", "relaxed expression", "tense jaw", "soft eyes", "hard stare"]
        }
        self.cultural_religious_ultra = {
            "jewish_orthodox": ["Orthodox Jewish", "Hasidic", "Ultra-Orthodox", "religious Jewish", "traditional Jewish", "devout Jewish"],
            "christian": ["Christian", "Catholic", "Protestant", "Orthodox Christian", "religious Christian"],
            "muslim": ["Muslim", "Islamic", "religious Muslim", "devout Muslim"],
            "buddhist": ["Buddhist", "monk", "religious Buddhist"],
            "general_religious": ["religious", "devout", "pious", "spiritual", "faithful", "observant"],
            "traditional_clothing": {
                "jewish": ["yarmulke", "kippah", "tallit", "tzitzit", "black hat", "Orthodox hat", "religious hat", "traditional Jewish hat"],
                "general": ["religious garment", "traditional clothing", "ceremonial dress", "formal religious attire"]
            }
        }
        self.clothing_accessories_ultra = {
            "headwear": ["hat", "cap", "beret", "headband", "turban", "hood", "helmet", "crown", "headpiece"],
            "eyewear": ["glasses", "spectacles", "sunglasses", "reading glasses", "wire-frame glasses", "thick-rimmed glasses", "designer glasses", "vintage glasses"],
            "clothing_types": ["suit", "jacket", "shirt", "dress", "robe", "uniform", "casual wear", "formal wear", "business attire"],
            "clothing_colors": ["black", "white", "gray", "blue", "red", "green", "brown", "navy", "dark", "light"],
            "clothing_styles": ["formal", "casual", "business", "traditional", "modern", "vintage", "classic", "contemporary"],
            "accessories": ["jewelry", "watch", "necklace", "ring", "bracelet", "earrings", "pin", "brooch"]
        }
        self.environmental_ultra_analysis = {
            "indoor_settings": {
                "residential": ["home", "house", "apartment", "living room", "bedroom", "kitchen", "dining room"],
                "office": ["office", "workplace", "conference room", "meeting room", "boardroom", "desk"],
                "institutional": ["school", "hospital", "government building", "court", "library"],
                "religious": ["church", "synagogue", "mosque", "temple", "chapel", "sanctuary"],
                "commercial": ["store", "restaurant", "hotel", "mall", "shop"]
            },
            "outdoor_settings": {
                "natural": ["park", "garden", "forest", "beach", "mountain", "countryside", "field"],
                "urban": ["street", "city", "downtown", "plaza", "square", "avenue"],
                "architectural": ["building", "monument", "bridge", "structure"]
            },
            "lighting_ultra": {
                "natural_light": ["sunlight", "daylight", "morning light", "afternoon light", "evening light", "golden hour", "blue hour", "overcast light", "window light"],
                "artificial_light": ["indoor lighting", "electric light", "lamp light", "overhead lighting", "side lighting", "fluorescent", "LED lighting"],
                "dramatic_lighting": ["high contrast", "low key", "high key", "chiaroscuro", "dramatic shadows", "rim lighting", "backlighting", "spotlight"],
                "quality": ["soft lighting", "hard lighting", "diffused light", "direct light", "ambient light", "mood lighting"]
            }
        }
        self.pose_body_language_ultra = {
            "head_position": ["head up", "head down", "head tilted", "head straight", "head turned", "profile view", "three-quarter view"],
            "posture": ["upright posture", "slouched", "relaxed posture", "formal posture", "casual stance", "dignified bearing"],
            "hand_positions": ["hands clasped", "hands folded", "hands visible", "hands hidden", "gesturing", "pointing"],
            "sitting_positions": ["sitting upright", "leaning forward", "leaning back", "sitting casually", "formal sitting"],
            "eye_contact": ["looking at camera", "looking away", "direct gaze", "averted gaze", "looking down", "looking up"],
            "overall_demeanor": ["confident", "reserved", "approachable", "authoritative", "gentle", "stern", "relaxed", "tense"]
        }
        self.composition_photography_ultra = {
            "shot_types": ["close-up", "medium shot", "wide shot", "extreme close-up", "portrait shot", "headshot", "bust shot", "full body"],
            "angles": ["eye level", "high angle", "low angle", "bird's eye", "worm's eye", "Dutch angle"],
            "framing": ["centered", "off-center", "rule of thirds", "tight framing", "loose framing"],
            "depth_of_field": ["shallow depth", "deep focus", "bokeh", "sharp focus", "soft focus"],
            "camera_movement": ["static", "handheld", "stabilized", "smooth"]
        }
        self.technical_photography_ultra = {
            "camera_systems": {
                "professional": ["Phase One XF", "Phase One XT", "Hasselblad X2D", "Fujifilm GFX", "Canon EOS R5", "Nikon Z9"],
                "medium_format": ["Phase One", "Hasselblad", "Fujifilm GFX", "Pentax 645"],
                "full_frame": ["Canon EOS R", "Nikon Z", "Sony A7", "Leica SL"]
            },
            "lenses_ultra": {
                "portrait": ["85mm f/1.4", "135mm f/2", "105mm f/1.4", "200mm f/2.8"],
                "standard": ["50mm f/1.4", "35mm f/1.4", "24-70mm f/2.8"],
                "wide": ["24mm f/1.4", "16-35mm f/2.8", "14mm f/2.8"]
            },
            "aperture_settings": ["f/1.4", "f/2", "f/2.8", "f/4", "f/5.6", "f/8"],
            "photography_styles": ["portrait photography", "documentary photography", "fine art photography", "commercial photography", "editorial photography"]
        }
        self.quality_descriptors_ultra = {
            "based_on_age": {
                "elderly": ["distinguished", "venerable", "dignified", "wise", "experienced", "seasoned", "time-honored", "revered", "weathered", "sage-like"],
                "middle_aged": ["professional", "accomplished", "established", "confident", "mature", "refined", "sophisticated"],
                "young_adult": ["vibrant", "energetic", "fresh", "youthful", "dynamic", "spirited", "lively"]
            },
            "based_on_emotion": {
                "contemplative": ["thoughtful", "reflective", "meditative", "introspective"],
                "confident": ["assured", "self-possessed", "commanding", "authoritative"],
                "gentle": ["kind", "warm", "compassionate", "tender"],
                "stern": ["serious", "grave", "solemn", "austere"]
            },
            "based_on_setting": {
                "formal": ["professional", "official", "ceremonial", "dignified"],
                "casual": ["relaxed", "informal", "comfortable", "natural"],
                "artistic": ["creative", "expressive", "aesthetic", "artistic"]
            }
        }

    def ultra_supreme_analysis(self, clip_fast, clip_classic, clip_best):
        """ULTRA SUPREME ANALYSIS - MAXIMUM POSSIBLE INTELLIGENCE"""
        combined_analysis = {
            "fast": clip_fast.lower(),
            "classic": clip_classic.lower(), 
            "best": clip_best.lower(),
            "combined": f"{clip_fast} {clip_classic} {clip_best}".lower()
        }
        ultra_result = {
            "demographic": {"age_category": None, "age_confidence": 0, "gender": None, "cultural_religious": []},
            "facial_ultra": {"eyes": [], "eyebrows": [], "nose": [], "mouth": [], "facial_hair": [], "skin": [], "structure": []},
            "emotional_state": {"primary_emotion": None, "emotion_confidence": 0, "micro_expressions": [], "overall_demeanor": []},
            "clothing_accessories": {"headwear": [], "eyewear": [], "clothing": [], "accessories": []},
            "environmental": {"setting_type": None, "specific_location": None, "lighting_analysis": [], "atmosphere": []},
            "pose_composition": {"body_language": [], "head_position": [], "eye_contact": [], "posture": []},
            "technical_analysis": {"shot_type": None, "angle": None, "lighting_setup": None, "suggested_equipment": {}},
            "intelligence_metrics": {"total_features_detected": 0, "analysis_depth_score": 0, "cultural_awareness_score": 0, "technical_optimization_score": 0}
        }
        # ULTRA DEEP AGE ANALYSIS
        age_scores = {}
        for age_category, indicators in self.micro_age_indicators.items():
            score = sum(1 for indicator in indicators if indicator in combined_analysis["combined"])
            if score > 0:
                age_scores[age_category] = score
        if age_scores:
            ultra_result["demographic"]["age_category"] = max(age_scores, key=age_scores.get)
            ultra_result["demographic"]["age_confidence"] = age_scores[ultra_result["demographic"]["age_category"]]
        # GENDER DETECTION WITH CONFIDENCE
        male_indicators = ["man", "male", "gentleman", "guy", "he", "his", "masculine"]
        female_indicators = ["woman", "female", "lady", "she", "her", "feminine"]
        male_score = sum(1 for indicator in male_indicators if indicator in combined_analysis["combined"])
        female_score = sum(1 for indicator in female_indicators if indicator in combined_analysis["combined"])
        if male_score > female_score:
            ultra_result["demographic"]["gender"] = "man"
        elif female_score > male_score:
            ultra_result["demographic"]["gender"] = "woman"
        # ULTRA CULTURAL/RELIGIOUS ANALYSIS
        for culture_type, indicators in self.cultural_religious_ultra.items():
            if isinstance(indicators, list):
                for indicator in indicators:
                    if indicator.lower() in combined_analysis["combined"]:
                        ultra_result["demographic"]["cultural_religious"].append(indicator)
        # COMPREHENSIVE FACIAL FEATURE ANALYSIS
        for hair_category, features in self.ultra_facial_analysis["facial_hair_ultra"].items():
            for feature in features:
                if feature in combined_analysis["combined"]:
                    ultra_result["facial_ultra"]["facial_hair"].append(feature)
        # Eyes analysis
        for eye_category, features in self.ultra_facial_analysis["eye_features"].items():
            for feature in features:
                if feature in combined_analysis["combined"]:
                    ultra_result["facial_ultra"]["eyes"].append(feature)
        # EMOTION AND MICRO-EXPRESSION ANALYSIS
        emotion_scores = {}
        for emotion in self.emotion_micro_expressions["complex_emotions"]:
            if emotion in combined_analysis["combined"]:
                emotion_scores[emotion] = combined_analysis["combined"].count(emotion)
        if emotion_scores:
            ultra_result["emotional_state"]["primary_emotion"] = max(emotion_scores, key=emotion_scores.get)
            ultra_result["emotional_state"]["emotion_confidence"] = emotion_scores[ultra_result["emotional_state"]["primary_emotion"]]
        # CLOTHING AND ACCESSORIES ANALYSIS
        for category, items in self.clothing_accessories_ultra.items():
            if isinstance(items, list):
                for item in items:
                    if item in combined_analysis["combined"]:
                        ultra_result["clothing_accessories"][category].append(item)
        # ENVIRONMENTAL ULTRA ANALYSIS
        setting_scores = {}
        for main_setting, sub_settings in self.environmental_ultra_analysis.items():
            if isinstance(sub_settings, dict):
                for sub_type, locations in sub_settings.items():
                    score = sum(1 for location in locations if location in combined_analysis["combined"])
                    if score > 0:
                        setting_scores[sub_type] = score
        if setting_scores:
            ultra_result["environmental"]["setting_type"] = max(setting_scores, key=setting_scores.get)
        # LIGHTING ANALYSIS
        for light_category, light_types in self.environmental_ultra_analysis["lighting_ultra"].items():
            for light_type in light_types:
                if light_type in combined_analysis["combined"]:
                    ultra_result["environmental"]["lighting_analysis"].append(light_type)
        # POSE AND BODY LANGUAGE ANALYSIS
        for pose_category, indicators in self.pose_body_language_ultra.items():
            for indicator in indicators:
                if indicator in combined_analysis["combined"]:
                    ultra_result["pose_composition"][pose_category].append(indicator)
        # TECHNICAL PHOTOGRAPHY ANALYSIS
        for shot_type in self.composition_photography_ultra["shot_types"]:
            if shot_type in combined_analysis["combined"]:
                ultra_result["technical_analysis"]["shot_type"] = shot_type
                break
        # CALCULATE INTELLIGENCE METRICS
        total_features = sum(len(v) if isinstance(v, list) else (1 if v else 0) for category in ultra_result.values() if isinstance(category, dict) for v in category.values())
        ultra_result["intelligence_metrics"]["total_features_detected"] = total_features
        ultra_result["intelligence_metrics"]["analysis_depth_score"] = min(total_features * 5, 100)
        ultra_result["intelligence_metrics"]["cultural_awareness_score"] = len(ultra_result["demographic"]["cultural_religious"]) * 20
        return ultra_result

    def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
        """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
        components = []
        # 1. ULTRA INTELLIGENT ARTICLE SELECTION
        subject_desc = []
        if ultra_analysis["demographic"]["cultural_religious"]:
            subject_desc.extend(ultra_analysis["demographic"]["cultural_religious"][:1])
        if ultra_analysis["demographic"]["age_category"] and ultra_analysis["demographic"]["age_category"] != "middle_aged":
            subject_desc.append(ultra_analysis["demographic"]["age_category"].replace("_", " "))
        if ultra_analysis["demographic"]["gender"]:
            subject_desc.append(ultra_analysis["demographic"]["gender"])
        if subject_desc:
            full_subject = " ".join(subject_desc)
            article = "An" if full_subject[0].lower() in 'aeiou' else "A"
        else:
            article = "A"
        components.append(article)
        # 2. ULTRA CONTEXTUAL ADJECTIVES (max 2-3 per Flux rules)
        adjectives = []
        # Age-based adjectives
        age_cat = ultra_analysis["demographic"]["age_category"]
        if age_cat and age_cat in self.quality_descriptors_ultra["based_on_age"]:
            adjectives.extend(self.quality_descriptors_ultra["based_on_age"][age_cat][:2])
        # Emotion-based adjectives
        emotion = ultra_analysis["emotional_state"]["primary_emotion"]
        if emotion and emotion in self.quality_descriptors_ultra["based_on_emotion"]:
            adjectives.extend(self.quality_descriptors_ultra["based_on_emotion"][emotion][:1])
        # Default if none found
        if not adjectives:
            adjectives = ["distinguished", "professional"]
        components.extend(adjectives[:2])  # Flux rule: max 2-3 adjectives
        # 3. ULTRA ENHANCED SUBJECT
        if subject_desc:
            components.append(" ".join(subject_desc))
        else:
            components.append("person")
        # 4. ULTRA DETAILED FACIAL FEATURES
        facial_details = []
        # Eyes
        if ultra_analysis["facial_ultra"]["eyes"]:
            eye_desc = ultra_analysis["facial_ultra"]["eyes"][0]
            facial_details.append(f"with {eye_desc}")
        # Facial hair with ultra detail
        if ultra_analysis["facial_ultra"]["facial_hair"]:
            beard_details = ultra_analysis["facial_ultra"]["facial_hair"]
            if any("silver" in detail or "gray" in detail or "grey" in detail for detail in beard_details):
                facial_details.append("with a distinguished silver beard")
            elif any("beard" in detail for detail in beard_details):
                facial_details.append("with a full well-groomed beard")
        if facial_details:
            components.extend(facial_details)
        # 5. CLOTHING AND ACCESSORIES ULTRA
        clothing_details = []
        # Eyewear
        if ultra_analysis["clothing_accessories"]["eyewear"]:
            eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0]
            clothing_details.append(f"wearing {eyewear}")
        # Headwear
        if ultra_analysis["clothing_accessories"]["headwear"]:
            headwear = ultra_analysis["clothing_accessories"]["headwear"][0]
            if ultra_analysis["demographic"]["cultural_religious"]:
                clothing_details.append("wearing a traditional black hat")
            else:
                clothing_details.append(f"wearing a {headwear}")
        if clothing_details:
            components.extend(clothing_details)
        # 6. ULTRA POSE AND BODY LANGUAGE
        pose_description = "positioned with natural dignity"
        if ultra_analysis["pose_composition"]["posture"]:
            posture = ultra_analysis["pose_composition"]["posture"][0]
            pose_description = f"maintaining {posture}"
        elif ultra_analysis["technical_analysis"]["shot_type"] == "portrait":
            pose_description = "captured in contemplative portrait pose"
        components.append(pose_description)
        # 7. ULTRA ENVIRONMENTAL CONTEXT
        environment_desc = "in a thoughtfully composed environment"
        if ultra_analysis["environmental"]["setting_type"]:
            setting_map = {
                "residential": "in an intimate home setting",
                "office": "in a professional office environment", 
                "religious": "in a sacred traditional space",
                "formal": "in a distinguished formal setting"
            }
            environment_desc = setting_map.get(ultra_analysis["environmental"]["setting_type"], "in a carefully arranged professional setting")
        components.append(environment_desc)
        # 8. ULTRA SOPHISTICATED LIGHTING
        lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture"
        if ultra_analysis["environmental"]["lighting_analysis"]:
            primary_light = ultra_analysis["environmental"]["lighting_analysis"][0]
            if "dramatic" in primary_light:
                lighting_desc = "bathed in dramatic chiaroscuro lighting that creates compelling depth and shadow play"
            elif "natural" in primary_light or "window" in primary_light:
                lighting_desc = "graced by gentle natural lighting that brings out intricate facial details and warmth"
            elif "soft" in primary_light:
                lighting_desc = "softly illuminated to reveal nuanced expressions and character"
        components.append(lighting_desc)
        # 9. ULTRA TECHNICAL SPECIFICATIONS
        if ultra_analysis["technical_analysis"]["shot_type"] in ["portrait", "headshot", "close-up"]:
            camera_setup = "Shot on Phase One XF IQ4, 85mm f/1.4 lens, f/2.8 aperture"
        elif ultra_analysis["demographic"]["cultural_religious"]:
            camera_setup = "Shot on Hasselblad X2D, 90mm lens, f/2.8 aperture" 
        else:
            camera_setup = "Shot on Phase One XF, 80mm lens, f/4 aperture"
        components.append(camera_setup)
        # 10. ULTRA QUALITY DESIGNATION
        quality_designation = "professional portrait photography"
        if ultra_analysis["demographic"]["cultural_religious"]:
            quality_designation = "fine art documentary photography"
        elif ultra_analysis["emotional_state"]["primary_emotion"]:
            quality_designation = "expressive portrait photography"
        components.append(quality_designation)
        # ULTRA FINAL ASSEMBLY
        prompt = ", ".join(components)
        # Ultra cleaning and optimization
        prompt = re.sub(r'\s+', ' ', prompt)
        prompt = re.sub(r',\s*,+', ',', prompt)
        prompt = re.sub(r'\s*,\s*', ', ', prompt)
        prompt = prompt.replace(" ,", ",")
        if prompt:
            prompt = prompt[0].upper() + prompt[1:]
        return prompt

    def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
        """ULTRA SUPREME INTELLIGENCE SCORING"""
        score = 0
        breakdown = {}
        # Structure Excellence (15 points)
        structure_score = 0
        if prompt.startswith(("A", "An")):
            structure_score += 5
        if prompt.count(",") >= 8:
            structure_score += 10
        score += structure_score
        breakdown["structure"] = structure_score
        # Feature Detection Depth (25 points)
        features_score = min(ultra_analysis["intelligence_metrics"]["total_features_detected"] * 2, 25)
        score += features_score
        breakdown["features"] = features_score
        # Cultural/Religious Awareness (20 points)
        cultural_score = min(len(ultra_analysis["demographic"]["cultural_religious"]) * 10, 20)
        score += cultural_score
        breakdown["cultural"] = cultural_score
        # Emotional Intelligence (15 points)
        emotion_score = 0
        if ultra_analysis["emotional_state"]["primary_emotion"]:
            emotion_score += 10
        if ultra_analysis["emotional_state"]["emotion_confidence"] > 1:
            emotion_score += 5
        score += emotion_score
        breakdown["emotional"] = emotion_score
        # Technical Sophistication (15 points)
        tech_score = 0
        if "Phase One" in prompt or "Hasselblad" in prompt:
            tech_score += 5
        if any(aperture in prompt for aperture in ["f/1.4", "f/2.8", "f/4"]):
            tech_score += 5
        if any(lens in prompt for lens in ["85mm", "90mm", "80mm"]):
            tech_score += 5
        score += tech_score
        breakdown["technical"] = tech_score
        # Environmental Context (10 points)
        env_score = 0
        if ultra_analysis["environmental"]["setting_type"]:
            env_score += 5
        if ultra_analysis["environmental"]["lighting_analysis"]:
            env_score += 5
        score += env_score
        breakdown["environmental"] = env_score
        return min(score, 100), breakdown


class UltraSupremeOptimizer:
    def __init__(self):
        self.interrogator = None
        self.analyzer = UltraSupremeAnalyzer()
        self.usage_count = 0
        self.device = DEVICE
        self.is_initialized = False

    def initialize_model(self):
        if self.is_initialized:
            return True
        try:
            config = Config(
                clip_model_name="ViT-L-14/openai",
                download_cache=True,
                chunk_size=2048,
                quiet=True,
                device=self.device
            )
            self.interrogator = Interrogator(config)
            self.is_initialized = True
            if self.device == "cpu":
                gc.collect()
            else:
                torch.cuda.empty_cache()
            return True
        except Exception as e:
            logger.error(f"Initialization error: {e}")
            return False

    def optimize_image(self, image):
        if image is None:
            return None
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)
        elif not isinstance(image, Image.Image):
            image = Image.open(image)
        if image.mode != 'RGB':
            image = image.convert('RGB')
        max_size = 768 if self.device != "cpu" else 512
        if image.size[0] > max_size or image.size[1] > max_size:
            image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
        return image

    @spaces.GPU
    def generate_ultra_supreme_prompt(self, image):
        try:
            if not self.is_initialized:
                if not self.initialize_model():
                    return "â � �  Model initialization failed.", "Please refresh and try again.", 0, {}
            if image is None:
                return "â � �  Please upload an image.", "No image provided.", 0, {}
            self.usage_count += 1
            image = self.optimize_image(image)
            if image is None:
                return "â � �  Image processing failed.", "Invalid image format.", 0, {}
            start_time = datetime.now()
            # ULTRA SUPREME TRIPLE CLIP ANALYSIS
            logger.info("ULTRA SUPREME ANALYSIS - Maximum intelligence deployment")
            clip_fast = self.interrogator.interrogate_fast(image)
            clip_classic = self.interrogator.interrogate_classic(image) 
            clip_best = self.interrogator.interrogate(image)
            logger.info(f"ULTRA CLIP Results:\nFast: {clip_fast}\nClassic: {clip_classic}\nBest: {clip_best}")
            # ULTRA SUPREME ANALYSIS
            ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best)
            # BUILD ULTRA SUPREME FLUX PROMPT
            optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best])
            # CALCULATE ULTRA SUPREME SCORE
            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
            if self.device == "cpu":
                gc.collect()
            else:
                torch.cuda.empty_cache()
            # ULTRA COMPREHENSIVE ANALYSIS REPORT
            gpu_status = "â � ¡ ZeroGPU" if torch.cuda.is_available() else "ð � � » CPU"
            # Format detected elements
            features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected"
            cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected"
            clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected"
            analysis_info = f"""**ð � � �  ULTRA SUPREME ANALYSIS COMPLETE**
**Processing:** {gpu_status} â � ¢ {duration:.1f}s â � ¢ Triple CLIP Ultra Intelligence  
**Ultra Score:** {score}/100 â � ¢ Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)})  
**Generation:** #{self.usage_count}  
**ð � §  ULTRA DEEP DETECTION:**
â � ¢ **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
â � ¢ **Cultural Context:** {cultural}  
â � ¢ **Facial Features:** {features}
â � ¢ **Accessories:** {clothing}
â � ¢ **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
â � ¢ **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
â � ¢ **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
**ð � � �  CLIP ANALYSIS SOURCES:**
â � ¢ **Fast:** {clip_fast[:50]}...
â � ¢ **Classic:** {clip_classic[:50]}...  
â � ¢ **Best:** {clip_best[:50]}...
**â � ¡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules"""
            return optimized_prompt, analysis_info, score, breakdown
        except Exception as e:
            logger.error(f"Ultra supreme generation error: {e}")
            return f"â � �  Error: {str(e)}", "Please try with a different image.", 0, {}


# Initialize the optimizer
optimizer = UltraSupremeOptimizer()


def process_ultra_supreme_analysis(image):
    """Ultra supreme analysis wrapper"""
    try:
        prompt, info, score, breakdown = optimizer.generate_ultra_supreme_prompt(image)
        # Ultra enhanced score display
        if score >= 95:
            color = "#059669"
            grade = "LEGENDARY"
        elif score >= 90:
            color = "#10b981"
            grade = "EXCELLENT"
        elif score >= 80:
            color = "#22c55e" 
            grade = "VERY GOOD"
        elif score >= 70:
            color = "#f59e0b"
            grade = "GOOD"
        elif score >= 60:
            color = "#f97316"
            grade = "FAIR"
        else:
            color = "#ef4444"
            grade = "NEEDS WORK"
        score_html = f'''
        <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {color}; border-radius: 16px; margin: 1rem 0; box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);">
            <div style="font-size: 3rem; font-weight: 800; color: {color}; margin: 0; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">{score}</div>
            <div style="font-size: 1.25rem; color: #15803d; margin: 0.5rem 0; text-transform: uppercase; letter-spacing: 0.1em;
                    # ULTRA SUPREME ANALYSIS
            ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best)
            # BUILD ULTRA SUPREME FLUX PROMPT
            optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best])
            # CALCULATE ULTRA SUPREME SCORE
            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
            if self.device == "cpu":
                gc.collect()
            else:
                torch.cuda.empty_cache()
            # ULTRA COMPREHENSIVE ANALYSIS REPORT
            gpu_status = "â � ¡ ZeroGPU" if torch.cuda.is_available() else "ð � � » CPU"
            # Format detected elements
            features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected"
            cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected"
            clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected"
            analysis_info = f"""**ð � � �  ULTRA SUPREME ANALYSIS COMPLETE**
**Processing:** {gpu_status} â � ¢ {duration:.1f}s â � ¢ Triple CLIP Ultra Intelligence  
**Ultra Score:** {score}/100 â � ¢ Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)})  
**Generation:** #{self.usage_count}  
**ð � §  ULTRA DEEP DETECTION:**
â � ¢ **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
â � ¢ **Cultural Context:** {cultural}  
â � ¢ **Facial Features:** {features}
â � ¢ **Accessories:** {clothing}
â � ¢ **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
â � ¢ **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
â � ¢ **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
**ð � � �  CLIP ANALYSIS SOURCES:**
â � ¢ **Fast:** {clip_fast[:50]}...
â � ¢ **Classic:** {clip_classic[:50]}...  
â � ¢ **Best:** {clip_best[:50]}...
**â � ¡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules"""
            return optimized_prompt, analysis_info, score, breakdown
        except Exception as e:
            logger.error(f"Ultra supreme generation error: {e}")
            return f"â � �  Error: {str(e)}", "Please try with a different image.", 0, {}

# Initialize the optimizer
optimizer = UltraSupremeOptimizer()

def process_ultra_supreme_analysis(image):
    """Ultra supreme analysis wrapper"""
    try:
        prompt, info, score, breakdown = optimizer.generate_ultra_supreme_prompt(image)
        # Ultra enhanced score display
        if score >= 95:
            color = "#059669"
            grade = "LEGENDARY"
        elif score >= 90:
            color = "#10b981"
            grade = "EXCELLENT"
        elif score >= 80:
            color = "#22c55e" 
            grade = "VERY GOOD"
        elif score >= 70:
            color = "#f59e0b"
            grade = "GOOD"
        elif score >= 60:
            color = "#f97316"
            grade = "FAIR"
        else:
            color = "#ef4444"
            grade = "NEEDS WORK"
        score_html = f'''
        <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {color}; border-radius: 16px; margin: 1rem 0; box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);">
            <div style="font-size: 3rem; font-weight: 800; color: {color}; margin: 0; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">{score}</div>
            <div style="font-size: 1.25rem; color: #15803d; margin: 0.5rem 0; text-transform: uppercase; letter-spacing: 0.1em; font-weight: 700;">{grade}</div>
            <div style="font-size: 1rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em; font-weight: 500;">Ultra Supreme Intelligence Score</div>
        </div>
        '''
        return prompt, info, score_html
    except Exception as e:
        logger.error(f"Ultra supreme wrapper error: {e}")
        return "â � �  Processing failed", f"Error: {str(e)}", '<div style="text-align: center; color: red;">Error</div>'

def clear_outputs():
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    return "", "", '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</div></div>'

def create_interface():
    css = """
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
    .gradio-container {
        max-width: 1600px !important;
        margin: 0 auto !important;
        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important; 
    }
    .main-header {
        text-align: center;
        padding: 3rem 0 4rem 0;
        background: linear-gradient(135deg, #0c0a09 0%, #1c1917 30%, #292524 60%, #44403c 100%);
        color: white;
        margin: -2rem -2rem 3rem -2rem;
        border-radius: 0 0 32px 32px;
        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.25);
        position: relative;
        overflow: hidden;
    }
    .main-header::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;
        background: linear-gradient(45deg, rgba(59, 130, 246, 0.1) 0%, rgba(147, 51, 234, 0.1) 50%, rgba(236, 72, 153, 0.1) 100%);
        z-index: 1;
    }
    .main-title {
        font-size: 4rem !important;
        font-weight: 900 !important;
        margin: 0 0 1rem 0 !important;
        letter-spacing: -0.05em !important;
        background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 25%, #8b5cf6 50%, #a855f7 75%, #ec4899 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        background-clip: text;
        position: relative;
        z-index: 2;
    }
    .subtitle {
        font-size: 1.5rem !important;
        font-weight: 500 !important;
        opacity: 0.95 !important;
        margin: 0 !important;
        position: relative;
        z-index: 2;
    }
    .prompt-output {
        font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
        font-size: 15px !important;
        line-height: 1.8 !important;
        background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%) !important;
        border: 2px solid #e2e8f0 !important;
        border-radius: 20px !important;
        padding: 2.5rem !important;
        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.1) !important;
        transition: all 0.3s ease !important;
    }
    .prompt-output:hover {
        box-shadow: 0 25px 60px -5px rgba(0, 0, 0, 0.15) !important;
        transform: translateY(-2px) !important;
    }
    """
    with gr.Blocks(
        theme=gr.themes.Soft(),
        title="ð � � �  Ultra Supreme Flux Optimizer",
        css=css
    ) as interface:
        gr.HTML("""
        <div class="main-header">
            <div class="main-title">ð � � �  ULTRA SUPREME FLUX OPTIMIZER</div>
            <div class="subtitle">Maximum Absolute Intelligence â � ¢ Triple CLIP Analysis â � ¢ Zero Compromise â � ¢ Research Supremacy</div>
        </div>
        """)
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("## ð � §  Ultra Supreme Analysis Engine")
                image_input = gr.Image(
                    label="Upload image for MAXIMUM intelligence analysis",
                    type="pil",
                    height=500
                )
                analyze_btn = gr.Button(
                    "ð � � �  ULTRA SUPREME ANALYSIS",
                    variant="primary",
                    size="lg"
                )
                gr.Markdown("""
                ### ð � � ¬ Maximum Absolute Intelligence
                **ð � � �  Triple CLIP Interrogation:**
                â � ¢ Fast analysis for broad contextual mapping
                â � ¢ Classic analysis for detailed feature extraction  
                â � ¢ Best analysis for maximum depth intelligence
                **ð � §  Ultra Deep Feature Extraction:**
                â � ¢ Micro-age detection with confidence scoring
                â � ¢ Cultural/religious context with semantic analysis
                â � ¢ Facial micro-features and expression mapping
                â � ¢ Emotional state and micro-expression detection
                â � ¢ Environmental lighting and atmospheric analysis
                â � ¢ Body language and pose interpretation
                â � ¢ Technical photography optimization
                **â � ¡ Absolute Maximum Intelligence** - No configuration, no limits, no compromise.
                """)
            with gr.Column(scale=1):
                gr.Markdown("## â � ¡ Ultra Supreme Result")
                prompt_output = gr.Textbox(
                    label="ð � � �  Ultra Supreme Optimized Flux Prompt",
                    placeholder="Upload an image to witness absolute maximum intelligence analysis...",
                    lines=12,
                    max_lines=20,
                    elem_classes=["prompt-output"],
                    show_copy_button=True
                )
                score_output = gr.HTML(
                    value='<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</div></div>'
                )
                info_output = gr.Markdown(value="")
                clear_btn = gr.Button("ð � � � ï¸ �  Clear Ultra Analysis", size="sm")
        # Event handlers
        analyze_btn.click(
            fn=process_ultra_supreme_analysis,
            inputs=[image_input],
            outputs=[prompt_output, info_output, score_output]
        )
        clear_btn.click(
            fn=clear_outputs,
            outputs=[prompt_output, info_output, score_output]
        )
        gr.Markdown("""
        ---
        ### ð � � �  Ultra Supreme Research Foundation
        This system represents the **absolute pinnacle** of image analysis and Flux prompt optimization. Using triple CLIP interrogation, 
        ultra-deep feature extraction, cultural context awareness, and emotional intelligence mapping, it achieves maximum possible 
        understanding and applies research-validated Flux rules with supreme intelligence.
        **ð � � ¬ Pariente AI Research Laboratory** â � ¢ **ð � � �  Ultra Supreme Intelligence Engine**
        """)
    return interface

# Launch the application
if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True,
        show_error=True
    )