Update app.py

app.py

@@ -1,664 +1,49 @@
-import spaces
+"""
+Ultra Supreme Flux Optimizer - Main Gradio Interface
+"""
+
 import gradio as gr
 import torch
-from PIL import Image
-import numpy as np
-from clip_interrogator import Config, Interrogator
+import gc
 import logging
-import os
 import warnings
-from datetime import datetime
-import gc
-import re
-import math
+import os
+
+from optimizer import UltraSupremeOptimizer
+from constants import SCORE_GRADES
 
+# Configure warnings and environment
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+# Configure logging
 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]"]
-        
-        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_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"]:
-                        if category == "clothing_types":
-                            ultra_result["clothing_accessories"]["clothing"].append(item)
-                        elif category == "clothing_styles":
-                            ultra_result["clothing_accessories"]["clothing"].append(item)
-                        elif category in ["headwear", "eyewear", "accessories"]:
-                            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"]:
-                    if pose_category in ultra_result["pose_composition"]:
-                        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 "❌ Model initialization failed.", "Please refresh and try again.", 0, {}
-            
-            if image is None:
-                return "❌ Please upload an image.", "No image provided.", 0, {}
-            
-            self.usage_count += 1
-            
-            image = self.optimize_image(image)
-            if image is None:
-                return "❌ 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 = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 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"""**🚀 ULTRA SUPREME ANALYSIS COMPLETE**
-**Processing:** {gpu_status} • {duration:.1f}s • Triple CLIP Ultra Intelligence  
-**Ultra Score:** {score}/100 • 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}  
-**🧠 ULTRA DEEP DETECTION:**
-- **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
-- **Cultural Context:** {cultural}  
-- **Facial Features:** {features}
-- **Accessories:** {clothing}
-- **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
-- **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
-- **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
-**📊 CLIP ANALYSIS SOURCES:**
-- **Fast:** {clip_fast[:50]}...
-- **Classic:** {clip_classic[:50]}...  
-- **Best:** {clip_best[:50]}...
-**⚡ 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"❌ Error: {str(e)}", "Please try with a different image.", 0, {}
-
-# Initialize the optimizer
+# Initialize the optimizer globally
 optimizer = UltraSupremeOptimizer()
 
+
 def process_ultra_supreme_analysis(image):
-    """Ultra supreme analysis wrapper"""
+    """Process image and generate ultra supreme analysis"""
     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"
+        # Find appropriate grade based on score
+        grade_info = None
+        for threshold, grade_data in sorted(SCORE_GRADES.items(), reverse=True):
+            if score >= threshold:
+                grade_info = grade_data
+                break
+        
+        if not grade_info:
+            grade_info = SCORE_GRADES[0]  # Default to lowest grade
             
         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="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {grade_info["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: {grade_info["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_info["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>
         '''
@@ -669,13 +54,18 @@ def process_ultra_supreme_analysis(image):
         logger.error(f"Ultra supreme wrapper error: {e}")
         return "❌ Processing failed", f"Error: {str(e)}", '<div style="text-align: center; color: red;">Error</div>'
 
+
 def clear_outputs():
+    """Clear all outputs and free memory"""
     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():  # <-- Esta línea debe empezar en la columna 0, sin espacios al inicio
+
+def create_interface():
+    """Create the Gradio interface"""
+    
     css = """
     @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
     
@@ -749,19 +139,34 @@ def create_interface():  # <-- Esta línea debe empezar en la columna 0, sin esp
         transform: translateY(-2px) !important;
     }
     
-    /* Fix para texto en el footer que aparece blanco sobre blanco */
+    /* Fix for footer text visibility */
     footer, .footer, [class*="footer"] {
         color: #374151 !important;
     }
     
-    /* Asegurar que todo el texto del footer sea visible */
     footer *, .footer *, [class*="footer"] * {
         color: #374151 !important;
     }
     
-    /* Fix específico para links en el footer */
     footer a, .footer a, [class*="footer"] a {
         color: #3b82f6 !important;
+        text-decoration: underline;
+    }
+    
+    footer a:hover, .footer a:hover, [class*="footer"] a:hover {
+        color: #2563eb !important;
+    }
+    
+    /* Additional style fixes for Gradio components */
+    .gr-button-primary {
+        background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%) !important;
+        border: none !important;
+    }
+    
+    .gr-button-primary:hover {
+        background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%) !important;
+        transform: translateY(-1px);
+        box-shadow: 0 4px 12px rgba(37, 99, 235, 0.3);
     }
     """
     
@@ -855,11 +260,12 @@ def create_interface():  # <-- Esta línea debe empezar en la columna 0, sin esp
         understanding and applies research-validated Flux rules with supreme intelligence.
         
         **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme Intelligence Engine**
-        """, elem_classes=["footer-content"])
+        """)
     
     return interface
 
-# Launch the application
+
+# Main execution
 if __name__ == "__main__":
     demo = create_interface()
     demo.launch(