Update app.py

app.py

@@ -21,36 +21,34 @@ logger = logging.getLogger(__name__)
 
 def get_device():
     if torch.cuda.is_available():
-    return "cuda"
+        return "cuda"
     elif torch.backends.mps.is_available():
         return "mps"
-        else:
-            return "cpu"
+    else:
+        return "cpu"
 
-            DEVICE = get_device()
+DEVICE = get_device()
 
-            class UltraSupremeAnalyzer:
-                """
-                ULTRA SUPREME ANALYSIS ENGINE - ABSOLUTE MAXIMUM INTELLIGENCE
-                """
+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": {
+    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"],
@@ -59,516 +57,513 @@ def get_device():
             "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"]
-}
-}
+                "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}
-}
-        
+    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
+        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"]]
+        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_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"])
+        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"
+        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)
+        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)
+        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)
+        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)
+        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"]]
+        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)
+        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
+        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)
+        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)
+        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)
+        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
+        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
+        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"""
+        return ultra_result
+        def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
+        """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
         
-components = []
+        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)
+        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 = []
+        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])
+        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])
+        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"]
+        if not adjectives:
+            adjectives = ["distinguished", "professional"]
         
-components.extend(adjectives[:2])  # Flux rule: max 2-3 adjectives
+        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")
+        if subject_desc:
+            components.append(" ".join(subject_desc))
+        else:
+            components.append("person")
         
         # 4. ULTRA DETAILED FACIAL FEATURES
-facial_details = []
+        facial_details = []
         
         # Eyes
-if ultra_analysis["facial_ultra"]["eyes"]:
-eye_desc = ultra_analysis["facial_ultra"]["eyes"][0]
-facial_details.append(f"with {eye_desc}")
+        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 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)
+        if facial_details:
+            components.extend(facial_details)
         
         # 5. CLOTHING AND ACCESSORIES ULTRA
-clothing_details = []
+        clothing_details = []
         
         # Eyewear
-if ultra_analysis["clothing_accessories"]["eyewear"]:
-eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0]
-clothing_details.append(f"wearing {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 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)
+        if clothing_details:
+            components.extend(clothing_details)
         
         # 6. ULTRA POSE AND BODY LANGUAGE
-pose_description = "positioned with natural dignity"
+        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"
+        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)
+        components.append(pose_description)
         
         # 7. ULTRA ENVIRONMENTAL CONTEXT
-environment_desc = "in a thoughtfully composed environment"
+        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")
+        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)
+        components.append(environment_desc)
         
         # 8. ULTRA SOPHISTICATED LIGHTING
-lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture"
+        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"
+        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)
+        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"
+        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)
+        components.append(camera_setup)
         
         # 10. ULTRA QUALITY DESIGNATION
-quality_designation = "professional portrait photography"
+        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"
+        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)
+        components.append(quality_designation)
         
         # ULTRA FINAL ASSEMBLY
-prompt = ", ".join(components)
+        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(" ,", ",")
+        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:]
+        if prompt:
+            prompt = prompt[0].upper() + prompt[1:]
         
-return prompt
-    
-def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
-"""ULTRA SUPREME INTELLIGENCE SCORING"""
+        return prompt
+        def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
+        """ULTRA SUPREME INTELLIGENCE SCORING"""
         
-score = 0
-breakdown = {}
+        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
+        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
+        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
+        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
+        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
+        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
+        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 __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
-)
+    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
+            self.interrogator = Interrogator(config)
+            self.is_initialized = True
             
-if self.device == "cpu":
-gc.collect()
-else:
-torch.cuda.empty_cache()
+            if self.device == "cpu":
+                gc.collect()
+            else:
+                torch.cuda.empty_cache()
                 
-return True
+            return True
             
-except Exception as e:
-logger.error(f"Initialization error: {e}")
-return False
+        except Exception as e:
+            logger.error(f"Initialization error: {e}")
+            return False
 
-def optimize_image(self, image):
-if image is None:
-return None
+    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 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')
+        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)
+        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
+        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 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, {}
+                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, {}
+                return "❌ Image processing failed.", "Invalid image format.", 0, {}
             
             start_time = datetime.now()
             
@@ -595,264 +590,263 @@ elif not isinstance(image, Image.Image):
             
             # Memory cleanup
             if self.device == "cpu":
-            gc.collect()
+                gc.collect()
             else:
                 torch.cuda.empty_cache()
             
             # ULTRA COMPREHENSIVE ANALYSIS REPORT
-                gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
+            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"
+            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"""
+            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
+            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, {}
+        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
-                    optimizer = UltraSupremeOptimizer()
-
-                    def process_ultra_supreme_analysis(image):
-                        """Ultra supreme analysis wrapper"""
-                        try:
-                            prompt, info, score, breakdown = optimizer.generate_ultra_supreme_prompt(image)
+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"
+        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 "❌ Processing failed", f"Error: {str(e)}", '<div style="text-align: center; color: red;">Error</div>'
+        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 "❌ 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 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');
+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;
-                                                        }
+    .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 {
+        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-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;
-                                            }
+    .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;
-                                        }
+    .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 {
+        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;
-                                }
-                                """
+    .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="🚀 Ultra Supreme Flux Optimizer",
-                                css=css
-                            ) as interface:
-        
-                                gr.HTML("""
-                                <div class="main-header">
-                                <div class="main-title">🚀 ULTRA SUPREME FLUX OPTIMIZER</div>
-                                <div class="subtitle">Maximum Absolute Intelligence • Triple CLIP Analysis • Zero Compromise • Research Supremacy</div>
-                                </div>
-                                """)
-        
-                                with gr.Row():
-                                    with gr.Column(scale=1):
-                                        gr.Markdown("## 🧠 Ultra Supreme Analysis Engine")
+    with gr.Blocks(
+        theme=gr.themes.Soft(),
+        title="🚀 Ultra Supreme Flux Optimizer",
+        css=css
+    ) as interface:
+        
+        gr.HTML("""
+        <div class="main-header">
+            <div class="main-title">🚀 ULTRA SUPREME FLUX OPTIMIZER</div>
+            <div class="subtitle">Maximum Absolute Intelligence • Triple CLIP Analysis • Zero Compromise • Research Supremacy</div>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("## 🧠 Ultra Supreme Analysis Engine")
                 
-                                        image_input = gr.Image(
-                                        label="Upload image for MAXIMUM intelligence analysis",
-                                        type="pil",
-                                        height=500
-                                    )
+                image_input = gr.Image(
+                    label="Upload image for MAXIMUM intelligence analysis",
+                    type="pil",
+                    height=500
+                )
                 
-                                    analyze_btn = gr.Button(
-                                    "🚀 ULTRA SUPREME ANALYSIS",
-                                    variant="primary",
-                                    size="lg"
-                                )
+                analyze_btn = gr.Button(
+                    "🚀 ULTRA SUPREME ANALYSIS",
+                    variant="primary",
+                    size="lg"
+                )
                 
-                                gr.Markdown("""
+                gr.Markdown("""
                 ### 🔬 Maximum Absolute Intelligence
                 
-                                **🚀 Triple CLIP Interrogation:**
-                                • Fast analysis for broad contextual mapping
-                                • Classic analysis for detailed feature extraction  
-                                • Best analysis for maximum depth intelligence
+                **🚀 Triple CLIP Interrogation:**
+                • Fast analysis for broad contextual mapping
+                • Classic analysis for detailed feature extraction  
+                • Best analysis for maximum depth intelligence
                 
-                                **🧠 Ultra Deep Feature Extraction:**
-                                • Micro-age detection with confidence scoring
-                                • Cultural/religious context with semantic analysis
-                                • Facial micro-features and expression mapping
-                                • Emotional state and micro-expression detection
-                                • Environmental lighting and atmospheric analysis
-                                • Body language and pose interpretation
-                                • Technical photography optimization
+                **🧠 Ultra Deep Feature Extraction:**
+                • Micro-age detection with confidence scoring
+                • Cultural/religious context with semantic analysis
+                • Facial micro-features and expression mapping
+                • Emotional state and micro-expression detection
+                • Environmental lighting and atmospheric analysis
+                • Body language and pose interpretation
+                • Technical photography optimization
                 
-                                **⚡ Absolute Maximum Intelligence** - No configuration, no limits, no compromise.
-                                """)
+                **⚡ Absolute Maximum Intelligence** - No configuration, no limits, no compromise.
+                """)
             
-                                with gr.Column(scale=1):
-                                    gr.Markdown("## ⚡ Ultra Supreme Result")
+            with gr.Column(scale=1):
+                gr.Markdown("## ⚡ Ultra Supreme Result")
                 
-                                    prompt_output = gr.Textbox(
-                                    label="🚀 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
-                                )
+                prompt_output = gr.Textbox(
+                    label="🚀 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>'
-                            )
+                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="")
+                info_output = gr.Markdown(value="")
                 
-                            clear_btn = gr.Button("🗑️ Clear Ultra Analysis", size="sm")
+                clear_btn = gr.Button("🗑️ 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("""
-                    ---
+        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("""
+        ---
         ### 🏆 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.
+        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.
         
-                    **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme Intelligence Engine**
-                    """)
+        **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme Intelligence Engine**
+        """)
     
-                    return interface
+    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
-                )
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
+    )