Update app.py

app.py

@@ -15,7 +15,6 @@ import math
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -33,12 +32,9 @@ 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"],
@@ -48,7 +44,6 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -71,13 +66,11 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -89,7 +82,6 @@ class UltraSupremeAnalyzer:
                 "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"],
@@ -98,7 +90,6 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -119,7 +110,6 @@ class UltraSupremeAnalyzer:
                 "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"],
@@ -128,7 +118,6 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -136,7 +125,6 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -151,7 +139,6 @@ class UltraSupremeAnalyzer:
             "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"],
@@ -170,16 +157,15 @@ class UltraSupremeAnalyzer:
                 "artistic": ["creative", "expressive", "aesthetic", "artistic"]
             }
         }
-            def ultra_supreme_analysis(self, clip_fast, clip_classic, clip_best):
-            """ULTRA SUPREME ANALYSIS - MAXIMUM POSSIBLE INTELLIGENCE"""
-        
+
+    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": []},
@@ -190,66 +176,54 @@ class UltraSupremeAnalyzer:
             "technical_analysis": {"shot_type": None, "angle": None, "lighting_setup": None, "suggested_equipment": {}},
             "intelligence_metrics": {"total_features_detected": 0, "analysis_depth_score": 0, "cultural_awareness_score": 0, "technical_optimization_score": 0}
         }
-        
         # ULTRA DEEP AGE ANALYSIS
         age_scores = {}
         for age_category, indicators in self.micro_age_indicators.items():
             score = sum(1 for indicator in indicators if indicator in combined_analysis["combined"])
             if score > 0:
                 age_scores[age_category] = score
-        
         if age_scores:
             ultra_result["demographic"]["age_category"] = max(age_scores, key=age_scores.get)
             ultra_result["demographic"]["age_confidence"] = age_scores[ultra_result["demographic"]["age_category"]]
-        
         # GENDER DETECTION WITH CONFIDENCE
         male_indicators = ["man", "male", "gentleman", "guy", "he", "his", "masculine"]
         female_indicators = ["woman", "female", "lady", "she", "her", "feminine"]
-        
         male_score = sum(1 for indicator in male_indicators if indicator in combined_analysis["combined"])
         female_score = sum(1 for indicator in female_indicators if indicator in combined_analysis["combined"])
-        
         if male_score > female_score:
             ultra_result["demographic"]["gender"] = "man"
         elif female_score > male_score:
             ultra_result["demographic"]["gender"] = "woman"
-        
         # ULTRA CULTURAL/RELIGIOUS ANALYSIS
         for culture_type, indicators in self.cultural_religious_ultra.items():
             if isinstance(indicators, list):
                 for indicator in indicators:
                     if indicator.lower() in combined_analysis["combined"]:
                         ultra_result["demographic"]["cultural_religious"].append(indicator)
-        
         # COMPREHENSIVE FACIAL FEATURE ANALYSIS
         for hair_category, features in self.ultra_facial_analysis["facial_hair_ultra"].items():
             for feature in features:
                 if feature in combined_analysis["combined"]:
                     ultra_result["facial_ultra"]["facial_hair"].append(feature)
-        
         # Eyes analysis
         for eye_category, features in self.ultra_facial_analysis["eye_features"].items():
             for feature in features:
                 if feature in combined_analysis["combined"]:
                     ultra_result["facial_ultra"]["eyes"].append(feature)
-        
         # EMOTION AND MICRO-EXPRESSION ANALYSIS
         emotion_scores = {}
         for emotion in self.emotion_micro_expressions["complex_emotions"]:
             if emotion in combined_analysis["combined"]:
                 emotion_scores[emotion] = combined_analysis["combined"].count(emotion)
-        
         if emotion_scores:
             ultra_result["emotional_state"]["primary_emotion"] = max(emotion_scores, key=emotion_scores.get)
             ultra_result["emotional_state"]["emotion_confidence"] = emotion_scores[ultra_result["emotional_state"]["primary_emotion"]]
-        
         # CLOTHING AND ACCESSORIES ANALYSIS
         for category, items in self.clothing_accessories_ultra.items():
             if isinstance(items, list):
                 for item in items:
                     if item in combined_analysis["combined"]:
                         ultra_result["clothing_accessories"][category].append(item)
-        
         # ENVIRONMENTAL ULTRA ANALYSIS
         setting_scores = {}
         for main_setting, sub_settings in self.environmental_ultra_analysis.items():
@@ -258,41 +232,33 @@ class UltraSupremeAnalyzer:
                     score = sum(1 for location in locations if location in combined_analysis["combined"])
                     if score > 0:
                         setting_scores[sub_type] = score
-        
         if setting_scores:
             ultra_result["environmental"]["setting_type"] = max(setting_scores, key=setting_scores.get)
-        
         # LIGHTING ANALYSIS
         for light_category, light_types in self.environmental_ultra_analysis["lighting_ultra"].items():
             for light_type in light_types:
                 if light_type in combined_analysis["combined"]:
                     ultra_result["environmental"]["lighting_analysis"].append(light_type)
-        
         # POSE AND BODY LANGUAGE ANALYSIS
         for pose_category, indicators in self.pose_body_language_ultra.items():
             for indicator in indicators:
                 if indicator in combined_analysis["combined"]:
                     ultra_result["pose_composition"][pose_category].append(indicator)
-        
         # TECHNICAL PHOTOGRAPHY ANALYSIS
         for shot_type in self.composition_photography_ultra["shot_types"]:
             if shot_type in combined_analysis["combined"]:
                 ultra_result["technical_analysis"]["shot_type"] = shot_type
                 break
-        
         # CALCULATE INTELLIGENCE METRICS
         total_features = sum(len(v) if isinstance(v, list) else (1 if v else 0) for category in ultra_result.values() if isinstance(category, dict) for v in category.values())
         ultra_result["intelligence_metrics"]["total_features_detected"] = total_features
         ultra_result["intelligence_metrics"]["analysis_depth_score"] = min(total_features * 5, 100)
         ultra_result["intelligence_metrics"]["cultural_awareness_score"] = len(ultra_result["demographic"]["cultural_religious"]) * 20
-        
         return ultra_result
-    
+
     def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
         """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
-        
         components = []
-        
         # 1. ULTRA INTELLIGENT ARTICLE SELECTION
         subject_desc = []
         if ultra_analysis["demographic"]["cultural_religious"]:
@@ -301,47 +267,37 @@ class UltraSupremeAnalyzer:
             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"]
@@ -349,18 +305,14 @@ class UltraSupremeAnalyzer:
                 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]
@@ -368,24 +320,18 @@ class UltraSupremeAnalyzer:
                 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",
@@ -394,12 +340,9 @@ class UltraSupremeAnalyzer:
                 "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:
@@ -408,9 +351,7 @@ class UltraSupremeAnalyzer:
                 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"
@@ -418,39 +359,29 @@ class UltraSupremeAnalyzer:
             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")):
@@ -459,17 +390,14 @@ class UltraSupremeAnalyzer:
             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"]:
@@ -478,7 +406,6 @@ class UltraSupremeAnalyzer:
             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:
@@ -489,7 +416,6 @@ class UltraSupremeAnalyzer:
             tech_score += 5
         score += tech_score
         breakdown["technical"] = tech_score
-        
         # Environmental Context (10 points)
         env_score = 0
         if ultra_analysis["environmental"]["setting_type"]:
@@ -498,20 +424,20 @@ class UltraSupremeAnalyzer:
             env_score += 5
         score += env_score
         breakdown["environmental"] = env_score
-        
         return min(score, 100), breakdown
-        class UltraSupremeOptimizer:
+
+
+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",
@@ -520,115 +446,163 @@ class UltraSupremeAnalyzer:
                 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, {}
-            
+                    return "â � �  Model initialization failed.", "Please refresh and try again.", 0, {}
             if image is None:
-                return "❌ Please upload an image.", "No image provided.", 0, {}
-            
+                return "â � �  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 "â � �  Image processing failed.", "Invalid image format.", 0, {}
             start_time = datetime.now()
-            
             # ULTRA SUPREME TRIPLE CLIP ANALYSIS
             logger.info("ULTRA SUPREME ANALYSIS - Maximum intelligence deployment")
-            
             clip_fast = self.interrogator.interrogate_fast(image)
             clip_classic = self.interrogator.interrogate_classic(image) 
             clip_best = self.interrogator.interrogate(image)
-            
             logger.info(f"ULTRA CLIP Results:\nFast: {clip_fast}\nClassic: {clip_classic}\nBest: {clip_best}")
-            
             # ULTRA SUPREME ANALYSIS
             ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best)
-            
             # BUILD ULTRA SUPREME FLUX PROMPT
             optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best])
-            
             # CALCULATE ULTRA SUPREME SCORE
             score, breakdown = self.analyzer.calculate_ultra_supreme_score(optimized_prompt, ultra_analysis)
-            
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
-            
             # Memory cleanup
             if self.device == "cpu":
                 gc.collect()
             else:
                 torch.cuda.empty_cache()
-            
             # ULTRA COMPREHENSIVE ANALYSIS REPORT
-            gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
-            
+            gpu_status = "â � ¡ ZeroGPU" if torch.cuda.is_available() else "ð � � » CPU"
+            # Format detected elements
+            features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected"
+            cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected"
+            clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected"
+            analysis_info = f"""**ð � � �  ULTRA SUPREME ANALYSIS COMPLETE**
+**Processing:** {gpu_status} â � ¢ {duration:.1f}s â � ¢ Triple CLIP Ultra Intelligence  
+**Ultra Score:** {score}/100 â � ¢ Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)})  
+**Generation:** #{self.usage_count}  
+**ð � §  ULTRA DEEP DETECTION:**
+â � ¢ **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
+â � ¢ **Cultural Context:** {cultural}  
+â � ¢ **Facial Features:** {features}
+â � ¢ **Accessories:** {clothing}
+â � ¢ **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
+â � ¢ **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
+â � ¢ **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
+**ð � � �  CLIP ANALYSIS SOURCES:**
+â � ¢ **Fast:** {clip_fast[:50]}...
+â � ¢ **Classic:** {clip_classic[:50]}...  
+â � ¢ **Best:** {clip_best[:50]}...
+**â � ¡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules"""
+            return optimized_prompt, analysis_info, score, breakdown
+        except Exception as e:
+            logger.error(f"Ultra supreme generation error: {e}")
+            return f"â � �  Error: {str(e)}", "Please try with a different image.", 0, {}
+
+
+# Initialize the optimizer
+optimizer = UltraSupremeOptimizer()
+
+
+def process_ultra_supreme_analysis(image):
+    """Ultra supreme analysis wrapper"""
+    try:
+        prompt, info, score, breakdown = optimizer.generate_ultra_supreme_prompt(image)
+        # Ultra enhanced score display
+        if score >= 95:
+            color = "#059669"
+            grade = "LEGENDARY"
+        elif score >= 90:
+            color = "#10b981"
+            grade = "EXCELLENT"
+        elif score >= 80:
+            color = "#22c55e" 
+            grade = "VERY GOOD"
+        elif score >= 70:
+            color = "#f59e0b"
+            grade = "GOOD"
+        elif score >= 60:
+            color = "#f97316"
+            grade = "FAIR"
+        else:
+            color = "#ef4444"
+            grade = "NEEDS WORK"
+        score_html = f'''
+        <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {color}; border-radius: 16px; margin: 1rem 0; box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);">
+            <div style="font-size: 3rem; font-weight: 800; color: {color}; margin: 0; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">{score}</div>
+            <div style="font-size: 1.25rem; color: #15803d; margin: 0.5rem 0; text-transform: uppercase; letter-spacing: 0.1em;
+                    # ULTRA SUPREME ANALYSIS
+            ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best)
+            # BUILD ULTRA SUPREME FLUX PROMPT
+            optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best])
+            # CALCULATE ULTRA SUPREME SCORE
+            score, breakdown = self.analyzer.calculate_ultra_supreme_score(optimized_prompt, ultra_analysis)
+            end_time = datetime.now()
+            duration = (end_time - start_time).total_seconds()
+            # Memory cleanup
+            if self.device == "cpu":
+                gc.collect()
+            else:
+                torch.cuda.empty_cache()
+            # ULTRA COMPREHENSIVE ANALYSIS REPORT
+            gpu_status = "â � ¡ ZeroGPU" if torch.cuda.is_available() else "ð � � » CPU"
             # Format detected elements
             features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected"
             cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected"
             clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected"
-            
-            analysis_info = f"""**🚀 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)})  
+            analysis_info = f"""**ð � � �  ULTRA SUPREME ANALYSIS COMPLETE**
+**Processing:** {gpu_status} â � ¢ {duration:.1f}s â � ¢ Triple CLIP Ultra Intelligence  
+**Ultra Score:** {score}/100 â � ¢ Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)})  
 **Generation:** #{self.usage_count}  
-**🧠 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"""
-            
+**ð � §  ULTRA DEEP DETECTION:**
+â � ¢ **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
+â � ¢ **Cultural Context:** {cultural}  
+â � ¢ **Facial Features:** {features}
+â � ¢ **Accessories:** {clothing}
+â � ¢ **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
+â � ¢ **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
+â � ¢ **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
+**ð � � �  CLIP ANALYSIS SOURCES:**
+â � ¢ **Fast:** {clip_fast[:50]}...
+â � ¢ **Classic:** {clip_classic[:50]}...  
+â � ¢ **Best:** {clip_best[:50]}...
+**â � ¡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules"""
             return optimized_prompt, analysis_info, score, breakdown
-            
         except Exception as e:
             logger.error(f"Ultra supreme generation error: {e}")
-            return f"❌ Error: {str(e)}", "Please try with a different image.", 0, {}
+            return f"â � �  Error: {str(e)}", "Please try with a different image.", 0, {}
 
 # Initialize the optimizer
 optimizer = UltraSupremeOptimizer()
@@ -637,7 +611,6 @@ 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"
@@ -657,7 +630,6 @@ def process_ultra_supreme_analysis(image):
         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>
@@ -665,12 +637,10 @@ def process_ultra_supreme_analysis(image):
             <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>'
+        return "â � �  Processing failed", f"Error: {str(e)}", '<div style="text-align: center; color: red;">Error</div>'
 
 def clear_outputs():
     gc.collect()
@@ -681,14 +651,12 @@ def clear_outputs():
 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;
+        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important; 
     }
-    
     .main-header {
         text-align: center;
         padding: 3rem 0 4rem 0;
@@ -700,7 +668,6 @@ def create_interface():
         position: relative;
         overflow: hidden;
     }
-    
     .main-header::before {
         content: '';
         position: absolute;
@@ -711,7 +678,6 @@ def create_interface():
         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;
@@ -724,7 +690,6 @@ def create_interface():
         position: relative;
         z-index: 2;
     }
-    
     .subtitle {
         font-size: 1.5rem !important;
         font-weight: 500 !important;
@@ -733,7 +698,6 @@ def create_interface():
         position: relative;
         z-index: 2;
     }
-    
     .prompt-output {
         font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
         font-size: 15px !important;
@@ -745,105 +709,84 @@ def create_interface():
         box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.1) !important;
         transition: all 0.3s ease !important;
     }
-    
     .prompt-output:hover {
         box-shadow: 0 25px 60px -5px rgba(0, 0, 0, 0.15) !important;
         transform: translateY(-2px) !important;
     }
     """
-    
     with gr.Blocks(
         theme=gr.themes.Soft(),
-        title="🚀 Ultra Supreme Flux Optimizer",
+        title="ð � � �  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 class="main-title">ð � � �  ULTRA SUPREME FLUX OPTIMIZER</div>
+            <div class="subtitle">Maximum Absolute Intelligence â � ¢ Triple CLIP Analysis â � ¢ Zero Compromise â � ¢ Research Supremacy</div>
         </div>
         """)
-        
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown("## 🧠 Ultra Supreme Analysis Engine")
-                
+                gr.Markdown("## ð � §  Ultra Supreme Analysis Engine")
                 image_input = gr.Image(
                     label="Upload image for MAXIMUM intelligence analysis",
                     type="pil",
                     height=500
                 )
-                
                 analyze_btn = gr.Button(
-                    "🚀 ULTRA SUPREME ANALYSIS",
+                    "ð � � �  ULTRA SUPREME ANALYSIS",
                     variant="primary",
                     size="lg"
                 )
-                
                 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
-                
-                **🧠 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.
+                ### ð � � ¬ Maximum Absolute Intelligence
+                **ð � � �  Triple CLIP Interrogation:**
+                â � ¢ Fast analysis for broad contextual mapping
+                â � ¢ Classic analysis for detailed feature extraction  
+                â � ¢ Best analysis for maximum depth intelligence
+                **ð � §  Ultra Deep Feature Extraction:**
+                â � ¢ Micro-age detection with confidence scoring
+                â � ¢ Cultural/religious context with semantic analysis
+                â � ¢ Facial micro-features and expression mapping
+                â � ¢ Emotional state and micro-expression detection
+                â � ¢ Environmental lighting and atmospheric analysis
+                â � ¢ Body language and pose interpretation
+                â � ¢ Technical photography optimization
+                **â � ¡ Absolute Maximum Intelligence** - No configuration, no limits, no compromise.
                 """)
-            
             with gr.Column(scale=1):
-                gr.Markdown("## ⚡ Ultra Supreme Result")
-                
+                gr.Markdown("## â � ¡ Ultra Supreme Result")
                 prompt_output = gr.Textbox(
-                    label="🚀 Ultra Supreme Optimized Flux Prompt",
+                    label="ð � � �  Ultra Supreme Optimized Flux Prompt",
                     placeholder="Upload an image to witness absolute maximum intelligence analysis...",
                     lines=12,
                     max_lines=20,
                     elem_classes=["prompt-output"],
                     show_copy_button=True
                 )
-                
                 score_output = gr.HTML(
                     value='<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</div></div>'
                 )
-                
                 info_output = gr.Markdown(value="")
-                
-                clear_btn = gr.Button("🗑️ Clear Ultra Analysis", size="sm")
-        
+                clear_btn = gr.Button("ð � � � ï¸ �  Clear Ultra Analysis", size="sm")
         # Event handlers
         analyze_btn.click(
             fn=process_ultra_supreme_analysis,
             inputs=[image_input],
             outputs=[prompt_output, info_output, score_output]
         )
-        
         clear_btn.click(
             fn=clear_outputs,
             outputs=[prompt_output, info_output, score_output]
         )
-        
         gr.Markdown("""
         ---
-        ### 🏆 Ultra Supreme Research Foundation
-        
+        ### ð � � �  Ultra Supreme Research Foundation
         This system represents the **absolute pinnacle** of image analysis and Flux prompt optimization. Using triple CLIP interrogation, 
         ultra-deep feature extraction, cultural context awareness, and emotional intelligence mapping, it achieves maximum possible 
         understanding and applies research-validated Flux rules with supreme intelligence.
-        
-        **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme Intelligence Engine**
+        **ð � � ¬ Pariente AI Research Laboratory** â � ¢ **ð � � �  Ultra Supreme Intelligence Engine**
         """)
-    
     return interface
 
 # Launch the application