Flux_Prompt_Optimizer

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Malaji71 commited on Jun 11

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398d82e

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1 Parent(s): 6a59263

Update app.py

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app.py +530 -403

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@@ -10,6 +10,7 @@ import warnings
 from datetime import datetime
 import gc
 import re
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
@@ -28,360 +29,482 @@ def get_device():
 DEVICE = get_device()
-class MaximumFluxAnalyzer:
     """
-    Maximum depth analysis engine - extracts EVERYTHING possible from images
     """
     def __init__(self):
         self.forbidden_elements = ["++", "weights", "white background [en dev]"]
-        # EXPANDED VOCABULARIES FOR MAXIMUM DETECTION
-        self.age_keywords = {
-            "elderly": ["old", "elderly", "aged", "senior", "mature", "weathered", "wrinkled", "gray", "grey", "white hair", "silver", "graying", "ancient", "vintage"],
-            "middle": ["middle-aged", "adult", "grown", "middle", "forties", "fifties"],
-            "young": ["young", "youth", "teenage", "boy", "girl", "child", "kid", "adolescent"]
         }
-        self.facial_features = {
-            "beard_full": ["beard", "bearded", "facial hair", "full beard", "thick beard", "heavy beard"],
-            "beard_color": ["gray beard", "grey beard", "silver beard", "white beard", "salt pepper", "graying beard"],
-            "mustache": ["mustache", "moustache", "facial hair"],
-            "glasses": ["glasses", "spectacles", "eyeglasses", "wire-frame", "rimmed glasses", "reading glasses"],
-            "eyes": ["eyes", "gaze", "stare", "looking", "piercing", "intense", "deep eyes"],
-            "wrinkles": ["wrinkled", "lines", "aged", "weathered", "creased"],
-            "expression": ["serious", "contemplative", "thoughtful", "stern", "wise", "solemn"]
         }
-        self.religious_cultural = {
-            "jewish": ["jewish", "orthodox", "hasidic", "rabbi", "religious", "traditional", "ceremonial"],
-            "hat_types": ["hat", "cap", "yarmulke", "kippah", "black hat", "traditional hat", "religious headwear"],
-            "clothing": ["suit", "jacket", "formal", "black clothing", "traditional dress", "religious attire"]
         }
-        self.hair_descriptors = {
-            "color": ["gray", "grey", "silver", "white", "black", "brown", "blonde", "salt and pepper"],
-            "texture": ["curly", "wavy", "straight", "thick", "thin", "coarse", "fine"],
-            "style": ["long", "short", "receding", "balding", "full head"]
         }
-        self.setting_environments = {
-            "indoor": ["indoor", "inside", "interior", "room", "office", "home", "building"],
-            "formal": ["formal setting", "office", "meeting room", "conference", "official"],
-            "religious": ["synagogue", "temple", "religious", "ceremonial", "sacred"],
-            "studio": ["studio", "backdrop", "professional", "photography studio"],
-            "casual": ["casual", "relaxed", "informal", "comfortable"]
         }
-        self.lighting_types = {
-            "natural": ["natural light", "window light", "daylight", "sunlight"],
-            "artificial": ["artificial light", "lamp", "electric", "indoor lighting"],
-            "dramatic": ["dramatic", "contrast", "shadow", "chiaroscuro", "moody"],
-            "soft": ["soft", "gentle", "diffused", "even", "flattering"],
-            "harsh": ["harsh", "direct", "strong", "bright", "intense"]
         }
-        self.composition_styles = {
-            "portrait": ["portrait", "headshot", "face", "facial", "close-up", "bust"],
-            "seated": ["sitting", "seated", "chair", "sitting down"],
-            "standing": ["standing", "upright", "vertical"],
-            "three_quarter": ["three quarter", "three-quarter", "angled", "turned"]
         }
-        self.quality_adjectives = {
-            "age_based": {
-                "elderly": ["distinguished", "dignified", "venerable", "wise", "weathered", "experienced"],
-                "middle": ["professional", "mature", "confident", "established"],
-                "young": ["youthful", "fresh", "vibrant", "energetic"]
-            },
-            "cultural": ["traditional", "Orthodox", "religious", "ceremonial", "devout"],
-            "general": ["elegant", "refined", "sophisticated", "classic", "timeless"]
         }
-    def extract_maximum_info(self, clip_fast, clip_classic, clip_best):
-        """Combine all three CLIP analyses for maximum information extraction"""
-        # Combine all analyses
-        combined_text = f"{clip_fast} {clip_classic} {clip_best}".lower()
-        analysis = {
-            "age": None,
-            "age_confidence": 0,
-            "gender": None,
-            "facial_features": [],
-            "hair_description": [],
-            "clothing_items": [],
-            "cultural_religious": [],
-            "setting": None,
-            "lighting": None,
-            "composition": None,
-            "mood": None,
-            "technical_suggestions": {}
-        }
-        # DEEP AGE DETECTION
-        age_scores = {"elderly": 0, "middle": 0, "young": 0}
-        for age_type, keywords in self.age_keywords.items():
-            for keyword in keywords:
-                if keyword in combined_text:
-                    age_scores[age_type] += 1
-        if max(age_scores.values()) > 0:
-            analysis["age"] = max(age_scores, key=age_scores.get)
-            analysis["age_confidence"] = age_scores[analysis["age"]]
-        # GENDER DETECTION
-        if any(word in combined_text for word in ["man", "male", "gentleman", "guy", "he", "his"]):
-            analysis["gender"] = "man"
-        elif any(word in combined_text for word in ["woman", "female", "lady", "she", "her"]):
-            analysis["gender"] = "woman"
-        # COMPREHENSIVE FACIAL FEATURES
-        if any(word in combined_text for word in self.facial_features["beard_full"]):
-            if any(word in combined_text for word in self.facial_features["beard_color"]):
-                analysis["facial_features"].append("silver beard")
-            else:
-                analysis["facial_features"].append("full beard")
-        if any(word in combined_text for word in self.facial_features["glasses"]):
-            analysis["facial_features"].append("wire-frame glasses")
-        if any(word in combined_text for word in self.facial_features["wrinkles"]):
-            analysis["facial_features"].append("weathered features")
-        # HAIR ANALYSIS
-        hair_colors = [color for color in self.hair_descriptors["color"] if color in combined_text]
-        if hair_colors:
-            analysis["hair_description"].extend(hair_colors)
-        # CULTURAL/RELIGIOUS DETECTION
-        if any(word in combined_text for word in self.religious_cultural["jewish"]):
-            analysis["cultural_religious"].append("Orthodox Jewish")
-        if any(word in combined_text for word in self.religious_cultural["hat_types"]):
-            analysis["clothing_items"].append("traditional black hat")
-        if any(word in combined_text for word in self.religious_cultural["clothing"]):
-            analysis["clothing_items"].append("formal religious attire")
-        # ENHANCED SETTING DETECTION
-        setting_scores = {}
-        for setting_type, keywords in self.setting_environments.items():
-            score = sum(1 for keyword in keywords if keyword in combined_text)
             if score > 0:
-                setting_scores[setting_type] = score
         if setting_scores:
-            analysis["setting"] = max(setting_scores, key=setting_scores.get)
         # LIGHTING ANALYSIS
-        lighting_detected = []
-        for light_type, keywords in self.lighting_types.items():
-            if any(keyword in combined_text for keyword in keywords):
-                lighting_detected.append(light_type)
-        if lighting_detected:
-            analysis["lighting"] = lighting_detected[0]  # Take first/strongest match
-        # COMPOSITION DETECTION
-        for comp_type, keywords in self.composition_styles.items():
-            if any(keyword in combined_text for keyword in keywords):
-                analysis["composition"] = comp_type
                 break
-        # TECHNICAL SUGGESTIONS BASED ON ANALYSIS
-        if analysis["composition"] == "portrait":
-            analysis["technical_suggestions"] = {
-                "lens": "85mm lens",
-                "aperture": "f/2.8 aperture",
-                "camera": "Shot on Phase One XF"
-            }
-        elif analysis["composition"] == "seated":
-            analysis["technical_suggestions"] = {
-                "lens": "85mm lens",
-                "aperture": "f/4 aperture",
-                "camera": "Shot on Phase One"
-            }
-        else:
-            analysis["technical_suggestions"] = {
-                "lens": "50mm lens",
-                "aperture": "f/2.8 aperture",
-                "camera": "Shot on Phase One"
-            }
-        return analysis
-    def build_maximum_flux_prompt(self, analysis, original_clips):
-        """Build the most detailed Flux prompt possible"""
         components = []
-        # 1. INTELLIGENT ARTICLE SELECTION
-        if analysis["cultural_religious"] and analysis["age"]:
-            # "An elderly Orthodox Jewish man"
-            article = "An" if analysis["age"] == "elderly" else "A"
-        elif analysis["gender"]:
-            article = "A"
         else:
             article = "A"
         components.append(article)
-        # 2. CONTEXT-AWARE ADJECTIVES (max 2-3 per Flux rules)
         adjectives = []
-        if analysis["age"] and analysis["age"] in self.quality_adjectives["age_based"]:
-            adjectives.extend(self.quality_adjectives["age_based"][analysis["age"]][:2])
-        if analysis["cultural_religious"]:
-            adjectives.extend(self.quality_adjectives["cultural"][:1])
         if not adjectives:
-            adjectives = self.quality_adjectives["general"][:2]
-        # Limit to 2-3 adjectives as per Flux rules
-        components.extend(adjectives[:2])
-        # 3. ENHANCED SUBJECT DESCRIPTION
-        subject_parts = []
-        if analysis["cultural_religious"]:
-            subject_parts.extend(analysis["cultural_religious"])
-        if analysis["age"] and analysis["age"] != "middle":
-            subject_parts.append(analysis["age"])
-        if analysis["gender"]:
-            subject_parts.append(analysis["gender"])
-        else:
-            subject_parts.append("person")
-        main_subject = " ".join(subject_parts)
-        components.append(main_subject)
-        # 4. DETAILED FACIAL FEATURES
-        if analysis["facial_features"]:
-            feature_desc = "with " + " and ".join(analysis["facial_features"])
-            components.append(feature_desc)
-        # 5. CLOTHING AND ACCESSORIES
-        if analysis["clothing_items"]:
-            clothing_desc = "wearing " + " and ".join(analysis["clothing_items"])
-            components.append(clothing_desc)
-        # 6. ACTION/POSE (based on composition)
-        action_map = {
-            "seated": "seated in contemplative pose",
-            "standing": "standing with dignified presence",
-            "portrait": "captured in intimate portrait style",
-            "three_quarter": "positioned in three-quarter view"
-        }
-        if analysis["composition"]:
-            action = action_map.get(analysis["composition"], "positioned thoughtfully")
-        else:
-            action = "positioned with natural composure"
-        components.append(action)
-        # 7. ENHANCED ENVIRONMENTAL CONTEXT
-        setting_descriptions = {
-            "indoor": "in a warmly lit indoor environment",
-            "formal": "in a professional formal setting",
-            "religious": "in a traditional religious space",
-            "studio": "in a controlled studio environment",
-            "casual": "in a comfortable informal setting"
-        }
-        if analysis["setting"]:
-            context = setting_descriptions.get(analysis["setting"], "in a thoughtfully composed environment")
-        else:
-            context = "within a carefully arranged scene"
-        components.append(context)
-        # 8. SOPHISTICATED LIGHTING DESCRIPTION
-        lighting_descriptions = {
-            "natural": "bathed in gentle natural lighting that enhances facial texture and depth",
-            "dramatic": "illuminated by dramatic lighting that creates compelling shadows and highlights",
-            "soft": "softly lit to emphasize character and warmth",
-            "artificial": "under controlled artificial lighting for optimal detail capture"
-        }
-        if analysis["lighting"]:
-            lighting_desc = lighting_descriptions.get(analysis["lighting"], "with professional lighting that emphasizes facial features and texture")
-        else:
-            lighting_desc = "captured with sophisticated portrait lighting that brings out intricate facial details"
         components.append(lighting_desc)
-        # 9. TECHNICAL SPECIFICATIONS
-        tech_parts = []
-        if analysis["technical_suggestions"]:
-            tech_parts.append(analysis["technical_suggestions"]["camera"])
-            tech_parts.append(analysis["technical_suggestions"]["lens"])
-            tech_parts.append(analysis["technical_suggestions"]["aperture"])
         else:
-            tech_parts = ["Shot on Phase One", "85mm lens", "f/2.8 aperture"]
-        components.append(", ".join(tech_parts))
-        # 10. QUALITY MARKER
-        components.append("professional portrait photography")
-        # FINAL ASSEMBLY AND OPTIMIZATION
         prompt = ", ".join(components)
-        # Clean up the prompt
-        prompt = re.sub(r'\s+', ' ', prompt)  # Remove extra spaces
-        prompt = re.sub(r',\s*,', ',', prompt)  # Remove double commas
-        prompt = prompt.replace(" ,", ",")  # Fix spacing around commas
-        # Ensure proper capitalization
-        prompt = prompt[0].upper() + prompt[1:] if prompt else ""
         return prompt
-    def calculate_maximum_score(self, prompt, analysis):
-        """Calculate intelligence score based on depth of analysis"""
         score = 0
-        max_possible = 100
-        # Structure compliance (10 points)
         if prompt.startswith(("A", "An")):
-            score += 10
-        # Feature detection depth (20 points)
-        feature_score = len(analysis["facial_features"]) * 5
-        score += min(feature_score, 20)
-        # Cultural/contextual awareness (20 points)
-        if analysis["cultural_religious"]:
-            score += 15
-        if analysis["age"]:
-            score += 5
-        # Technical appropriateness (15 points)
-        if "85mm" in prompt and analysis["composition"] in ["portrait", "seated"]:
-            score += 15
-        elif "50mm" in prompt:
-            score += 10
-        # Lighting sophistication (15 points)
-        if "lighting" in prompt and len(prompt.split("lighting")[1].split(",")[0]) > 10:
-            score += 15
-        # Setting context (10 points)
-        if analysis["setting"]:
-            score += 10
-        # Forbidden elements check (10 points)
-        if not any(forbidden in prompt for forbidden in self.forbidden_elements):
-            score += 10
-        return min(score, max_possible)
-class MaximumFluxOptimizer:
     def __init__(self):
         self.interrogator = None
-        self.analyzer = MaximumFluxAnalyzer()
         self.usage_count = 0
         self.device = DEVICE
         self.is_initialized = False
@@ -432,40 +555,40 @@ class MaximumFluxOptimizer:
         return image
     @spaces.GPU
-    def generate_maximum_prompt(self, image):
         try:
             if not self.is_initialized:
                 if not self.initialize_model():
-                    return "❌ Model initialization failed.", "Please refresh and try again.", 0
             if image is None:
-                return "❌ Please upload an image.", "No image provided.", 0
             self.usage_count += 1
             image = self.optimize_image(image)
             if image is None:
-                return "❌ Image processing failed.", "Invalid image format.", 0
             start_time = datetime.now()
-            # TRIPLE CLIP ANALYSIS FOR MAXIMUM INFORMATION
-            logger.info("Starting MAXIMUM analysis - Triple CLIP interrogation")
             clip_fast = self.interrogator.interrogate_fast(image)
             clip_classic = self.interrogator.interrogate_classic(image)
             clip_best = self.interrogator.interrogate(image)
-            logger.info(f"CLIP Results:\nFast: {clip_fast}\nClassic: {clip_classic}\nBest: {clip_best}")
-            # MAXIMUM DEPTH ANALYSIS
-            deep_analysis = self.analyzer.extract_maximum_info(clip_fast, clip_classic, clip_best)
-            # BUILD MAXIMUM QUALITY FLUX PROMPT
-            optimized_prompt = self.analyzer.build_maximum_flux_prompt(deep_analysis, [clip_fast, clip_classic, clip_best])
-            # CALCULATE INTELLIGENCE SCORE
-            score = self.analyzer.calculate_maximum_score(optimized_prompt, deep_analysis)
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
@@ -476,52 +599,54 @@ class MaximumFluxOptimizer:
             else:
                 torch.cuda.empty_cache()
-            # COMPREHENSIVE ANALYSIS REPORT
             gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
             # Format detected elements
-            features = ", ".join(deep_analysis["facial_features"]) if deep_analysis["facial_features"] else "None detected"
-            cultural = ", ".join(deep_analysis["cultural_religious"]) if deep_analysis["cultural_religious"] else "None detected"
-            clothing = ", ".join(deep_analysis["clothing_items"]) if deep_analysis["clothing_items"] else "None detected"
-            analysis_info = f"""**MAXIMUM ANALYSIS COMPLETE**
-**Processing:** {gpu_status} • {duration:.1f}s • Triple CLIP interrogation
-**Intelligence Score:** {score}/100
-**Analysis Confidence:** {deep_analysis.get("age_confidence", 0)} age indicators detected
 **Generation:** #{self.usage_count}
-**DEEP DETECTION RESULTS:**
-• **Age Category:** {deep_analysis.get("age", "Unspecified").title()}
 • **Cultural Context:** {cultural}
 • **Facial Features:** {features}
-• **Clothing/Accessories:** {clothing}
-• **Setting:** {deep_analysis.get("setting", "Standard").title()}
-• **Composition:** {deep_analysis.get("composition", "Standard").title()}
-• **Lighting:** {deep_analysis.get("lighting", "Standard").title()}
-**CLIP ANALYSIS SOURCES:**
-• **Fast:** {clip_fast[:60]}...
-• **Classic:** {clip_classic[:60]}...
-• **Best:** {clip_best[:60]}...
-**FLUX OPTIMIZATION:** Applied maximum depth analysis with Pariente AI research rules"""
-            return optimized_prompt, analysis_info, score
         except Exception as e:
-            logger.error(f"Maximum generation error: {e}")
-            return f"❌ Error: {str(e)}", "Please try with a different image.", 0
-optimizer = MaximumFluxOptimizer()
-def process_maximum_analysis(image):
-    """Maximum analysis wrapper"""
     try:
-        prompt, info, score = optimizer.generate_maximum_prompt(image)
-        # Enhanced score display
-        if score >= 90:
             color = "#10b981"
             grade = "EXCELLENT"
         elif score >= 80:
@@ -538,31 +663,31 @@ def process_maximum_analysis(image):
             grade = "NEEDS WORK"
         score_html = f'''
-        <div style="text-align: center; padding: 1.5rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 2px solid {color}; border-radius: 12px; margin: 1rem 0; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);">
-            <div style="font-size: 2.5rem; font-weight: 700; color: {color}; margin: 0;">{score}</div>
-            <div style="font-size: 1rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em; font-weight: 600;">{grade}</div>
-            <div style="font-size: 0.875rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em;">Maximum Intelligence Score</div>
         </div>
         '''
         return prompt, info, score_html
     except Exception as e:
-        logger.error(f"Maximum 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;">Maximum Intelligence Score</div></div>'
 def create_interface():
     css = """
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap');
     .gradio-container {
-        max-width: 1400px !important;
         margin: 0 auto !important;
         font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
         background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important;
@@ -570,140 +695,142 @@ def create_interface():
     .main-header {
         text-align: center;
-        padding: 2rem 0 3rem 0;
-        background: linear-gradient(135deg, #0f172a 0%, #1e293b 50%, #334155 100%);
         color: white;
-        margin: -2rem -2rem 2rem -2rem;
-        border-radius: 0 0 24px 24px;
-        box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1);
     }
     .main-title {
-        font-size: 3rem !important;
-        font-weight: 800 !important;
-        margin: 0 0 0.5rem 0 !important;
-        letter-spacing: -0.025em !important;
-        background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 50%, #2563eb 100%);
         -webkit-background-clip: text;
         -webkit-text-fill-color: transparent;
         background-clip: text;
     }
     .subtitle {
-        font-size: 1.25rem !important;
-        font-weight: 400 !important;
-        opacity: 0.9 !important;
         margin: 0 !important;
     }
     .prompt-output {
         font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
-        font-size: 14px !important;
-        line-height: 1.7 !important;
         background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%) !important;
-        border: 1px solid #e2e8f0 !important;
-        border-radius: 16px !important;
-        padding: 2rem !important;
-        box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1) !important;
     }
     """
     with gr.Blocks(
         theme=gr.themes.Soft(),
-        title="Maximum Flux Prompt Optimizer",
         css=css
     ) as interface:
         gr.HTML("""
         <div class="main-header">
-            <div class="main-title">🧠 Maximum Flux Optimizer</div>
-            <div class="subtitle">Triple CLIP Analysis • Maximum Intelligence • Zero Configuration</div>
         </div>
         """)
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown("## 🔬 Maximum Analysis")
                 image_input = gr.Image(
-                    label="Upload your image for maximum analysis",
                     type="pil",
-                    height=450
                 )
                 analyze_btn = gr.Button(
-                    "🚀 MAXIMUM ANALYSIS",
                     variant="primary",
                     size="lg"
                 )
                 gr.Markdown("""
-                ### Maximum Intelligence Engine
-                **Triple CLIP Interrogation:**
-                • Fast analysis for broad context
-                • Classic analysis for detailed features
-                • Best analysis for maximum depth
-                **Deep Feature Extraction:**
-                • Age, gender, cultural context
-                • Facial features, expressions, accessories
-                • Clothing, religious/cultural indicators
-                • Environmental setting and lighting
-                • Composition and technical optimization
-                **No configuration needed** - Maximum intelligence applied automatically.
                 """)
             with gr.Column(scale=1):
-                gr.Markdown("## ⚡ Maximum Result")
                 prompt_output = gr.Textbox(
-                    label="Maximum Optimized Flux Prompt",
-                    placeholder="Upload an image to see the maximum intelligence analysis...",
-                    lines=10,
-                    max_lines=15,
                     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;">Maximum Intelligence Score</div></div>'
                 )
                 info_output = gr.Markdown(value="")
-                clear_btn = gr.Button("🗑️ Clear Analysis", size="sm")
         gr.Markdown("""
         ---
-        ### 🔬 Maximum Research Foundation
-        This system represents the absolute maximum in image analysis and Flux prompt optimization. Using triple CLIP interrogation
-        and deep feature extraction, it identifies every possible detail and applies research-validated Flux rules with maximum intelligence.
-        **Pariente AI Research Laboratory** • Maximum Intelligence • Research-Driven • Zero Compromise
-        """)
-        # Maximum event handlers
-        analyze_btn.click(
-            fn=process_maximum_analysis,
-            inputs=[image_input],
-            outputs=[prompt_output, info_output, score_output]
-        )
-        clear_btn.click(
-            fn=clear_outputs,
-            outputs=[prompt_output, info_output, score_output]
-        )
-    return interface
-if __name__ == "__main__":
-    logger.info("🚀 Starting MAXIMUM Flux Prompt Optimizer")
-    interface = create_interface()
-    interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )

 from datetime import datetime
 import gc
 import re
+import math
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=UserWarning)
 DEVICE = get_device()
+class UltraSupremeAnalyzer:
     """
+    ULTRA SUPREME ANALYSIS ENGINE - ABSOLUTE MAXIMUM INTELLIGENCE
     """
     def __init__(self):
         self.forbidden_elements = ["++", "weights", "white background [en dev]"]
+        # ULTRA COMPREHENSIVE VOCABULARIES - MAXIMUM DEPTH
+        self.micro_age_indicators = {
+            "infant": ["baby", "infant", "newborn", "toddler"],
+            "child": ["child", "kid", "young", "little", "small", "youth"],
+            "teen": ["teenager", "teen", "adolescent", "young adult", "student"],
+            "young_adult": ["young adult", "twenties", "thirty", "youthful", "fresh"],
+            "middle_aged": ["middle-aged", "forties", "fifties", "mature", "experienced"],
+            "senior": ["senior", "older", "elderly", "aged", "vintage", "seasoned"],
+            "elderly": ["elderly", "old", "ancient", "weathered", "aged", "gray", "grey", "white hair", "silver", "wrinkled", "lined", "creased", "time-worn", "distinguished by age"]
         }
+        self.ultra_facial_analysis = {
+            "eye_features": {
+                "shape": ["round eyes", "almond eyes", "narrow eyes", "wide eyes", "deep-set eyes", "prominent eyes"],
+                "expression": ["intense gaze", "piercing stare", "gentle eyes", "wise eyes", "tired eyes", "alert eyes", "contemplative stare", "focused gaze", "distant look"],
+                "color": ["brown eyes", "blue eyes", "green eyes", "hazel eyes", "dark eyes", "light eyes"],
+                "condition": ["clear eyes", "bloodshot", "bright eyes", "dull eyes", "sparkling eyes"]
+            },
+            "eyebrow_analysis": ["thick eyebrows", "thin eyebrows", "bushy eyebrows", "arched eyebrows", "straight eyebrows", "gray eyebrows"],
+            "nose_features": ["prominent nose", "straight nose", "aquiline nose", "small nose", "wide nose", "narrow nose"],
+            "mouth_expression": {
+                "shape": ["thin lips", "full lips", "small mouth", "wide mouth"],
+                "expression": ["slight smile", "serious expression", "frown", "neutral expression", "contemplative look", "stern look", "gentle expression"]
+            },
+            "facial_hair_ultra": {
+                "beard_types": ["full beard", "goatee", "mustache", "stubble", "clean-shaven", "five o'clock shadow"],
+                "beard_texture": ["thick beard", "thin beard", "coarse beard", "fine beard", "well-groomed beard", "unkempt beard"],
+                "beard_color": ["black beard", "brown beard", "gray beard", "grey beard", "silver beard", "white beard", "salt-and-pepper beard", "graying beard"],
+                "beard_length": ["long beard", "short beard", "trimmed beard", "full-length beard"]
+            },
+            "skin_analysis": ["smooth skin", "weathered skin", "wrinkled skin", "clear skin", "rough skin", "aged skin", "youthful skin", "tanned skin", "pale skin", "olive skin"],
+            "facial_structure": ["angular face", "round face", "oval face", "square jaw", "defined cheekbones", "high cheekbones", "strong jawline", "soft features", "sharp features"]
         }
+        self.emotion_micro_expressions = {
+            "primary_emotions": ["happy", "sad", "angry", "fearful", "surprised", "disgusted", "contemptuous"],
+            "complex_emotions": ["contemplative", "melancholic", "serene", "intense", "peaceful", "troubled", "confident", "uncertain", "wise", "stern", "gentle", "authoritative"],
+            "emotional_indicators": ["furrowed brow", "raised eyebrows", "squinted eyes", "pursed lips", "relaxed expression", "tense jaw", "soft eyes", "hard stare"]
         }
+        self.cultural_religious_ultra = {
+            "jewish_orthodox": ["Orthodox Jewish", "Hasidic", "Ultra-Orthodox", "religious Jewish", "traditional Jewish", "devout Jewish"],
+            "christian": ["Christian", "Catholic", "Protestant", "Orthodox Christian", "religious Christian"],
+            "muslim": ["Muslim", "Islamic", "religious Muslim", "devout Muslim"],
+            "buddhist": ["Buddhist", "monk", "religious Buddhist"],
+            "general_religious": ["religious", "devout", "pious", "spiritual", "faithful", "observant"],
+            "traditional_clothing": {
+                "jewish": ["yarmulke", "kippah", "tallit", "tzitzit", "black hat", "Orthodox hat", "religious hat", "traditional Jewish hat"],
+                "general": ["religious garment", "traditional clothing", "ceremonial dress", "formal religious attire"]
+            }
         }
+        self.clothing_accessories_ultra = {
+            "headwear": ["hat", "cap", "beret", "headband", "turban", "hood", "helmet", "crown", "headpiece"],
+            "eyewear": ["glasses", "spectacles", "sunglasses", "reading glasses", "wire-frame glasses", "thick-rimmed glasses", "designer glasses", "vintage glasses"],
+            "clothing_types": ["suit", "jacket", "shirt", "dress", "robe", "uniform", "casual wear", "formal wear", "business attire"],
+            "clothing_colors": ["black", "white", "gray", "blue", "red", "green", "brown", "navy", "dark", "light"],
+            "clothing_styles": ["formal", "casual", "business", "traditional", "modern", "vintage", "classic", "contemporary"],
+            "accessories": ["jewelry", "watch", "necklace", "ring", "bracelet", "earrings", "pin", "brooch"]
         }
+        self.environmental_ultra_analysis = {
+            "indoor_settings": {
+                "residential": ["home", "house", "apartment", "living room", "bedroom", "kitchen", "dining room"],
+                "office": ["office", "workplace", "conference room", "meeting room", "boardroom", "desk"],
+                "institutional": ["school", "hospital", "government building", "court", "library"],
+                "religious": ["church", "synagogue", "mosque", "temple", "chapel", "sanctuary"],
+                "commercial": ["store", "restaurant", "hotel", "mall", "shop"]
+            },
+            "outdoor_settings": {
+                "natural": ["park", "garden", "forest", "beach", "mountain", "countryside", "field"],
+                "urban": ["street", "city", "downtown", "plaza", "square", "avenue"],
+                "architectural": ["building", "monument", "bridge", "structure"]
+            },
+            "lighting_ultra": {
+                "natural_light": ["sunlight", "daylight", "morning light", "afternoon light", "evening light", "golden hour", "blue hour", "overcast light", "window light"],
+                "artificial_light": ["indoor lighting", "electric light", "lamp light", "overhead lighting", "side lighting", "fluorescent", "LED lighting"],
+                "dramatic_lighting": ["high contrast", "low key", "high key", "chiaroscuro", "dramatic shadows", "rim lighting", "backlighting", "spotlight"],
+                "quality": ["soft lighting", "hard lighting", "diffused light", "direct light", "ambient light", "mood lighting"]
+            }
         }
+        self.pose_body_language_ultra = {
+            "head_position": ["head up", "head down", "head tilted", "head straight", "head turned", "profile view", "three-quarter view"],
+            "posture": ["upright posture", "slouched", "relaxed posture", "formal posture", "casual stance", "dignified bearing"],
+            "hand_positions": ["hands clasped", "hands folded", "hands visible", "hands hidden", "gesturing", "pointing"],
+            "sitting_positions": ["sitting upright", "leaning forward", "leaning back", "sitting casually", "formal sitting"],
+            "eye_contact": ["looking at camera", "looking away", "direct gaze", "averted gaze", "looking down", "looking up"],
+            "overall_demeanor": ["confident", "reserved", "approachable", "authoritative", "gentle", "stern", "relaxed", "tense"]
         }
+        self.composition_photography_ultra = {
+            "shot_types": ["close-up", "medium shot", "wide shot", "extreme close-up", "portrait shot", "headshot", "bust shot", "full body"],
+            "angles": ["eye level", "high angle", "low angle", "bird's eye", "worm's eye", "Dutch angle"],
+            "framing": ["centered", "off-center", "rule of thirds", "tight framing", "loose framing"],
+            "depth_of_field": ["shallow depth", "deep focus", "bokeh", "sharp focus", "soft focus"],
+            "camera_movement": ["static", "handheld", "stabilized", "smooth"]
         }
+        self.technical_photography_ultra = {
+            "camera_systems": {
+                "professional": ["Phase One XF", "Phase One XT", "Hasselblad X2D", "Fujifilm GFX", "Canon EOS R5", "Nikon Z9"],
+                "medium_format": ["Phase One", "Hasselblad", "Fujifilm GFX", "Pentax 645"],
+                "full_frame": ["Canon EOS R", "Nikon Z", "Sony A7", "Leica SL"]
+            },
+            "lenses_ultra": {
+                "portrait": ["85mm f/1.4", "135mm f/2", "105mm f/1.4", "200mm f/2.8"],
+                "standard": ["50mm f/1.4", "35mm f/1.4", "24-70mm f/2.8"],
+                "wide": ["24mm f/1.4", "16-35mm f/2.8", "14mm f/2.8"]
+            },
+            "aperture_settings": ["f/1.4", "f/2", "f/2.8", "f/4", "f/5.6", "f/8"],
+            "photography_styles": ["portrait photography", "documentary photography", "fine art photography", "commercial photography", "editorial photography"]
+        }
+        self.quality_descriptors_ultra = {
+            "based_on_age": {
+                "elderly": ["distinguished", "venerable", "dignified", "wise", "experienced", "seasoned", "time-honored", "revered", "weathered", "sage-like"],
+                "middle_aged": ["professional", "accomplished", "established", "confident", "mature", "refined", "sophisticated"],
+                "young_adult": ["vibrant", "energetic", "fresh", "youthful", "dynamic", "spirited", "lively"]
+            },
+            "based_on_emotion": {
+                "contemplative": ["thoughtful", "reflective", "meditative", "introspective"],
+                "confident": ["assured", "self-possessed", "commanding", "authoritative"],
+                "gentle": ["kind", "warm", "compassionate", "tender"],
+                "stern": ["serious", "grave", "solemn", "austere"]
+            },
+            "based_on_setting": {
+                "formal": ["professional", "official", "ceremonial", "dignified"],
+                "casual": ["relaxed", "informal", "comfortable", "natural"],
+                "artistic": ["creative", "expressive", "aesthetic", "artistic"]
+            }
+        }
+        def ultra_supreme_analysis(self, clip_fast, clip_classic, clip_best):
+        """ULTRA SUPREME ANALYSIS - MAXIMUM POSSIBLE INTELLIGENCE"""
+        combined_analysis = {
+            "fast": clip_fast.lower(),
+            "classic": clip_classic.lower(),
+            "best": clip_best.lower(),
+            "combined": f"{clip_fast} {clip_classic} {clip_best}".lower()
+        }
+        ultra_result = {
+            "demographic": {"age_category": None, "age_confidence": 0, "gender": None, "cultural_religious": []},
+            "facial_ultra": {"eyes": [], "eyebrows": [], "nose": [], "mouth": [], "facial_hair": [], "skin": [], "structure": []},
+            "emotional_state": {"primary_emotion": None, "emotion_confidence": 0, "micro_expressions": [], "overall_demeanor": []},
+            "clothing_accessories": {"headwear": [], "eyewear": [], "clothing": [], "accessories": []},
+            "environmental": {"setting_type": None, "specific_location": None, "lighting_analysis": [], "atmosphere": []},
+            "pose_composition": {"body_language": [], "head_position": [], "eye_contact": [], "posture": []},
+            "technical_analysis": {"shot_type": None, "angle": None, "lighting_setup": None, "suggested_equipment": {}},
+            "intelligence_metrics": {"total_features_detected": 0, "analysis_depth_score": 0, "cultural_awareness_score": 0, "technical_optimization_score": 0}
+        }
+        # ULTRA DEEP AGE ANALYSIS
+        age_scores = {}
+        for age_category, indicators in self.micro_age_indicators.items():
+            score = sum(1 for indicator in indicators if indicator in combined_analysis["combined"])
             if score > 0:
+                age_scores[age_category] = score
+        if age_scores:
+            ultra_result["demographic"]["age_category"] = max(age_scores, key=age_scores.get)
+            ultra_result["demographic"]["age_confidence"] = age_scores[ultra_result["demographic"]["age_category"]]
+        # GENDER DETECTION WITH CONFIDENCE
+        male_indicators = ["man", "male", "gentleman", "guy", "he", "his", "masculine"]
+        female_indicators = ["woman", "female", "lady", "she", "her", "feminine"]
+        male_score = sum(1 for indicator in male_indicators if indicator in combined_analysis["combined"])
+        female_score = sum(1 for indicator in female_indicators if indicator in combined_analysis["combined"])
+        if male_score > female_score:
+            ultra_result["demographic"]["gender"] = "man"
+        elif female_score > male_score:
+            ultra_result["demographic"]["gender"] = "woman"
+        # ULTRA CULTURAL/RELIGIOUS ANALYSIS
+        for culture_type, indicators in self.cultural_religious_ultra.items():
+            if isinstance(indicators, list):
+                for indicator in indicators:
+                    if indicator.lower() in combined_analysis["combined"]:
+                        ultra_result["demographic"]["cultural_religious"].append(indicator)
+        # COMPREHENSIVE FACIAL FEATURE ANALYSIS
+        for hair_category, features in self.ultra_facial_analysis["facial_hair_ultra"].items():
+            for feature in features:
+                if feature in combined_analysis["combined"]:
+                    ultra_result["facial_ultra"]["facial_hair"].append(feature)
+        # Eyes analysis
+        for eye_category, features in self.ultra_facial_analysis["eye_features"].items():
+            for feature in features:
+                if feature in combined_analysis["combined"]:
+                    ultra_result["facial_ultra"]["eyes"].append(feature)
+        # EMOTION AND MICRO-EXPRESSION ANALYSIS
+        emotion_scores = {}
+        for emotion in self.emotion_micro_expressions["complex_emotions"]:
+            if emotion in combined_analysis["combined"]:
+                emotion_scores[emotion] = combined_analysis["combined"].count(emotion)
+        if emotion_scores:
+            ultra_result["emotional_state"]["primary_emotion"] = max(emotion_scores, key=emotion_scores.get)
+            ultra_result["emotional_state"]["emotion_confidence"] = emotion_scores[ultra_result["emotional_state"]["primary_emotion"]]
+        # CLOTHING AND ACCESSORIES ANALYSIS
+        for category, items in self.clothing_accessories_ultra.items():
+            if isinstance(items, list):
+                for item in items:
+                    if item in combined_analysis["combined"]:
+                        ultra_result["clothing_accessories"][category].append(item)
+        # ENVIRONMENTAL ULTRA ANALYSIS
+        setting_scores = {}
+        for main_setting, sub_settings in self.environmental_ultra_analysis.items():
+            if isinstance(sub_settings, dict):
+                for sub_type, locations in sub_settings.items():
+                    score = sum(1 for location in locations if location in combined_analysis["combined"])
+                    if score > 0:
+                        setting_scores[sub_type] = score
         if setting_scores:
+            ultra_result["environmental"]["setting_type"] = max(setting_scores, key=setting_scores.get)
         # LIGHTING ANALYSIS
+        for light_category, light_types in self.environmental_ultra_analysis["lighting_ultra"].items():
+            for light_type in light_types:
+                if light_type in combined_analysis["combined"]:
+                    ultra_result["environmental"]["lighting_analysis"].append(light_type)
+        # POSE AND BODY LANGUAGE ANALYSIS
+        for pose_category, indicators in self.pose_body_language_ultra.items():
+            for indicator in indicators:
+                if indicator in combined_analysis["combined"]:
+                    ultra_result["pose_composition"][pose_category].append(indicator)
+        # TECHNICAL PHOTOGRAPHY ANALYSIS
+        for shot_type in self.composition_photography_ultra["shot_types"]:
+            if shot_type in combined_analysis["combined"]:
+                ultra_result["technical_analysis"]["shot_type"] = shot_type
                 break
+        # CALCULATE INTELLIGENCE METRICS
+        total_features = sum(len(v) if isinstance(v, list) else (1 if v else 0) for category in ultra_result.values() if isinstance(category, dict) for v in category.values())
+        ultra_result["intelligence_metrics"]["total_features_detected"] = total_features
+        ultra_result["intelligence_metrics"]["analysis_depth_score"] = min(total_features * 5, 100)
+        ultra_result["intelligence_metrics"]["cultural_awareness_score"] = len(ultra_result["demographic"]["cultural_religious"]) * 20
+        return ultra_result
+    def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
+        """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
         components = []
+        # 1. ULTRA INTELLIGENT ARTICLE SELECTION
+        subject_desc = []
+        if ultra_analysis["demographic"]["cultural_religious"]:
+            subject_desc.extend(ultra_analysis["demographic"]["cultural_religious"][:1])
+        if ultra_analysis["demographic"]["age_category"] and ultra_analysis["demographic"]["age_category"] != "middle_aged":
+            subject_desc.append(ultra_analysis["demographic"]["age_category"].replace("_", " "))
+        if ultra_analysis["demographic"]["gender"]:
+            subject_desc.append(ultra_analysis["demographic"]["gender"])
+        if subject_desc:
+            full_subject = " ".join(subject_desc)
+            article = "An" if full_subject[0].lower() in 'aeiou' else "A"
         else:
             article = "A"
         components.append(article)
+        # 2. ULTRA CONTEXTUAL ADJECTIVES (max 2-3 per Flux rules)
         adjectives = []
+        # Age-based adjectives
+        age_cat = ultra_analysis["demographic"]["age_category"]
+        if age_cat and age_cat in self.quality_descriptors_ultra["based_on_age"]:
+            adjectives.extend(self.quality_descriptors_ultra["based_on_age"][age_cat][:2])
+        # Emotion-based adjectives
+        emotion = ultra_analysis["emotional_state"]["primary_emotion"]
+        if emotion and emotion in self.quality_descriptors_ultra["based_on_emotion"]:
+            adjectives.extend(self.quality_descriptors_ultra["based_on_emotion"][emotion][:1])
+        # Default if none found
         if not adjectives:
+            adjectives = ["distinguished", "professional"]
+        components.extend(adjectives[:2])  # Flux rule: max 2-3 adjectives
+        # 3. ULTRA ENHANCED SUBJECT
+        if subject_desc:
+            components.append(" ".join(subject_desc))
+        else:
+            components.append("person")
+        # 4. ULTRA DETAILED FACIAL FEATURES
+        facial_details = []
+        # Eyes
+        if ultra_analysis["facial_ultra"]["eyes"]:
+            eye_desc = ultra_analysis["facial_ultra"]["eyes"][0]
+            facial_details.append(f"with {eye_desc}")
+        # Facial hair with ultra detail
+        if ultra_analysis["facial_ultra"]["facial_hair"]:
+            beard_details = ultra_analysis["facial_ultra"]["facial_hair"]
+            if any("silver" in detail or "gray" in detail or "grey" in detail for detail in beard_details):
+                facial_details.append("with a distinguished silver beard")
+            elif any("beard" in detail for detail in beard_details):
+                facial_details.append("with a full well-groomed beard")
+        if facial_details:
+            components.extend(facial_details)
+        # 5. CLOTHING AND ACCESSORIES ULTRA
+        clothing_details = []
+        # Eyewear
+        if ultra_analysis["clothing_accessories"]["eyewear"]:
+            eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0]
+            clothing_details.append(f"wearing {eyewear}")
+        # Headwear
+        if ultra_analysis["clothing_accessories"]["headwear"]:
+            headwear = ultra_analysis["clothing_accessories"]["headwear"][0]
+            if ultra_analysis["demographic"]["cultural_religious"]:
+                clothing_details.append("wearing a traditional black hat")
+            else:
+                clothing_details.append(f"wearing a {headwear}")
+        if clothing_details:
+            components.extend(clothing_details)
+        # 6. ULTRA POSE AND BODY LANGUAGE
+        pose_description = "positioned with natural dignity"
+        if ultra_analysis["pose_composition"]["posture"]:
+            posture = ultra_analysis["pose_composition"]["posture"][0]
+            pose_description = f"maintaining {posture}"
+        elif ultra_analysis["technical_analysis"]["shot_type"] == "portrait":
+            pose_description = "captured in contemplative portrait pose"
+        components.append(pose_description)
+        # 7. ULTRA ENVIRONMENTAL CONTEXT
+        environment_desc = "in a thoughtfully composed environment"
+        if ultra_analysis["environmental"]["setting_type"]:
+            setting_map = {
+                "residential": "in an intimate home setting",
+                "office": "in a professional office environment",
+                "religious": "in a sacred traditional space",
+                "formal": "in a distinguished formal setting"
+            }
+            environment_desc = setting_map.get(ultra_analysis["environmental"]["setting_type"], "in a carefully arranged professional setting")
+        components.append(environment_desc)
+        # 8. ULTRA SOPHISTICATED LIGHTING
+        lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture"
+        if ultra_analysis["environmental"]["lighting_analysis"]:
+            primary_light = ultra_analysis["environmental"]["lighting_analysis"][0]
+            if "dramatic" in primary_light:
+                lighting_desc = "bathed in dramatic chiaroscuro lighting that creates compelling depth and shadow play"
+            elif "natural" in primary_light or "window" in primary_light:
+                lighting_desc = "graced by gentle natural lighting that brings out intricate facial details and warmth"
+            elif "soft" in primary_light:
+                lighting_desc = "softly illuminated to reveal nuanced expressions and character"
         components.append(lighting_desc)
+        # 9. ULTRA TECHNICAL SPECIFICATIONS
+        if ultra_analysis["technical_analysis"]["shot_type"] in ["portrait", "headshot", "close-up"]:
+            camera_setup = "Shot on Phase One XF IQ4, 85mm f/1.4 lens, f/2.8 aperture"
+        elif ultra_analysis["demographic"]["cultural_religious"]:
+            camera_setup = "Shot on Hasselblad X2D, 90mm lens, f/2.8 aperture"
         else:
+            camera_setup = "Shot on Phase One XF, 80mm lens, f/4 aperture"
+        components.append(camera_setup)
+        # 10. ULTRA QUALITY DESIGNATION
+        quality_designation = "professional portrait photography"
+        if ultra_analysis["demographic"]["cultural_religious"]:
+            quality_designation = "fine art documentary photography"
+        elif ultra_analysis["emotional_state"]["primary_emotion"]:
+            quality_designation = "expressive portrait photography"
+        components.append(quality_designation)
+        # ULTRA FINAL ASSEMBLY
         prompt = ", ".join(components)
+        # Ultra cleaning and optimization
+        prompt = re.sub(r'\s+', ' ', prompt)
+        prompt = re.sub(r',\s*,+', ',', prompt)
+        prompt = re.sub(r'\s*,\s*', ', ', prompt)
+        prompt = prompt.replace(" ,", ",")
+        if prompt:
+            prompt = prompt[0].upper() + prompt[1:]
         return prompt
+    def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
+        """ULTRA SUPREME INTELLIGENCE SCORING"""
         score = 0
+        breakdown = {}
+        # Structure Excellence (15 points)
+        structure_score = 0
         if prompt.startswith(("A", "An")):
+            structure_score += 5
+        if prompt.count(",") >= 8:
+            structure_score += 10
+        score += structure_score
+        breakdown["structure"] = structure_score
+        # Feature Detection Depth (25 points)
+        features_score = min(ultra_analysis["intelligence_metrics"]["total_features_detected"] * 2, 25)
+        score += features_score
+        breakdown["features"] = features_score
+        # Cultural/Religious Awareness (20 points)
+        cultural_score = min(len(ultra_analysis["demographic"]["cultural_religious"]) * 10, 20)
+        score += cultural_score
+        breakdown["cultural"] = cultural_score
+        # Emotional Intelligence (15 points)
+        emotion_score = 0
+        if ultra_analysis["emotional_state"]["primary_emotion"]:
+            emotion_score += 10
+        if ultra_analysis["emotional_state"]["emotion_confidence"] > 1:
+            emotion_score += 5
+        score += emotion_score
+        breakdown["emotional"] = emotion_score
+        # Technical Sophistication (15 points)
+        tech_score = 0
+        if "Phase One" in prompt or "Hasselblad" in prompt:
+            tech_score += 5
+        if any(aperture in prompt for aperture in ["f/1.4", "f/2.8", "f/4"]):
+            tech_score += 5
+        if any(lens in prompt for lens in ["85mm", "90mm", "80mm"]):
+            tech_score += 5
+        score += tech_score
+        breakdown["technical"] = tech_score
+        # Environmental Context (10 points)
+        env_score = 0
+        if ultra_analysis["environmental"]["setting_type"]:
+            env_score += 5
+        if ultra_analysis["environmental"]["lighting_analysis"]:
+            env_score += 5
+        score += env_score
+        breakdown["environmental"] = env_score
+        return min(score, 100), breakdown
+class UltraSupremeOptimizer:
     def __init__(self):
         self.interrogator = None
+        self.analyzer = UltraSupremeAnalyzer()
         self.usage_count = 0
         self.device = DEVICE
         self.is_initialized = False
         return image
     @spaces.GPU
+    def generate_ultra_supreme_prompt(self, image):
         try:
             if not self.is_initialized:
                 if not self.initialize_model():
+                    return "❌ Model initialization failed.", "Please refresh and try again.", 0, {}
             if image is None:
+                return "❌ Please upload an image.", "No image provided.", 0, {}
             self.usage_count += 1
             image = self.optimize_image(image)
             if image is None:
+                return "❌ Image processing failed.", "Invalid image format.", 0, {}
             start_time = datetime.now()
+            # ULTRA SUPREME TRIPLE CLIP ANALYSIS
+            logger.info("ULTRA SUPREME ANALYSIS - Maximum intelligence deployment")
             clip_fast = self.interrogator.interrogate_fast(image)
             clip_classic = self.interrogator.interrogate_classic(image)
             clip_best = self.interrogator.interrogate(image)
+            logger.info(f"ULTRA CLIP Results:\nFast: {clip_fast}\nClassic: {clip_classic}\nBest: {clip_best}")
+            # ULTRA SUPREME ANALYSIS
+            ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best)
+            # BUILD ULTRA SUPREME FLUX PROMPT
+            optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best])
+            # CALCULATE ULTRA SUPREME SCORE
+            score, breakdown = self.analyzer.calculate_ultra_supreme_score(optimized_prompt, ultra_analysis)
             end_time = datetime.now()
             duration = (end_time - start_time).total_seconds()
             else:
                 torch.cuda.empty_cache()
+            # ULTRA COMPREHENSIVE ANALYSIS REPORT
             gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
             # Format detected elements
+            features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected"
+            cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected"
+            clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected"
+            analysis_info = f"""**🚀 ULTRA SUPREME ANALYSIS COMPLETE**
+**Processing:** {gpu_status} • {duration:.1f}s • Triple CLIP Ultra Intelligence
+**Ultra Score:** {score}/100 • Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)})
 **Generation:** #{self.usage_count}
+**🧠 ULTRA DEEP DETECTION:**
+• **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)})
 • **Cultural Context:** {cultural}
 • **Facial Features:** {features}
+• **Accessories:** {clothing}
+• **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()}
+• **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()}
+• **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]}
+**📊 CLIP ANALYSIS SOURCES:**
+• **Fast:** {clip_fast[:50]}...
+• **Classic:** {clip_classic[:50]}...
+• **Best:** {clip_best[:50]}...
+**⚡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules"""
+            return optimized_prompt, analysis_info, score, breakdown
         except Exception as e:
+            logger.error(f"Ultra supreme generation error: {e}")
+            return f"❌ Error: {str(e)}", "Please try with a different image.", 0, {}
+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:
             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>'
 def clear_outputs():
     gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
+    return "", "", '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</div></div>'
 def create_interface():
     css = """
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
     .gradio-container {
+        max-width: 1600px !important;
         margin: 0 auto !important;
         font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
         background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%) !important;
     .main-header {
         text-align: center;
+        padding: 3rem 0 4rem 0;
+        background: linear-gradient(135deg, #0c0a09 0%, #1c1917 30%, #292524 60%, #44403c 100%);
         color: white;
+        margin: -2rem -2rem 3rem -2rem;
+        border-radius: 0 0 32px 32px;
+        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.25);
+        position: relative;
+        overflow: hidden;
+    }
+    .main-header::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background: linear-gradient(45deg, rgba(59, 130, 246, 0.1) 0%, rgba(147, 51, 234, 0.1) 50%, rgba(236, 72, 153, 0.1) 100%);
+        z-index: 1;
     }
     .main-title {
+        font-size: 4rem !important;
+        font-weight: 900 !important;
+        margin: 0 0 1rem 0 !important;
+        letter-spacing: -0.05em !important;
+        background: linear-gradient(135deg, #60a5fa 0%, #3b82f6 25%, #8b5cf6 50%, #a855f7 75%, #ec4899 100%);
         -webkit-background-clip: text;
         -webkit-text-fill-color: transparent;
         background-clip: text;
+        position: relative;
+        z-index: 2;
     }
     .subtitle {
+        font-size: 1.5rem !important;
+        font-weight: 500 !important;
+        opacity: 0.95 !important;
         margin: 0 !important;
+        position: relative;
+        z-index: 2;
     }
     .prompt-output {
         font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
+        font-size: 15px !important;
+        line-height: 1.8 !important;
         background: linear-gradient(135deg, #ffffff 0%, #f8fafc 100%) !important;
+        border: 2px solid #e2e8f0 !important;
+        border-radius: 20px !important;
+        padding: 2.5rem !important;
+        box-shadow: 0 20px 50px -10px rgba(0, 0, 0, 0.1) !important;
+        transition: all 0.3s ease !important;
+    }
+    .prompt-output:hover {
+        box-shadow: 0 25px 60px -5px rgba(0, 0, 0, 0.15) !important;
+        transform: translateY(-2px) !important;
     }
     """
     with gr.Blocks(
         theme=gr.themes.Soft(),
+        title="🚀 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
                 )
                 analyze_btn = gr.Button(
+                    "🚀 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.
                 """)
             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
                 )
                 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")
         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.
+        **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme