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| import spaces | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| from clip_interrogator import Config, Interrogator | |
| import logging | |
| import os | |
| import warnings | |
| from datetime import datetime | |
| import gc | |
| import re | |
| import math | |
| warnings.filterwarnings("ignore", category=FutureWarning) | |
| warnings.filterwarnings("ignore", category=UserWarning) | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def get_device(): | |
| if torch.cuda.is_available(): | |
| return "cuda" | |
| elif torch.backends.mps.is_available(): | |
| return "mps" | |
| else: | |
| return "cpu" | |
| DEVICE = get_device() | |
| class UltraSupremeAnalyzer: | |
| """ | |
| ULTRA SUPREME ANALYSIS ENGINE - ABSOLUTE MAXIMUM INTELLIGENCE | |
| """ | |
| def __init__(self): | |
| self.forbidden_elements = ["++", "weights", "white background [en dev]"] | |
| self.micro_age_indicators = { | |
| "infant": ["baby", "infant", "newborn", "toddler"], | |
| "child": ["child", "kid", "young", "little", "small", "youth"], | |
| "teen": ["teenager", "teen", "adolescent", "young adult", "student"], | |
| "young_adult": ["young adult", "twenties", "thirty", "youthful", "fresh"], | |
| "middle_aged": ["middle-aged", "forties", "fifties", "mature", "experienced"], | |
| "senior": ["senior", "older", "elderly", "aged", "vintage", "seasoned"], | |
| "elderly": ["elderly", "old", "ancient", "weathered", "aged", "gray", "grey", "white hair", "silver", "wrinkled", "lined", "creased", "time-worn", "distinguished by age"] | |
| } | |
| self.ultra_facial_analysis = { | |
| "eye_features": { | |
| "shape": ["round eyes", "almond eyes", "narrow eyes", "wide eyes", "deep-set eyes", "prominent eyes"], | |
| "expression": ["intense gaze", "piercing stare", "gentle eyes", "wise eyes", "tired eyes", "alert eyes", "contemplative stare", "focused gaze", "distant look"], | |
| "color": ["brown eyes", "blue eyes", "green eyes", "hazel eyes", "dark eyes", "light eyes"], | |
| "condition": ["clear eyes", "bloodshot", "bright eyes", "dull eyes", "sparkling eyes"] | |
| }, | |
| "eyebrow_analysis": ["thick eyebrows", "thin eyebrows", "bushy eyebrows", "arched eyebrows", "straight eyebrows", "gray eyebrows"], | |
| "nose_features": ["prominent nose", "straight nose", "aquiline nose", "small nose", "wide nose", "narrow nose"], | |
| "mouth_expression": { | |
| "shape": ["thin lips", "full lips", "small mouth", "wide mouth"], | |
| "expression": ["slight smile", "serious expression", "frown", "neutral expression", "contemplative look", "stern look", "gentle expression"] | |
| }, | |
| "facial_hair_ultra": { | |
| "beard_types": ["full beard", "goatee", "mustache", "stubble", "clean-shaven", "five o'clock shadow"], | |
| "beard_texture": ["thick beard", "thin beard", "coarse beard", "fine beard", "well-groomed beard", "unkempt beard"], | |
| "beard_color": ["black beard", "brown beard", "gray beard", "grey beard", "silver beard", "white beard", "salt-and-pepper beard", "graying beard"], | |
| "beard_length": ["long beard", "short beard", "trimmed beard", "full-length beard"] | |
| }, | |
| "skin_analysis": ["smooth skin", "weathered skin", "wrinkled skin", "clear skin", "rough skin", "aged skin", "youthful skin", "tanned skin", "pale skin", "olive skin"], | |
| "facial_structure": ["angular face", "round face", "oval face", "square jaw", "defined cheekbones", "high cheekbones", "strong jawline", "soft features", "sharp features"] | |
| } | |
| self.emotion_micro_expressions = { | |
| "primary_emotions": ["happy", "sad", "angry", "fearful", "surprised", "disgusted", "contemptuous"], | |
| "complex_emotions": ["contemplative", "melancholic", "serene", "intense", "peaceful", "troubled", "confident", "uncertain", "wise", "stern", "gentle", "authoritative"], | |
| "emotional_indicators": ["furrowed brow", "raised eyebrows", "squinted eyes", "pursed lips", "relaxed expression", "tense jaw", "soft eyes", "hard stare"] | |
| } | |
| self.cultural_religious_ultra = { | |
| "jewish_orthodox": ["Orthodox Jewish", "Hasidic", "Ultra-Orthodox", "religious Jewish", "traditional Jewish", "devout Jewish"], | |
| "christian": ["Christian", "Catholic", "Protestant", "Orthodox Christian", "religious Christian"], | |
| "muslim": ["Muslim", "Islamic", "religious Muslim", "devout Muslim"], | |
| "buddhist": ["Buddhist", "monk", "religious Buddhist"], | |
| "general_religious": ["religious", "devout", "pious", "spiritual", "faithful", "observant"], | |
| "traditional_clothing": { | |
| "jewish": ["yarmulke", "kippah", "tallit", "tzitzit", "black hat", "Orthodox hat", "religious hat", "traditional Jewish hat"], | |
| "general": ["religious garment", "traditional clothing", "ceremonial dress", "formal religious attire"] | |
| } | |
| } | |
| self.clothing_accessories_ultra = { | |
| "headwear": ["hat", "cap", "beret", "headband", "turban", "hood", "helmet", "crown", "headpiece"], | |
| "eyewear": ["glasses", "spectacles", "sunglasses", "reading glasses", "wire-frame glasses", "thick-rimmed glasses", "designer glasses", "vintage glasses"], | |
| "clothing_types": ["suit", "jacket", "shirt", "dress", "robe", "uniform", "casual wear", "formal wear", "business attire"], | |
| "clothing_colors": ["black", "white", "gray", "blue", "red", "green", "brown", "navy", "dark", "light"], | |
| "clothing_styles": ["formal", "casual", "business", "traditional", "modern", "vintage", "classic", "contemporary"], | |
| "accessories": ["jewelry", "watch", "necklace", "ring", "bracelet", "earrings", "pin", "brooch"] | |
| } | |
| self.environmental_ultra_analysis = { | |
| "indoor_settings": { | |
| "residential": ["home", "house", "apartment", "living room", "bedroom", "kitchen", "dining room"], | |
| "office": ["office", "workplace", "conference room", "meeting room", "boardroom", "desk"], | |
| "institutional": ["school", "hospital", "government building", "court", "library"], | |
| "religious": ["church", "synagogue", "mosque", "temple", "chapel", "sanctuary"], | |
| "commercial": ["store", "restaurant", "hotel", "mall", "shop"] | |
| }, | |
| "outdoor_settings": { | |
| "natural": ["park", "garden", "forest", "beach", "mountain", "countryside", "field"], | |
| "urban": ["street", "city", "downtown", "plaza", "square", "avenue"], | |
| "architectural": ["building", "monument", "bridge", "structure"] | |
| }, | |
| "lighting_ultra": { | |
| "natural_light": ["sunlight", "daylight", "morning light", "afternoon light", "evening light", "golden hour", "blue hour", "overcast light", "window light"], | |
| "artificial_light": ["indoor lighting", "electric light", "lamp light", "overhead lighting", "side lighting", "fluorescent", "LED lighting"], | |
| "dramatic_lighting": ["high contrast", "low key", "high key", "chiaroscuro", "dramatic shadows", "rim lighting", "backlighting", "spotlight"], | |
| "quality": ["soft lighting", "hard lighting", "diffused light", "direct light", "ambient light", "mood lighting"] | |
| } | |
| } | |
| self.pose_body_language_ultra = { | |
| "head_position": ["head up", "head down", "head tilted", "head straight", "head turned", "profile view", "three-quarter view"], | |
| "posture": ["upright posture", "slouched", "relaxed posture", "formal posture", "casual stance", "dignified bearing"], | |
| "hand_positions": ["hands clasped", "hands folded", "hands visible", "hands hidden", "gesturing", "pointing"], | |
| "sitting_positions": ["sitting upright", "leaning forward", "leaning back", "sitting casually", "formal sitting"], | |
| "eye_contact": ["looking at camera", "looking away", "direct gaze", "averted gaze", "looking down", "looking up"], | |
| "overall_demeanor": ["confident", "reserved", "approachable", "authoritative", "gentle", "stern", "relaxed", "tense"] | |
| } | |
| self.composition_photography_ultra = { | |
| "shot_types": ["close-up", "medium shot", "wide shot", "extreme close-up", "portrait shot", "headshot", "bust shot", "full body"], | |
| "angles": ["eye level", "high angle", "low angle", "bird's eye", "worm's eye", "Dutch angle"], | |
| "framing": ["centered", "off-center", "rule of thirds", "tight framing", "loose framing"], | |
| "depth_of_field": ["shallow depth", "deep focus", "bokeh", "sharp focus", "soft focus"], | |
| "camera_movement": ["static", "handheld", "stabilized", "smooth"] | |
| } | |
| self.technical_photography_ultra = { | |
| "camera_systems": { | |
| "professional": ["Phase One XF", "Phase One XT", "Hasselblad X2D", "Fujifilm GFX", "Canon EOS R5", "Nikon Z9"], | |
| "medium_format": ["Phase One", "Hasselblad", "Fujifilm GFX", "Pentax 645"], | |
| "full_frame": ["Canon EOS R", "Nikon Z", "Sony A7", "Leica SL"] | |
| }, | |
| "lenses_ultra": { | |
| "portrait": ["85mm f/1.4", "135mm f/2", "105mm f/1.4", "200mm f/2.8"], | |
| "standard": ["50mm f/1.4", "35mm f/1.4", "24-70mm f/2.8"], | |
| "wide": ["24mm f/1.4", "16-35mm f/2.8", "14mm f/2.8"] | |
| }, | |
| "aperture_settings": ["f/1.4", "f/2", "f/2.8", "f/4", "f/5.6", "f/8"], | |
| "photography_styles": ["portrait photography", "documentary photography", "fine art photography", "commercial photography", "editorial photography"] | |
| } | |
| self.quality_descriptors_ultra = { | |
| "based_on_age": { | |
| "elderly": ["distinguished", "venerable", "dignified", "wise", "experienced", "seasoned", "time-honored", "revered", "weathered", "sage-like"], | |
| "middle_aged": ["professional", "accomplished", "established", "confident", "mature", "refined", "sophisticated"], | |
| "young_adult": ["vibrant", "energetic", "fresh", "youthful", "dynamic", "spirited", "lively"] | |
| }, | |
| "based_on_emotion": { | |
| "contemplative": ["thoughtful", "reflective", "meditative", "introspective"], | |
| "confident": ["assured", "self-possessed", "commanding", "authoritative"], | |
| "gentle": ["kind", "warm", "compassionate", "tender"], | |
| "stern": ["serious", "grave", "solemn", "austere"] | |
| }, | |
| "based_on_setting": { | |
| "formal": ["professional", "official", "ceremonial", "dignified"], | |
| "casual": ["relaxed", "informal", "comfortable", "natural"], | |
| "artistic": ["creative", "expressive", "aesthetic", "artistic"] | |
| } | |
| } | |
| def ultra_supreme_analysis(self, clip_fast, clip_classic, clip_best): | |
| """ULTRA SUPREME ANALYSIS - MAXIMUM POSSIBLE INTELLIGENCE""" | |
| combined_analysis = { | |
| "fast": clip_fast.lower(), | |
| "classic": clip_classic.lower(), | |
| "best": clip_best.lower(), | |
| "combined": f"{clip_fast} {clip_classic} {clip_best}".lower() | |
| } | |
| ultra_result = { | |
| "demographic": {"age_category": None, "age_confidence": 0, "gender": None, "cultural_religious": []}, | |
| "facial_ultra": {"eyes": [], "eyebrows": [], "nose": [], "mouth": [], "facial_hair": [], "skin": [], "structure": []}, | |
| "emotional_state": {"primary_emotion": None, "emotion_confidence": 0, "micro_expressions": [], "overall_demeanor": []}, | |
| "clothing_accessories": {"headwear": [], "eyewear": [], "clothing": [], "accessories": []}, | |
| "environmental": {"setting_type": None, "specific_location": None, "lighting_analysis": [], "atmosphere": []}, | |
| "pose_composition": {"body_language": [], "head_position": [], "eye_contact": [], "posture": []}, | |
| "technical_analysis": {"shot_type": None, "angle": None, "lighting_setup": None, "suggested_equipment": {}}, | |
| "intelligence_metrics": {"total_features_detected": 0, "analysis_depth_score": 0, "cultural_awareness_score": 0, "technical_optimization_score": 0} | |
| } | |
| # ULTRA DEEP AGE ANALYSIS | |
| age_scores = {} | |
| for age_category, indicators in self.micro_age_indicators.items(): | |
| score = sum(1 for indicator in indicators if indicator in combined_analysis["combined"]) | |
| if score > 0: | |
| age_scores[age_category] = score | |
| if age_scores: | |
| ultra_result["demographic"]["age_category"] = max(age_scores, key=age_scores.get) | |
| ultra_result["demographic"]["age_confidence"] = age_scores[ultra_result["demographic"]["age_category"]] | |
| # GENDER DETECTION WITH CONFIDENCE | |
| male_indicators = ["man", "male", "gentleman", "guy", "he", "his", "masculine"] | |
| female_indicators = ["woman", "female", "lady", "she", "her", "feminine"] | |
| male_score = sum(1 for indicator in male_indicators if indicator in combined_analysis["combined"]) | |
| female_score = sum(1 for indicator in female_indicators if indicator in combined_analysis["combined"]) | |
| if male_score > female_score: | |
| ultra_result["demographic"]["gender"] = "man" | |
| elif female_score > male_score: | |
| ultra_result["demographic"]["gender"] = "woman" | |
| # ULTRA CULTURAL/RELIGIOUS ANALYSIS | |
| for culture_type, indicators in self.cultural_religious_ultra.items(): | |
| if isinstance(indicators, list): | |
| for indicator in indicators: | |
| if indicator.lower() in combined_analysis["combined"]: | |
| ultra_result["demographic"]["cultural_religious"].append(indicator) | |
| # COMPREHENSIVE FACIAL FEATURE ANALYSIS | |
| for hair_category, features in self.ultra_facial_analysis["facial_hair_ultra"].items(): | |
| for feature in features: | |
| if feature in combined_analysis["combined"]: | |
| ultra_result["facial_ultra"]["facial_hair"].append(feature) | |
| # Eyes analysis | |
| for eye_category, features in self.ultra_facial_analysis["eye_features"].items(): | |
| for feature in features: | |
| if feature in combined_analysis["combined"]: | |
| ultra_result["facial_ultra"]["eyes"].append(feature) | |
| # EMOTION AND MICRO-EXPRESSION ANALYSIS | |
| emotion_scores = {} | |
| for emotion in self.emotion_micro_expressions["complex_emotions"]: | |
| if emotion in combined_analysis["combined"]: | |
| emotion_scores[emotion] = combined_analysis["combined"].count(emotion) | |
| if emotion_scores: | |
| ultra_result["emotional_state"]["primary_emotion"] = max(emotion_scores, key=emotion_scores.get) | |
| ultra_result["emotional_state"]["emotion_confidence"] = emotion_scores[ultra_result["emotional_state"]["primary_emotion"]] | |
| # CLOTHING AND ACCESSORIES ANALYSIS | |
| for category, items in self.clothing_accessories_ultra.items(): | |
| if isinstance(items, list): | |
| for item in items: | |
| if item in combined_analysis["combined"]: | |
| ultra_result["clothing_accessories"][category].append(item) | |
| # ENVIRONMENTAL ULTRA ANALYSIS | |
| setting_scores = {} | |
| for main_setting, sub_settings in self.environmental_ultra_analysis.items(): | |
| if isinstance(sub_settings, dict): | |
| for sub_type, locations in sub_settings.items(): | |
| score = sum(1 for location in locations if location in combined_analysis["combined"]) | |
| if score > 0: | |
| setting_scores[sub_type] = score | |
| if setting_scores: | |
| ultra_result["environmental"]["setting_type"] = max(setting_scores, key=setting_scores.get) | |
| # LIGHTING ANALYSIS | |
| for light_category, light_types in self.environmental_ultra_analysis["lighting_ultra"].items(): | |
| for light_type in light_types: | |
| if light_type in combined_analysis["combined"]: | |
| ultra_result["environmental"]["lighting_analysis"].append(light_type) | |
| # POSE AND BODY LANGUAGE ANALYSIS | |
| for pose_category, indicators in self.pose_body_language_ultra.items(): | |
| for indicator in indicators: | |
| if indicator in combined_analysis["combined"]: | |
| ultra_result["pose_composition"][pose_category].append(indicator) | |
| # TECHNICAL PHOTOGRAPHY ANALYSIS | |
| for shot_type in self.composition_photography_ultra["shot_types"]: | |
| if shot_type in combined_analysis["combined"]: | |
| ultra_result["technical_analysis"]["shot_type"] = shot_type | |
| break | |
| # CALCULATE INTELLIGENCE METRICS | |
| total_features = sum(len(v) if isinstance(v, list) else (1 if v else 0) for category in ultra_result.values() if isinstance(category, dict) for v in category.values()) | |
| ultra_result["intelligence_metrics"]["total_features_detected"] = total_features | |
| ultra_result["intelligence_metrics"]["analysis_depth_score"] = min(total_features * 5, 100) | |
| ultra_result["intelligence_metrics"]["cultural_awareness_score"] = len(ultra_result["demographic"]["cultural_religious"]) * 20 | |
| return ultra_result | |
| def build_ultra_supreme_prompt(self, ultra_analysis, clip_results): | |
| """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY""" | |
| components = [] | |
| # 1. ULTRA INTELLIGENT ARTICLE SELECTION | |
| subject_desc = [] | |
| if ultra_analysis["demographic"]["cultural_religious"]: | |
| subject_desc.extend(ultra_analysis["demographic"]["cultural_religious"][:1]) | |
| if ultra_analysis["demographic"]["age_category"] and ultra_analysis["demographic"]["age_category"] != "middle_aged": | |
| subject_desc.append(ultra_analysis["demographic"]["age_category"].replace("_", " ")) | |
| if ultra_analysis["demographic"]["gender"]: | |
| subject_desc.append(ultra_analysis["demographic"]["gender"]) | |
| if subject_desc: | |
| full_subject = " ".join(subject_desc) | |
| article = "An" if full_subject[0].lower() in 'aeiou' else "A" | |
| else: | |
| article = "A" | |
| components.append(article) | |
| # 2. ULTRA CONTEXTUAL ADJECTIVES (max 2-3 per Flux rules) | |
| adjectives = [] | |
| # Age-based adjectives | |
| age_cat = ultra_analysis["demographic"]["age_category"] | |
| if age_cat and age_cat in self.quality_descriptors_ultra["based_on_age"]: | |
| adjectives.extend(self.quality_descriptors_ultra["based_on_age"][age_cat][:2]) | |
| # Emotion-based adjectives | |
| emotion = ultra_analysis["emotional_state"]["primary_emotion"] | |
| if emotion and emotion in self.quality_descriptors_ultra["based_on_emotion"]: | |
| adjectives.extend(self.quality_descriptors_ultra["based_on_emotion"][emotion][:1]) | |
| # Default if none found | |
| if not adjectives: | |
| adjectives = ["distinguished", "professional"] | |
| components.extend(adjectives[:2]) # Flux rule: max 2-3 adjectives | |
| # 3. ULTRA ENHANCED SUBJECT | |
| if subject_desc: | |
| components.append(" ".join(subject_desc)) | |
| else: | |
| components.append("person") | |
| # 4. ULTRA DETAILED FACIAL FEATURES | |
| facial_details = [] | |
| # Eyes | |
| if ultra_analysis["facial_ultra"]["eyes"]: | |
| eye_desc = ultra_analysis["facial_ultra"]["eyes"][0] | |
| facial_details.append(f"with {eye_desc}") | |
| # Facial hair with ultra detail | |
| if ultra_analysis["facial_ultra"]["facial_hair"]: | |
| beard_details = ultra_analysis["facial_ultra"]["facial_hair"] | |
| if any("silver" in detail or "gray" in detail or "grey" in detail for detail in beard_details): | |
| facial_details.append("with a distinguished silver beard") | |
| elif any("beard" in detail for detail in beard_details): | |
| facial_details.append("with a full well-groomed beard") | |
| if facial_details: | |
| components.extend(facial_details) | |
| # 5. CLOTHING AND ACCESSORIES ULTRA | |
| clothing_details = [] | |
| # Eyewear | |
| if ultra_analysis["clothing_accessories"]["eyewear"]: | |
| eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0] | |
| clothing_details.append(f"wearing {eyewear}") | |
| # Headwear | |
| if ultra_analysis["clothing_accessories"]["headwear"]: | |
| headwear = ultra_analysis["clothing_accessories"]["headwear"][0] | |
| if ultra_analysis["demographic"]["cultural_religious"]: | |
| clothing_details.append("wearing a traditional black hat") | |
| else: | |
| clothing_details.append(f"wearing a {headwear}") | |
| if clothing_details: | |
| components.extend(clothing_details) | |
| # 6. ULTRA POSE AND BODY LANGUAGE | |
| pose_description = "positioned with natural dignity" | |
| if ultra_analysis["pose_composition"]["posture"]: | |
| posture = ultra_analysis["pose_composition"]["posture"][0] | |
| pose_description = f"maintaining {posture}" | |
| elif ultra_analysis["technical_analysis"]["shot_type"] == "portrait": | |
| pose_description = "captured in contemplative portrait pose" | |
| components.append(pose_description) | |
| # 7. ULTRA ENVIRONMENTAL CONTEXT | |
| environment_desc = "in a thoughtfully composed environment" | |
| if ultra_analysis["environmental"]["setting_type"]: | |
| setting_map = { | |
| "residential": "in an intimate home setting", | |
| "office": "in a professional office environment", | |
| "religious": "in a sacred traditional space", | |
| "formal": "in a distinguished formal setting" | |
| } | |
| environment_desc = setting_map.get(ultra_analysis["environmental"]["setting_type"], "in a carefully arranged professional setting") | |
| components.append(environment_desc) | |
| # 8. ULTRA SOPHISTICATED LIGHTING | |
| lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture" | |
| if ultra_analysis["environmental"]["lighting_analysis"]: | |
| primary_light = ultra_analysis["environmental"]["lighting_analysis"][0] | |
| if "dramatic" in primary_light: | |
| lighting_desc = "bathed in dramatic chiaroscuro lighting that creates compelling depth and shadow play" | |
| elif "natural" in primary_light or "window" in primary_light: | |
| lighting_desc = "graced by gentle natural lighting that brings out intricate facial details and warmth" | |
| elif "soft" in primary_light: | |
| lighting_desc = "softly illuminated to reveal nuanced expressions and character" | |
| components.append(lighting_desc) | |
| # 9. ULTRA TECHNICAL SPECIFICATIONS | |
| if ultra_analysis["technical_analysis"]["shot_type"] in ["portrait", "headshot", "close-up"]: | |
| camera_setup = "Shot on Phase One XF IQ4, 85mm f/1.4 lens, f/2.8 aperture" | |
| elif ultra_analysis["demographic"]["cultural_religious"]: | |
| camera_setup = "Shot on Hasselblad X2D, 90mm lens, f/2.8 aperture" | |
| else: | |
| camera_setup = "Shot on Phase One XF, 80mm lens, f/4 aperture" | |
| components.append(camera_setup) | |
| # 10. ULTRA QUALITY DESIGNATION | |
| quality_designation = "professional portrait photography" | |
| if ultra_analysis["demographic"]["cultural_religious"]: | |
| quality_designation = "fine art documentary photography" | |
| elif ultra_analysis["emotional_state"]["primary_emotion"]: | |
| quality_designation = "expressive portrait photography" | |
| components.append(quality_designation) | |
| # ULTRA FINAL ASSEMBLY | |
| prompt = ", ".join(components) | |
| # Ultra cleaning and optimization | |
| prompt = re.sub(r'\s+', ' ', prompt) | |
| prompt = re.sub(r',\s*,+', ',', prompt) | |
| prompt = re.sub(r'\s*,\s*', ', ', prompt) | |
| prompt = prompt.replace(" ,", ",") | |
| if prompt: | |
| prompt = prompt[0].upper() + prompt[1:] | |
| return prompt | |
| def calculate_ultra_supreme_score(self, prompt, ultra_analysis): | |
| """ULTRA SUPREME INTELLIGENCE SCORING""" | |
| score = 0 | |
| breakdown = {} | |
| # Structure Excellence (15 points) | |
| structure_score = 0 | |
| if prompt.startswith(("A", "An")): | |
| structure_score += 5 | |
| if prompt.count(",") >= 8: | |
| structure_score += 10 | |
| score += structure_score | |
| breakdown["structure"] = structure_score | |
| # Feature Detection Depth (25 points) | |
| features_score = min(ultra_analysis["intelligence_metrics"]["total_features_detected"] * 2, 25) | |
| score += features_score | |
| breakdown["features"] = features_score | |
| # Cultural/Religious Awareness (20 points) | |
| cultural_score = min(len(ultra_analysis["demographic"]["cultural_religious"]) * 10, 20) | |
| score += cultural_score | |
| breakdown["cultural"] = cultural_score | |
| # Emotional Intelligence (15 points) | |
| emotion_score = 0 | |
| if ultra_analysis["emotional_state"]["primary_emotion"]: | |
| emotion_score += 10 | |
| if ultra_analysis["emotional_state"]["emotion_confidence"] > 1: | |
| emotion_score += 5 | |
| score += emotion_score | |
| breakdown["emotional"] = emotion_score | |
| # Technical Sophistication (15 points) | |
| tech_score = 0 | |
| if "Phase One" in prompt or "Hasselblad" in prompt: | |
| tech_score += 5 | |
| if any(aperture in prompt for aperture in ["f/1.4", "f/2.8", "f/4"]): | |
| tech_score += 5 | |
| if any(lens in prompt for lens in ["85mm", "90mm", "80mm"]): | |
| tech_score += 5 | |
| score += tech_score | |
| breakdown["technical"] = tech_score | |
| # Environmental Context (10 points) | |
| env_score = 0 | |
| if ultra_analysis["environmental"]["setting_type"]: | |
| env_score += 5 | |
| if ultra_analysis["environmental"]["lighting_analysis"]: | |
| env_score += 5 | |
| score += env_score | |
| breakdown["environmental"] = env_score | |
| return min(score, 100), breakdown | |
| class UltraSupremeOptimizer: | |
| def __init__(self): | |
| self.interrogator = None | |
| self.analyzer = UltraSupremeAnalyzer() | |
| self.usage_count = 0 | |
| self.device = DEVICE | |
| self.is_initialized = False | |
| def initialize_model(self): | |
| if self.is_initialized: | |
| return True | |
| try: | |
| config = Config( | |
| clip_model_name="ViT-L-14/openai", | |
| download_cache=True, | |
| chunk_size=2048, | |
| quiet=True, | |
| device=self.device | |
| ) | |
| self.interrogator = Interrogator(config) | |
| self.is_initialized = True | |
| if self.device == "cpu": | |
| gc.collect() | |
| else: | |
| torch.cuda.empty_cache() | |
| return True | |
| except Exception as e: | |
| logger.error(f"Initialization error: {e}") | |
| return False | |
| def optimize_image(self, image): | |
| if image is None: | |
| return None | |
| if isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| elif not isinstance(image, Image.Image): | |
| image = Image.open(image) | |
| if image.mode != 'RGB': | |
| image = image.convert('RGB') | |
| max_size = 768 if self.device != "cpu" else 512 | |
| if image.size[0] > max_size or image.size[1] > max_size: | |
| image.thumbnail((max_size, max_size), Image.Resampling.LANCZOS) | |
| return image | |
| def generate_ultra_supreme_prompt(self, image): | |
| try: | |
| if not self.is_initialized: | |
| if not self.initialize_model(): | |
| return "❌ Model initialization failed.", "Please refresh and try again.", 0, {} | |
| if image is None: | |
| return "❌ Please upload an image.", "No image provided.", 0, {} | |
| self.usage_count += 1 | |
| image = self.optimize_image(image) | |
| if image is None: | |
| return "❌ Image processing failed.", "Invalid image format.", 0, {} | |
| start_time = datetime.now() | |
| # ULTRA SUPREME TRIPLE CLIP ANALYSIS | |
| logger.info("ULTRA SUPREME ANALYSIS - Maximum intelligence deployment") | |
| clip_fast = self.interrogator.interrogate_fast(image) | |
| clip_classic = self.interrogator.interrogate_classic(image) | |
| clip_best = self.interrogator.interrogate(image) | |
| logger.info(f"ULTRA CLIP Results:\nFast: {clip_fast}\nClassic: {clip_classic}\nBest: {clip_best}") | |
| # ULTRA SUPREME ANALYSIS | |
| ultra_analysis = self.analyzer.ultra_supreme_analysis(clip_fast, clip_classic, clip_best) | |
| # BUILD ULTRA SUPREME FLUX PROMPT | |
| optimized_prompt = self.analyzer.build_ultra_supreme_prompt(ultra_analysis, [clip_fast, clip_classic, clip_best]) | |
| # CALCULATE ULTRA SUPREME SCORE | |
| score, breakdown = self.analyzer.calculate_ultra_supreme_score(optimized_prompt, ultra_analysis) | |
| end_time = datetime.now() | |
| duration = (end_time - start_time).total_seconds() | |
| # Memory cleanup | |
| if self.device == "cpu": | |
| gc.collect() | |
| else: | |
| torch.cuda.empty_cache() | |
| # ULTRA COMPREHENSIVE ANALYSIS REPORT | |
| gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU" | |
| # Format detected elements | |
| features = ", ".join(ultra_analysis["facial_ultra"]["facial_hair"]) if ultra_analysis["facial_ultra"]["facial_hair"] else "None detected" | |
| cultural = ", ".join(ultra_analysis["demographic"]["cultural_religious"]) if ultra_analysis["demographic"]["cultural_religious"] else "None detected" | |
| clothing = ", ".join(ultra_analysis["clothing_accessories"]["eyewear"] + ultra_analysis["clothing_accessories"]["headwear"]) if ultra_analysis["clothing_accessories"]["eyewear"] or ultra_analysis["clothing_accessories"]["headwear"] else "None detected" | |
| analysis_info = f"""**🚀 ULTRA SUPREME ANALYSIS COMPLETE** | |
| **Processing:** {gpu_status} • {duration:.1f}s • Triple CLIP Ultra Intelligence | |
| **Ultra Score:** {score}/100 • Breakdown: Structure({breakdown.get('structure',0)}) Features({breakdown.get('features',0)}) Cultural({breakdown.get('cultural',0)}) Emotional({breakdown.get('emotional',0)}) Technical({breakdown.get('technical',0)}) | |
| **Generation:** #{self.usage_count} | |
| **🧠 ULTRA DEEP DETECTION:** | |
| - **Age Category:** {ultra_analysis["demographic"].get("age_category", "Unspecified").replace("_", " ").title()} (Confidence: {ultra_analysis["demographic"].get("age_confidence", 0)}) | |
| - **Cultural Context:** {cultural} | |
| - **Facial Features:** {features} | |
| - **Accessories:** {clothing} | |
| - **Setting:** {ultra_analysis["environmental"].get("setting_type", "Standard").title()} | |
| - **Emotion:** {ultra_analysis["emotional_state"].get("primary_emotion", "Neutral").title()} | |
| - **Total Features:** {ultra_analysis["intelligence_metrics"]["total_features_detected"]} | |
| **📊 CLIP ANALYSIS SOURCES:** | |
| - **Fast:** {clip_fast[:50]}... | |
| - **Classic:** {clip_classic[:50]}... | |
| - **Best:** {clip_best[:50]}... | |
| **⚡ ULTRA OPTIMIZATION:** Applied absolute maximum depth analysis with Pariente AI research rules""" | |
| return optimized_prompt, analysis_info, score, breakdown | |
| except Exception as e: | |
| logger.error(f"Ultra supreme generation error: {e}") | |
| return f"❌ Error: {str(e)}", "Please try with a different image.", 0, {} | |
| # Initialize the optimizer | |
| optimizer = UltraSupremeOptimizer() | |
| def process_ultra_supreme_analysis(image): | |
| """Ultra supreme analysis wrapper""" | |
| try: | |
| prompt, info, score, breakdown = optimizer.generate_ultra_supreme_prompt(image) | |
| # Ultra enhanced score display | |
| if score >= 95: | |
| color = "#059669" | |
| grade = "LEGENDARY" | |
| elif score >= 90: | |
| color = "#10b981" | |
| grade = "EXCELLENT" | |
| elif score >= 80: | |
| color = "#22c55e" | |
| grade = "VERY GOOD" | |
| elif score >= 70: | |
| color = "#f59e0b" | |
| grade = "GOOD" | |
| elif score >= 60: | |
| color = "#f97316" | |
| grade = "FAIR" | |
| else: | |
| color = "#ef4444" | |
| grade = "NEEDS WORK" | |
| score_html = f''' | |
| <div style="text-align: center; padding: 2rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 3px solid {color}; border-radius: 16px; margin: 1rem 0; box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);"> | |
| <div style="font-size: 3rem; font-weight: 800; color: {color}; margin: 0; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">{score}</div> | |
| <div style="font-size: 1.25rem; color: #15803d; margin: 0.5rem 0; text-transform: uppercase; letter-spacing: 0.1em; font-weight: 700;">{grade}</div> | |
| <div style="font-size: 1rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em; font-weight: 500;">Ultra Supreme Intelligence Score</div> | |
| </div> | |
| ''' | |
| return prompt, info, score_html | |
| except Exception as e: | |
| logger.error(f"Ultra supreme wrapper error: {e}") | |
| return "❌ 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") | |
| # 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 | |
| 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** | |
| """) | |
| return interface | |
| # Launch the application | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=True, | |
| show_error=True | |
| ) |