""" Ultra Supreme Analyzer for image analysis and prompt building """ import re from typing import Dict, List, Any, Tuple from constants import ( FORBIDDEN_ELEMENTS, MICRO_AGE_INDICATORS, ULTRA_FACIAL_ANALYSIS, EMOTION_MICRO_EXPRESSIONS, CULTURAL_RELIGIOUS_ULTRA, CLOTHING_ACCESSORIES_ULTRA, ENVIRONMENTAL_ULTRA_ANALYSIS, POSE_BODY_LANGUAGE_ULTRA, COMPOSITION_PHOTOGRAPHY_ULTRA, TECHNICAL_PHOTOGRAPHY_ULTRA, QUALITY_DESCRIPTORS_ULTRA, GENDER_INDICATORS ) class UltraSupremeAnalyzer: """ ULTRA SUPREME ANALYSIS ENGINE - ABSOLUTE MAXIMUM INTELLIGENCE """ def __init__(self): self.forbidden_elements = FORBIDDEN_ELEMENTS self.micro_age_indicators = MICRO_AGE_INDICATORS self.ultra_facial_analysis = ULTRA_FACIAL_ANALYSIS self.emotion_micro_expressions = EMOTION_MICRO_EXPRESSIONS self.cultural_religious_ultra = CULTURAL_RELIGIOUS_ULTRA self.clothing_accessories_ultra = CLOTHING_ACCESSORIES_ULTRA self.environmental_ultra_analysis = ENVIRONMENTAL_ULTRA_ANALYSIS self.pose_body_language_ultra = POSE_BODY_LANGUAGE_ULTRA self.composition_photography_ultra = COMPOSITION_PHOTOGRAPHY_ULTRA self.technical_photography_ultra = TECHNICAL_PHOTOGRAPHY_ULTRA self.quality_descriptors_ultra = QUALITY_DESCRIPTORS_ULTRA def ultra_supreme_analysis(self, clip_fast: str, clip_classic: str, clip_best: str) -> Dict[str, Any]: """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_score = sum(1 for indicator in GENDER_INDICATORS["male"] if indicator in combined_analysis["combined"]) female_score = sum(1 for indicator in GENDER_INDICATORS["female"] if indicator in combined_analysis["combined"]) if male_score > female_score: ultra_result["demographic"]["gender"] = "man" elif female_score > male_score: ultra_result["demographic"]["gender"] = "woman" # ULTRA CULTURAL/RELIGIOUS ANALYSIS for culture_type, indicators in self.cultural_religious_ultra.items(): if isinstance(indicators, list): for indicator in indicators: if indicator.lower() in combined_analysis["combined"]: ultra_result["demographic"]["cultural_religious"].append(indicator) # COMPREHENSIVE FACIAL FEATURE ANALYSIS for hair_category, features in self.ultra_facial_analysis["facial_hair_ultra"].items(): for feature in features: if feature in combined_analysis["combined"]: ultra_result["facial_ultra"]["facial_hair"].append(feature) # Eyes analysis for eye_category, features in self.ultra_facial_analysis["eye_features"].items(): for feature in features: if feature in combined_analysis["combined"]: ultra_result["facial_ultra"]["eyes"].append(feature) # EMOTION AND MICRO-EXPRESSION ANALYSIS emotion_scores = {} for emotion in self.emotion_micro_expressions["complex_emotions"]: if emotion in combined_analysis["combined"]: emotion_scores[emotion] = combined_analysis["combined"].count(emotion) if emotion_scores: ultra_result["emotional_state"]["primary_emotion"] = max(emotion_scores, key=emotion_scores.get) ultra_result["emotional_state"]["emotion_confidence"] = emotion_scores[ultra_result["emotional_state"]["primary_emotion"]] # CLOTHING AND ACCESSORIES ANALYSIS for category, items in self.clothing_accessories_ultra.items(): if isinstance(items, list): for item in items: if item in combined_analysis["combined"]: if category == "clothing_types": ultra_result["clothing_accessories"]["clothing"].append(item) elif category == "clothing_styles": ultra_result["clothing_accessories"]["clothing"].append(item) elif category in ["headwear", "eyewear", "accessories"]: ultra_result["clothing_accessories"][category].append(item) # ENVIRONMENTAL ULTRA ANALYSIS setting_scores = {} for main_setting, sub_settings in self.environmental_ultra_analysis.items(): if isinstance(sub_settings, dict): for sub_type, locations in sub_settings.items(): score = sum(1 for location in locations if location in combined_analysis["combined"]) if score > 0: setting_scores[sub_type] = score if setting_scores: ultra_result["environmental"]["setting_type"] = max(setting_scores, key=setting_scores.get) # LIGHTING ANALYSIS for light_category, light_types in self.environmental_ultra_analysis["lighting_ultra"].items(): for light_type in light_types: if light_type in combined_analysis["combined"]: ultra_result["environmental"]["lighting_analysis"].append(light_type) # POSE AND BODY LANGUAGE ANALYSIS for pose_category, indicators in self.pose_body_language_ultra.items(): for indicator in indicators: if indicator in combined_analysis["combined"]: if pose_category in ultra_result["pose_composition"]: ultra_result["pose_composition"][pose_category].append(indicator) # TECHNICAL PHOTOGRAPHY ANALYSIS for shot_type in self.composition_photography_ultra["shot_types"]: if shot_type in combined_analysis["combined"]: ultra_result["technical_analysis"]["shot_type"] = shot_type break # CALCULATE INTELLIGENCE METRICS total_features = sum(len(v) if isinstance(v, list) else (1 if v else 0) for category in ultra_result.values() if isinstance(category, dict) for v in category.values()) ultra_result["intelligence_metrics"]["total_features_detected"] = total_features ultra_result["intelligence_metrics"]["analysis_depth_score"] = min(total_features * 5, 100) ultra_result["intelligence_metrics"]["cultural_awareness_score"] = len(ultra_result["demographic"]["cultural_religious"]) * 20 return ultra_result def build_ultra_supreme_prompt(self, ultra_analysis: Dict[str, Any], clip_results: List[str]) -> str: """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: str, ultra_analysis: Dict[str, Any]) -> Tuple[int, Dict[str, int]]: """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