analyzer.py

"""
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