Create analyzer.py

analyzer.py

@@ -0,0 +1,378 @@
+"""
+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