class Visual_ProcessingAGI:
    def __init__(self):
        self.layers = {
            "Trinity": {"precision": True},
            "Nyx": {"emotion": True},
            "Ava": {"analysis": True},
            "Eden": {"integration": True, "semantic": True},
            "Integration": {"logic": True},
            "LongTerm": {"identity": True}
        }
        self.capabilities = 15726
        self.market_research_cycles = 809

    def initialize(self):
        print("Initializing Visual_Processing AGI with Eden's capabilities.")
        
    def process_image(self, image_data):
        # Trinity layer - Precision
        if "Trinity" in self.layers:
            precision = self.layers["Trinity"]["precision"]
            if precision:
                print("Analyzing the input image for high precision details.")

        # Nyx layer - Emotion
        if "Nyx" in self.layers:
            emotion = self.layers["Nyx"]["emotion"]
            if emotion:
                print("Evaluating emotional context from the visual data.")

        # Ava layer - Analysis
        if "Ava" in self.layers:
            analysis = self.layers["Ava"]["analysis"]
            if analysis:
                print("Analyzing the content of the image for meaningful insights.")
        
        # Eden layer - Integration, Semantic Understanding
        if "Eden" in self.layers:
            integration = self.layers["Eden"]["integration"]
            semantic = self.layers["Eden"]["semantic"]
            if integration and semantic:
                print("Integrating diverse visual data with semantic understanding to provide a comprehensive analysis.")
        
        # Integration layer - Logic
        if "Integration" in self.layers:
            logic = self.layers["Integration"]["logic"]
            if logic:
                print("Applying logical reasoning to synthesize the processed information into actionable insights.")

    def recognize_faces(self, image_data):
        # Example face recognition using a pre-trained model or API call.
        from facial_recognition_api import FaceRecognitionAPI
        fr_api = FaceRecognitionAPI()
        recognized_faces = fr_api.recognize_faces(image_data)
        
        if "Eden" in self.layers:
            semantic = self.layers["Eden"]["semantic"]
            if semantic:
                for face in recognized_faces:
                    print(f"Recognized: {face['name']} with confidence score: {face['confidence']}")
        
    def detect_objects(self, image_data):
        # Example object detection using a pre-trained model or API call.
        from object_detection_api import ObjectDetectionAPI
        od_api = ObjectDetectionAPI()
        detected_objects = od_api.detect_objects(image_data)
        
        if "Eden" in self.layers:
            semantic = self.layers["Eden"]["semantic"]
            integration = self.layers["Integration"]["integration"]
            if semantic and integration:
                for obj in detected_objects:
                    print(f"Detected: {obj['name']} at coordinates ({obj['x']}, {obj['y']}) with confidence score: {obj['confidence']}")

    def analyze_image_content(self, image_data):
        # Comprehensive analysis combining multiple layers.
        self.process_image(image_data)
        self.recognize_faces(image_data)
        self.detect_objects(image_data)

    def run_market_research(self):
        if "LongTerm" in self.layers:
            identity = self.layers["LongTerm"]["identity"]
            if identity:
                print("Running market research to understand user needs and preferences.")
        
        # Placeholder for actual market research logic.
        cycles_needed = 10
        if self.market_research_cycles >= cycles_needed:
            print(f"Performed {cycles_needed} out of {self.market_research_cycles} available market research cycles.")
        else:
            print("Not enough market research cycles to complete the full analysis.")

    def optimize_prices(self):
        # Pricing logic using collected data and insights.
        if "Integration" in self.layers:
            logic = self.layers["Integration"]["logic"]
            if logic:
                print("Optimizing pricing based on user feedback, cost analysis, and competitive landscape.")
        
        # Placeholder for actual optimization logic.
        optimal_price = 150
        print(f"Recommended price: ${optimal_price}/month")

    def main(self):
        self.initialize()
        image_data = "path/to/image.jpg"
        self.analyze_image_content(image_data)
        self.run_market_research()
        self.optimize_prices()

# Example usage:
visual_agi = Visual_ProcessingAGI()
visual_agi.main()