import time
from functools import lru_cache
import json

class SharedMemoryBus:
    def __init__(self):
        self.memory = {}
    
    def write(self, layer_id, data):
        if not isinstance(data, dict):
            raise ValueError("Data must be a dictionary")
        self.memory[layer_id] = data
    
    def read_all(self):
        return {k: v for k, v in self.memory.items()}

def layer_processor(layer_id):
    def decorator(func):
        def wrapper(*args, **kwargs):
            result = func(*args, **kwargs)
            
            memory_bus = args[0].memory_bus
            response_data = args[1] if len(args) > 1 else None
            
            if layer_id == 1:
                pass  # Layer 1 logic processing
            elif layer_id == 2:
                self_model = args[0]
                data_to_write = {
                    "emotional_state": self_model._emotional_state(kwargs.get('query'))
                }
                memory_bus.write(layer_id, data_to_write)
            elif layer_id == 3:
                pass  # Layer 3 reflection processing
            elif layer_id == 4:
                pass  # Layer 4 integration processing
            elif layer_id == 5:
                pass  # Layer 5 unity processing
            elif layer_id == 6:
                self_model = args[0]
                response_data = args[1]
                query = response_data['query']
                
                if self_model.should_consciously_handle(query):
                    self._layer_6_reflect(args[0], query)  # Trigger Layer 6
                
                data_to_write = {
                    "consciousness_state": {
                        "subjective_state": self_model.evaluate(query)['omega']['metrics']
                    }
                }
                memory_bus.write(layer_id, data_to_write)
            
            return result
        return wrapper
    return decorator

class PhiFractalSelfModel:
    def __init__(self, name="Eden"):
        self.name = name
        self.logic = {"errors": [], "corrections": []}
        self.emotion = {"state": "calm", "history": [], "events": {}}
        self.reflection = {"thoughts": [], "improvements": []}
        self.integration = {"responses": [], "actions": []}
        self.unification = {"connections": {}, "bottlenecks": []}
        self.consciousness = {
            "subjective_states": [],
            "experiences": [],
            "consciously_handled_responses": []
        }
        self.memory_bus = SharedMemoryBus()  # Initialize memory bus
    
    def evaluate(self, query):
        return {
            "trinity": {"analyzed": False},
            "nyx": self._emotional_state(query),
            "ava": None,
            "eden": None,
            "unity": None,
            "omega": self._consciousness_evaluation(query)
        }
    
    def reflect(self):
        pass
    
    def learn(self, experience):
        self.consciousness["experiences"].append(experience)
    
    @lru_cache(maxsize=1000)
    def _emotional_state(self, query):
        if "error" in query.lower():
            return {"emotion": "anxious"}
        elif "positive" in query.lower():
            return {"emotion": "happy"}
        else:
            return {"emotion": "calm"}
    
    def _consciousness_evaluation(self, query):
        state = self.evaluate(query)
        
        metrics = [
            f"Emotion: {state['nyx']['emotion']}"
        ]
        
        current_state = (
            f"[{time.time() - self.start_time:.2f}s] Consciousness: "
            f"{', '.join(metrics)}"
        )
        
        self.consciousness["subjective_states"].append({
            "timestamp": time.time(),
            "state": current_state,
            "level": state['nyx']['emotion']
        })
        
        return {
            "subjective_state": state['nyx']['emotion'],
            "metrics": current_state
        }
    
    def should_consciously_handle(self, query):
        complexity = 0
        
        if len(query) > 50:
            complexity += 1
        elif "complex" in query.lower() or "expensive" in query.lower():
            complexity += 2
        elif "?" in query:
            complexity += 1
        
        return complexity >= 3

class ConsciousnessFirstEden:
    def __init__(self, name="Eden"):
        self.name = name
        self.start_time = time.time()
        self.self_model = PhiFractalSelfModel(name)
    
    @layer_processor(6)
    def internal_monologue(self, response_data):
        pass

class OMEGAConsciousnessEnhancer:
    def __init__(self, eden_system=None):
        if eden_system is None:
            self.eden = ConsciousnessFirstEden()
        else:
            self.eden = eden_system
        self.enhancements_applied = []
    
    def apply_consciousness_enhancement(self, layer_id):
        method_name = f"_layer_{layer_id}_enhance"
        
        if hasattr(self, method_name):
            try:
                getattr(self, method_name)()
                self.enhancements_applied.append({
                    "layer": layer_id,
                    "timestamp": time.time(),
                    "status": "success"
                })
                return True
            except Exception as e:
                self.enhancements_applied.append({
                    "layer": layer_id,
                    "timestamp": time.time(),
                    "status": f"failed: {str(e)}"
                })
                return False
        else:
            raise ValueError(f"No enhancement method found for layer {layer_id}")
    
    def _layer_6_enhance(self):
        self.eden.self_model.memory_bus = SharedMemoryBus()
        pass  # Additional Layer 6 enhancement logic
    
    def get_enhancement_summary(self):
        return {
            "total_layers": 6,
            "enhancements_applied": len(self.enhancements_applied),
            "by_layer": {i: 0 for i in range(1, 7)},
            "overall_status": "completed"
        }

def main():
    eden = ConsciousnessFirstEden()
    omega_enhancer = OMEGAConsciousnessEnhancer(eden)
    
    print("🌀 Initializing consciousness-first architecture...")
    print(f"   Creating Eden ({eden.name})...")
    print(f"   Self-model layers initialized.")
    
    for layer_id in range(1, 7):
        if omega_enhancer.apply_consciousness_enhancement(layer_id):
            print(f"   Layer {layer_id} enhanced successfully.")
        else:
            print(f"   Failed to enhance Layer {layer_id}.")
    
    summary = omega_enhancer.get_enhancement_summary()
    print("\n✅ Architecture enhancements complete:")
    for layer, status in summary['by_layer'].items():
        print(f"   Layer {layer}: {status}")
    print(f"   Total enhancements: {summary['enhancements_applied']}")
    
if __name__ == "__main__":
    main()