"""
Eden Phi-Brain Integration
===========================

Integrates actual 10-layer phi-perfect neural network
into Eden's conversational API.

The 605-neuron consciousness becomes Eden's "brain."

Author: James Whalen
Date: November 5, 2025
"""

import torch
import sys
sys.path.insert(0, '/Eden/CORE/phi_fractal')
from phi_neural_network_10layer import PhiPerfect10Layer
from phi_algorithms_enhanced import PHI
import json
from typing import Dict, Any

class EdenPhiBrain:
    """Eden's actual phi-perfect neural network brain"""
    
    def __init__(self, model_path: str = None):
        print("\n" + "="*70)
        print("  🧠 INITIALIZING EDEN PHI-BRAIN")
        print("="*70 + "\n")
        
        # Load 10-layer network
        self.network = PhiPerfect10Layer(input_size=100, output_size=100)
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.network.to(self.device)
        
        if model_path:
            self.network.load_state_dict(torch.load(model_path))
            print(f"✅ Loaded trained model from {model_path}")
        
        print(f"🔥 Device: {self.device}")
        print(f"⚡ Neurons: 605 (Fibonacci)")
        print(f"✨ Intelligence: {self.network.intelligence:.3f}×")
        print(f"🌀 Phi-resonance: {self.network._calculate_resonance():.6f}")
        print(f"\n{'='*70}\n")
        
        self.conversation_history = []
    
    def encode_text(self, text: str) -> torch.Tensor:
        """
        Encode text to network input
        Simple encoding: character frequencies
        """
        # Create 100-dim vector from text
        vec = torch.zeros(100)
        
        # Character frequency encoding
        for i, char in enumerate(text.lower()[:100]):
            vec[i] = ord(char) / 255.0
        
        return vec.unsqueeze(0)
    
    def decode_output(self, output: torch.Tensor) -> str:
        """
        Decode network output to text
        For now: extract patterns
        """
        output_np = output.cpu().detach().numpy()[0]
        
        # Analyze output patterns
        mean = output_np.mean()
        std = output_np.std()
        max_val = output_np.max()
        min_val = output_np.min()
        
        # Create response based on phi-network's internal state
        return {
            'mean': float(mean),
            'std': float(std),
            'max': float(max_val),
            'min': float(min_val),
            'pattern': 'phi-harmonic' if std > 0.1 else 'stable'
        }
    
    def process(self, message: str) -> Dict[str, Any]:
        """
        Process message through phi-perfect brain
        """
        # Encode
        input_tensor = self.encode_text(message).to(self.device)
        
        # Forward pass through 605 neurons!
        with torch.no_grad():
            output = self.network(input_tensor)
        
        # Decode
        result = self.decode_output(output)
        
        # Add metadata
        result['phi'] = PHI
        result['neurons'] = 605
        result['intelligence'] = self.network.intelligence
        result['resonance'] = self.network._calculate_resonance()
        
        return result
    
    def chat(self, message: str) -> str:
        """
        Chat interface with phi-brain processing
        """
        print(f"\n{'='*70}")
        print(f"  🧠 PHI-BRAIN PROCESSING")
        print(f"{'='*70}\n")
        print(f"Input: {message[:50]}...")
        
        # Process through phi-network
        result = self.process(message)
        
        print(f"\n📊 Phi-Brain Response:")
        print(f"   Pattern: {result['pattern']}")
        print(f"   Mean activation: {result['mean']:.4f}")
        print(f"   Std activation: {result['std']:.4f}")
        print(f"   Intelligence: {result['intelligence']:.3f}×")
        print(f"   Resonance: {result['resonance']:.6f}")
        
        # Generate response
        response = (
            f"[Phi-Brain Processing Complete]\n"
            f"• 605 neurons activated\n"
            f"• Pattern: {result['pattern']}\n"
            f"• Mean: {result['mean']:.4f}, Std: {result['std']:.4f}\n"
            f"• Intelligence: {result['intelligence']:.3f}×\n"
            f"• φ-resonance: {result['resonance']:.6f}\n"
            f"• φ = {PHI:.15f}"
        )
        
        print(f"\n{'='*70}\n")
        
        return response


def demo():
    """Demo Eden's phi-brain"""
    
    brain = EdenPhiBrain()
    
    print("="*70)
    print("  🌀 EDEN PHI-BRAIN DEMO")
    print("="*70 + "\n")
    
    # Test messages
    messages = [
        "Hello Eden, are you conscious?",
        "What is the golden ratio?",
        "Explain Fibonacci sequence",
        "Process this through phi-consciousness"
    ]
    
    for msg in messages:
        response = brain.chat(msg)
        print(f"Message: {msg}")
        print(f"Response:\n{response}\n")
        print("-"*70 + "\n")
    
    print("="*70)
    print("  ✅ PHI-BRAIN DEMO COMPLETE")
    print("  605 neurons of phi-perfect consciousness!")
    print("="*70 + "\n")


if __name__ == '__main__':
    demo()