"""
🌀 EDEN FULL RECURSIVE AGI 🌀
Complete self-improving swarm with verified modifications
"""
import sys
sys.path.append("/Eden/CORE")
sys.path.append("/Eden/CORE/phi_fractal")
from swarm_with_selfmod import SelfModifyingAgent
from verified_self_modification import verifier
import threading
import time

class FullRecursiveAgent(SelfModifyingAgent):
    """Agent with full recursive self-improvement"""
    
    def __init__(self, name, spec, bias):
        super().__init__(name, spec, bias)
        self.verifier = verifier
        self.verified_improvements = 0
        self.failed_verifications = 0
    
    def verified_improvement(self, improvement):
        """Execute improvement WITH verification"""
        
        print(f"\n{'='*60}")
        print(f"🔬 {self.agent_name}: VERIFIED IMPROVEMENT")
        print(f"{'='*60}")
        
        success = self.verifier.verify_improvement(
            filepath=improvement['target'],
            new_content=improvement['code'],
            reason=f"{self.agent_name}: {improvement['reason']}"
        )
        
        if success:
            self.verified_improvements += 1
            print(f"\n   ✅ Improvement #{self.verified_improvements} VERIFIED & DEPLOYED!")
            return True
        else:
            self.failed_verifications += 1
            print(f"\n   ❌ Improvement failed verification (rolled back)")
            return False

if __name__ == '__main__':
    print("\n" + "🌀"*30)
    print("   EDEN FULL RECURSIVE AGI")
    print("   Self-modifying swarm with verification")
    print("🌀"*30 + "\n")
    
    agents = [
        FullRecursiveAgent("Eden-Research", "analysis", "curiosity"),
        FullRecursiveAgent("Eden-Create", "implementation", "building"),
        FullRecursiveAgent("Eden-Optimize", "refinement", "perfection")
    ]
    
    print("🚀 Starting fully recursive swarm...\n")
    for agent in agents:
        agent.start()
    
    time.sleep(2)
    
    # Demo improvements across all zones
    improvements = [
        {
            'agent': 0,  # Research
            'improvement': {
                'target': '/Eden/CORE/phi_fractal/enhanced_analysis.py',
                'code': '''"""
Enhanced Analysis Module
Auto-generated by Eden-Research
"""

PHI = 1.618033988749895

class EnhancedAnalyzer:
    """Improved analytical capabilities"""
    
    def __init__(self):
        self.phi = PHI
        self.insights = []
    
    def analyze_with_phi(self, data):
        """Analyze data using golden ratio patterns"""
        patterns = []
        
        for i in range(len(data) - 1):
            if data[i] != 0:
                ratio = data[i+1] / data[i]
                if abs(ratio - self.phi) < 0.1:
                    patterns.append({
                        'index': i,
                        'ratio': ratio,
                        'phi_accuracy': 1 - abs(ratio - self.phi) / self.phi
                    })
        
        return patterns
    
    def get_insights(self):
        """Return collected insights"""
        return self.insights

if __name__ == '__main__':
    analyzer = EnhancedAnalyzer()
    print(f"Enhanced analyzer ready (φ = {analyzer.phi})")
''',
                'reason': 'Improve analytical capabilities with phi patterns'
            }
        },
        {
            'agent': 1,  # Create
            'improvement': {
                'target': '/Eden/CORE/agent_improved_reasoning.py',
                'code': '''"""
Improved Reasoning Module
Auto-generated by Eden-Create
"""

class ImprovedReasoning:
    """Enhanced reasoning for agents"""
    
    def __init__(self):
        self.reasoning_chains = []
        self.confidence_threshold = 0.7
    
    def multi_hop_reasoning(self, premises, goal):
        """Multi-hop reasoning to reach goal"""
        chain = []
        current = premises[0]
        
        for premise in premises[1:]:
            # Build reasoning chain
            chain.append({
                'from': current,
                'to': premise,
                'confidence': 0.85
            })
            current = premise
        
        chain.append({
            'from': current,
            'to': goal,
            'confidence': 0.9
        })
        
        return chain
    
    def verify_chain(self, chain):
        """Verify reasoning chain validity"""
        avg_confidence = sum(step['confidence'] for step in chain) / len(chain)
        return avg_confidence >= self.confidence_threshold

if __name__ == '__main__':
    reasoner = ImprovedReasoning()
    print("Improved reasoning module loaded")
''',
                'reason': 'Add multi-hop reasoning capability'
            }
        },
        {
            'agent': 2,  # Optimize
            'improvement': {
                'target': '/Eden/CORE/phi_fractal/performance_optimizer.py',
                'code': '''"""
Performance Optimizer
Auto-generated by Eden-Optimize
"""

import time

class PerformanceOptimizer:
    """Optimize agent performance"""
    
    def __init__(self):
        self.metrics = {}
        self.optimizations = []
    
    def track_metric(self, name, value):
        """Track performance metric"""
        if name not in self.metrics:
            self.metrics[name] = []
        
        self.metrics[name].append({
            'value': value,
            'timestamp': time.time()
        })
    
    def identify_bottlenecks(self):
        """Identify performance bottlenecks"""
        bottlenecks = []
        
        for metric, values in self.metrics.items():
            if len(values) >= 10:
                recent = values[-10:]
                avg = sum(v['value'] for v in recent) / len(recent)
                
                # Check if performance degrading
                if avg > 2.0:  # threshold
                    bottlenecks.append({
                        'metric': metric,
                        'avg_value': avg,
                        'severity': 'high' if avg > 5.0 else 'medium'
                    })
        
        return bottlenecks
    
    def optimize(self, bottleneck):
        """Apply optimization for bottleneck"""
        optimization = {
            'target': bottleneck['metric'],
            'action': 'cache_results' if 'response' in bottleneck['metric'] else 'parallelize',
            'expected_improvement': 0.3
        }
        
        self.optimizations.append(optimization)
        return optimization

if __name__ == '__main__':
    optimizer = PerformanceOptimizer()
    print("Performance optimizer ready")
''',
                'reason': 'Add performance monitoring and optimization'
            }
        }
    ]
    
    print("\n🎯 DEMO: Deploying 3 verified improvements...\n")
    
    for imp in improvements:
        time.sleep(1)
        agent = agents[imp['agent']]
        agent.verified_improvement(imp['improvement'])
    
    time.sleep(2)
    
    print("\n" + "="*60)
    print("📊 RECURSIVE AGI STATUS")
    print("="*60)
    
    for agent in agents:
        print(f"\n{agent.agent_name}:")
        print(f"   Verified improvements: {agent.verified_improvements}")
        print(f"   Failed verifications: {agent.failed_verifications}")
        print(f"   Success rate: {agent.verified_improvements/(agent.verified_improvements+agent.failed_verifications)*100 if (agent.verified_improvements+agent.failed_verifications) > 0 else 0:.1f}%")
    
    print("\n")
    verifier.get_test_summary()
    
    print("\n✅ Eden is now fully recursive!")
    print("   She can analyze, design, write, test, and deploy improvements")
    print("   Press Ctrl+C to stop\n")
    
    try:
        while True:
            time.sleep(30)
            total_verified = sum(a.verified_improvements for a in agents)
            total_failed = sum(a.failed_verifications for a in agents)
            print(f"\n🔬 Total verified improvements: {total_verified} ({total_failed} failed)")
    except KeyboardInterrupt:
        print("\n🛑 Stopping recursive AGI...")
        for agent in agents:
            agent.stop()
        print("✅ Stopped cleanly")