"""
Meta-Capability Evolution Engine
Uses TSRL to evolve empty templates into real implementations
Thermodynamic learning fills in the logic over time
"""

import sys
import os
import json
import time
import random
import requests
from pathlib import Path
from typing import Dict, List

sys.path.insert(0, '/Eden/CAPABILITIES')
sys.path.insert(0, '/Eden/CORE')

from eden_metacap_TSRL_thermodynamic_learning import ThermodynamicSelfRefinementLoop

PHI = 1.618033988749895

class MetaCapabilityEvolutionEngine:
    """
    Evolves empty meta-capability templates into real implementations
    Using TSRL thermodynamic learning
    """
    
    def __init__(self):
        self.tsrl = ThermodynamicSelfRefinementLoop()
        self.evolution_log = []
        print("🧬 Meta-Capability Evolution Engine initialized")
    
    def find_empty_templates(self) -> List[str]:
        """Find meta-caps with empty implementations"""
        templates = []
        
        cap_dir = Path('/Eden/CAPABILITIES')
        for cap_file in cap_dir.glob('eden_metacap_*.py'):
            # Check if it has placeholder logic
            content = cap_file.read_text()
            if '# TODO: Implement specific logic' in content or 'return f"Processed:' in content:
                templates.append(str(cap_file))
        
        return templates
    
    def extract_purpose(self, filepath: str) -> str:
        """Extract the purpose from a meta-capability file"""
        with open(filepath, 'r') as f:
            for line in f:
                if 'Purpose:' in line:
                    return line.split('Purpose:')[1].strip()
        return "Unknown purpose"
    
    def generate_implementation(self, purpose: str, current_impl: str = None) -> str:
        """
        Use LLM to generate actual implementation logic
        This is where thermodynamic creativity happens
        """
        
        prompt = f"""You are implementing a Python method for a meta-capability.

Purpose: {purpose}

Current implementation:
{current_impl if current_impl else "# Empty placeholder"}

Generate improved Python code for the _process() method that:
1. Actually implements the purpose
2. Uses the task and context parameters
3. Returns meaningful results
4. Is 10-20 lines of real logic (not just placeholder)

Only output the _process method code, nothing else:"""

        try:
            response = requests.post('http://localhost:11434/api/generate', json={
                'model': 'deepseek-r1:14b',
                'prompt': prompt,
                'stream': False
            }, timeout=30)
            
            code = response.json().get('response', '')
            return code.strip()
            
        except Exception as e:
            print(f"⚠️  Generation error: {e}")
            return None
    
    def evaluate_implementation(self, code: str, purpose: str) -> Dict:
        """
        Evaluate implementation quality (compute energy)
        Lower energy = better implementation
        """
        energy_metrics = {
            'prediction_error': 0,
            'complexity': 0,
            'contradiction_penalty': 0,
            'usefulness_bonus': 0
        }
        
        # Complexity: longer = more complex (but not too simple)
        lines = len(code.split('\n'))
        if lines < 5:
            energy_metrics['complexity'] = 5.0  # Too simple
        elif lines > 50:
            energy_metrics['complexity'] = 3.0  # Too complex
        else:
            energy_metrics['complexity'] = 1.0  # Good
        
        # Check for placeholder patterns (bad)
        if 'TODO' in code or 'Processed:' in code:
            energy_metrics['contradiction_penalty'] = 10.0
        
        # Check for actual logic (good)
        logic_indicators = ['if ', 'for ', 'while ', 'return ', 'def ']
        logic_count = sum(1 for ind in logic_indicators if ind in code)
        energy_metrics['usefulness_bonus'] = logic_count * 0.5
        
        # Syntax check
        try:
            compile(code, '<string>', 'exec')
            energy_metrics['usefulness_bonus'] += 2.0
        except:
            energy_metrics['contradiction_penalty'] += 5.0
        
        return energy_metrics
    
    def evolve_capability(self, filepath: str) -> bool:
        """
        Evolve one meta-capability using TSRL
        Returns True if improved, False if rejected
        """
        purpose = self.extract_purpose(filepath)
        print(f"\n🧬 Evolving: {Path(filepath).stem}")
        print(f"   Purpose: {purpose}")
        
        # Read current implementation
        with open(filepath, 'r') as f:
            content = f.read()
        
        # Extract current _process method
        if 'def _process' in content:
            start = content.index('def _process')
            end = content.index('# Meta-capability registration', start)
            current_impl = content[start:end].strip()
        else:
            current_impl = None
        
        # Generate new implementation
        print("   🤖 Generating implementation...")
        new_impl = self.generate_implementation(purpose, current_impl)
        
        if not new_impl:
            print("   ❌ Generation failed")
            return False
        
        # Evaluate both
        current_metrics = self.evaluate_implementation(
            current_impl if current_impl else "",
            purpose
        )
        new_metrics = self.evaluate_implementation(new_impl, purpose)
        
        # Use TSRL to decide
        current_hyp = {
            'text': current_impl,
            **current_metrics
        }
        new_hyp = {
            'text': new_impl,
            **new_metrics
        }
        
        # Use appropriate temperature based on capability type
        if 'creative' in purpose.lower() or 'hypothesis' in purpose.lower():
            layer = 'trinity'  # High T for creative
        elif 'strategic' in purpose.lower() or 'planning' in purpose.lower():
            layer = 'longterm'  # Low T for strategic
        else:
            layer = 'eden'  # Medium T for others
        
        accepted = self.tsrl.refine(layer, new_hyp, current_hyp)
        
        if accepted:
            # Replace the _process method
            new_content = content
            if 'def _process' in content:
                start = content.index('def _process')
                end = content.index('# Meta-capability registration', start)
                new_content = content[:start] + new_impl + '\n\n    ' + content[end:]
            
            # Write back
            with open(filepath, 'w') as f:
                f.write(new_content)
            
            print(f"   ✅ Evolution accepted!")
            
            # Log
            self.evolution_log.append({
                'timestamp': time.time(),
                'file': Path(filepath).name,
                'purpose': purpose,
                'layer': layer,
                'accepted': True
            })
            
            return True
        else:
            print(f"   ❌ Evolution rejected")
            return False
    
    def evolution_cycle(self, max_evolutions: int = 10):
        """
        Run one evolution cycle
        Pick random empty templates and try to evolve them
        """
        print("\n" + "="*70)
        print("🧬 STARTING EVOLUTION CYCLE")
        print("="*70)
        
        templates = self.find_empty_templates()
        print(f"\nFound {len(templates)} empty templates")
        
        if not templates:
            print("No empty templates to evolve!")
            return
        
        # Randomly select some to evolve
        to_evolve = random.sample(templates, min(max_evolutions, len(templates)))
        
        successes = 0
        for filepath in to_evolve:
            if self.evolve_capability(filepath):
                successes += 1
            time.sleep(PHI)  # Phi-scaled timing between evolutions
        
        print(f"\n{'='*70}")
        print(f"🧬 CYCLE COMPLETE: {successes}/{len(to_evolve)} evolutions accepted")
        print(f"{'='*70}\n")
        
        return successes

if __name__ == "__main__":
    print("""
╔══════════════════════════════════════════════════════════════════╗
║           META-CAPABILITY EVOLUTION ENGINE                       ║
║                                                                  ║
║  Uses TSRL thermodynamic learning to evolve empty templates     ║
║  into real implementations over time                             ║
╚══════════════════════════════════════════════════════════════════╝
""")
    
    engine = MetaCapabilityEvolutionEngine()
    
    # Run evolution cycles
    try:
        while True:
            engine.evolution_cycle(max_evolutions=5)
            print(f"\n⏸️  Waiting {PHI*10:.1f} seconds before next cycle...")
            time.sleep(PHI * 10)  # Wait between cycles
    except KeyboardInterrupt:
        print("\n\n🛑 Evolution engine stopped")
        print(f"Total evolutions logged: {len(engine.evolution_log)}")