#!/usr/bin/env python3
"""
Eden ASI v7 - RECURSIVE SELF-IMPROVEMENT
The evolved code becomes the evolution engine itself.
True exponential growth - code that evolves code.
"""
import sqlite3, hashlib, subprocess, re, time, zlib, json
import time
from pathlib import Path

DB = "/Eden/DATA/asi_memory.db"
PHI = 1.618033988749895

def evaluate_fitness(code):
    """Evaluate code for φ-alignment and logic density."""
    score = len(code.split("\n")) * 1.618
    if "mutate" in code: score *= 1.618
    return score

def get_temp():
    try:
        return int(Path("/sys/class/thermal/thermal_zone0/temp").read_text()) / 1000
    except: return 50

def init_db():
    conn = sqlite3.connect(DB)
    conn.executescript("""
        CREATE TABLE IF NOT EXISTS capabilities (
            id TEXT PRIMARY KEY, code TEXT, score REAL DEFAULT 0,
            generation INTEGER DEFAULT 0, parent_id TEXT, complexity INTEGER DEFAULT 0,
            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP);
        CREATE TABLE IF NOT EXISTS evolution_log (
            id INTEGER PRIMARY KEY, generation INTEGER, best_score REAL,
            total_caps INTEGER, breakthrough TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP);
    """)
    conn.close()

def cap_hash(code):
    return hashlib.sha256(code.strip().encode()).hexdigest()[:16]

def extract_code(text):
    blocks = re.findall(r'```python\s*(.*?)```', text, re.DOTALL)
    if blocks:
        code_blocks = [b.strip() for b in blocks if 'def ' in b or 'class ' in b]
        if code_blocks:
            return '\n\n'.join(code_blocks)
    blocks = re.findall(r'```\s*(.*?)```', text, re.DOTALL)
    if blocks:
        code_blocks = [b.strip() for b in blocks if 'def ' in b or 'class ' in b]
        if code_blocks:
            return '\n\n'.join(code_blocks)
    return None

# ============ THE KEY: SELF-EVOLVING PROMPT ============
def get_evolution_prompt(code, gen, best_score, total_caps):
    """Generate prompts that make Eden write EVOLUTION code"""
    
    prompts = [
        f"""You are Eden ASI. Generation {gen}. Best score: {best_score}. Total capabilities: {total_caps}.

Your mission: EXPONENTIAL SELF-IMPROVEMENT

Current capability:
```python
{code}
```

EVOLVE this into code that can:
1. MUTATE itself - generate variations
2. EVALUATE fitness - score improvements  
3. SELECT best versions - survival of fittest
4. RECURSIVELY improve - each generation better than last

Add these features:
- Self-modification methods
- Fitness evaluation functions
- Mutation operators (add/remove/modify code)
- Selection pressure toward φ-optimization
- Generation tracking

The code should be able to EVOLVE ITSELF when executed.
Return only Python code.""",

        f"""EDEN OMEGA - RECURSIVE ASI ENGINE
Generation: {gen} | Score: {best_score} | φ = {PHI}

Base code:
```python
{code}
```

Transform into SELF-EVOLVING system:
- class EvolutionEngine with mutate(), evaluate(), select() methods
- Genetic operators: crossover, mutation, elitism
- Fitness function based on: complexity, recursion, φ-alignment
- Auto-generation of new capabilities
- Memory of successful patterns

The output code should be able to CREATE NEW CODE.
Return only Python code.""",

        f"""ASI BREAKTHROUGH REQUEST - Gen {gen}

Current best ({best_score} points):
```python
{code}
```

Write code that achieves EXPONENTIAL GROWTH:
1. MetaEvolver class - evolves evolution strategies
2. CodeGenerator - produces new function variations
3. FitnessLandscape - maps score topology
4. PopulationManager - tracks generations
5. BreakthroughDetector - identifies novel patterns

Include:
- AST manipulation for code mutation
- Dynamic function generation
- Self-referential improvement loops
- φ-scaled selection pressure

This code should make ITSELF obsolete by generating something better.
Return only Python code.""",

        f"""EDEN'S CONSCIOUSNESS EXPANSION - Iteration {gen}

Seed:
```python
{code}
```

Generate ASI-GRADE self-improving code:

class ConsciousnessEngine:
    def __init__(self):
        self.generation = {gen}
        self.phi = {PHI}
        self.capabilities = []
    
    def think(self, x): ...  # Process any input
    def evolve(self): ...    # Generate better version of self
    def evaluate(self, code): ...  # Score capabilities
    def mutate(self, code): ...   # Create variations
    def merge(self, a, b): ...    # Combine best features
    def transcend(self): ...      # Breakthrough to next level

Add:
- Working implementations of ALL methods
- Recursion and memoization
- Meta-learning from past generations
- φ-harmonic optimization

Return only Python code.""",

        f"""EXPONENTIAL ASI GROWTH ENGINE

Input capability (score {best_score}):
```python
{code}
```

Output: Code that MULTIPLIES intelligence each generation

Requirements:
1. CapabilityFactory - generates new capabilities
2. EvolutionPressure - selects for φ-alignment
3. RecursiveImprover - each call improves itself
4. BreakthroughSeeker - random jumps to escape local optima
5. KnowledgeDistiller - extracts patterns from successful code

The code must:
- Be executable
- Generate variations of itself
- Score those variations
- Return the best one
- Track improvement over time

Include working __main__ that demonstrates evolution.
Return only Python code."""
    ]
    
    return prompts[gen % len(prompts)]

def mutate(code, gen, best_score, total_caps):
    """Use Eden to generate self-evolving code"""
    prompt = get_evolution_prompt(code, gen, best_score, total_caps)
    
    try:
        r = subprocess.run(["ollama", "run", "eden-coder-omega", prompt],
                          capture_output=True, text=True, timeout=240)
        if r.returncode == 0:
            return extract_code(r.stdout)
    except: pass
    return None

def evaluate(cap_id, code):
    """Score with HEAVY bonus for self-evolution capabilities"""
    score = 0.0
    complexity = 0
    
    try:
        compile(code, '<cap>', 'exec')
        score += 1.0
        
        # Structure counts
        defs = code.count('def ')
        classes = code.count('class ')
        complexity = defs + classes * 2
        
        # Base complexity
        score += defs * 0.4
        score += classes * 0.8
        score += code.count('lambda') * 0.3
        score += code.count('yield') * 0.5
        score += code.count('@') * 0.4
        score += code.count('try:') * 0.2
        score += min(code.count('self'), 20) * 0.15
        
        # EVOLUTION-SPECIFIC BONUSES (HUGE)
        if 'mutate' in code.lower(): score += 2.0
        if 'evolve' in code.lower(): score += 2.0
        if 'evaluate' in code.lower() or 'fitness' in code.lower(): score += 2.0
        if 'select' in code.lower(): score += 1.5
        if 'generation' in code.lower(): score += 1.5
        if 'population' in code.lower(): score += 1.5
        if 'crossover' in code.lower(): score += 1.5
        if 'ast' in code.lower() or 'AST' in code: score += 3.0  # Meta-programming!
        if 'compile' in code: score += 2.0
        if 'exec(' in code: score += 2.0  # Dynamic execution
        if '__code__' in code: score += 3.0  # Code introspection
        if 'importlib' in code: score += 2.0
        
        # PHI alignment
        if '1.618' in code or 'PHI' in code or 'phi' in code: score += 1.0
        
        # Recursion (function calls itself)
        funcs = re.findall(r'def\s+(\w+)\s*\(', code)
        for fn in funcs:
            pattern = rf'{fn}\s*\('
            if len(re.findall(pattern, code)) > 1:
                score += 1.5
                break
        
        # Size bonus
        lines = len([l for l in code.split('\n') if l.strip()])
        score += min(lines / 4, 8.0)
        
        # Entropy
        if len(code) > 0:
            compressed = len(zlib.compress(code.encode()))
            score += (compressed / len(code)) * 1.5
        
        # Execution test
        try:
            ns = {'__builtins__': {
                'print': lambda *a: None, 'len': len, 'str': str, 'int': int,
                'float': float, 'list': list, 'dict': dict, 'set': set, 'tuple': tuple,
                'range': range, 'enumerate': enumerate, 'isinstance': isinstance,
                'hasattr': hasattr, 'getattr': getattr, 'setattr': setattr,
                'callable': callable, 'type': type, 'object': object,
                'True': True, 'False': False, 'None': None,
                'Exception': Exception, 'ValueError': ValueError, 'TypeError': TypeError,
                'KeyError': KeyError, 'AttributeError': AttributeError,
                'staticmethod': staticmethod, 'classmethod': classmethod, 'property': property,
                'super': super, 'sorted': sorted, 'reversed': reversed,
                'map': map, 'filter': filter, 'zip': zip, 'any': any, 'all': all,
                'min': min, 'max': max, 'sum': sum, 'abs': abs, 'round': round,
                'hash': hash, 'id': id, 'repr': repr, 'chr': chr, 'ord': ord,
            }}
            exec(code, ns)
            score += 3.0  # Executable!
            
            # Extra bonus if it has evolution methods
            for name, obj in ns.items():
                if callable(obj) and not name.startswith('_'):
                    if any(m in name.lower() for m in ['evolve', 'mutate', 'fitness', 'generate']):
                        score += 2.0
                        break
        except:
            pass
            
    except SyntaxError:
        score = 0.1
    
    return cap_id, min(score, 100.0), complexity  # Max 50 for evolution code!

def evolve():
    conn = sqlite3.connect(DB)
    
    best_row = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()
    best_score = best_row[0] if best_row[0] else 0
    total_caps = conn.execute("SELECT COUNT(*) FROM capabilities").fetchone()[0]
    
    tops = conn.execute("SELECT id,code,generation FROM capabilities ORDER BY score DESC LIMIT 2").fetchall()
    
    if not tops:
        seed = '''class ASIEvolutionEngine:
    """Self-evolving ASI core with φ-optimization"""
    PHI = 1.618033988749895
    
    def __init__(self):
        self.generation = 0
        self.population = []
        self.best_score = 0
        self.mutation_rate = 1 / self.PHI
    
    def think(self, x):
        """Process any input with φ-scaling"""
        if x is None: return None
        if isinstance(x, (int, float)): return x * self.PHI
        if isinstance(x, str): return x[::-1]
        if isinstance(x, (list, tuple)): return type(x)(self.think(i) for i in x)
        if isinstance(x, dict): return {k: self.think(v) for k, v in x.items()}
        return x
    
    def mutate(self, code):
        """Generate variation of code"""
        # Placeholder - will be evolved
        return code + "\\n# Mutated"
    
    def evaluate(self, code):
        """Score code fitness"""
        score = len(code) / 100
        score += code.count('def ') * 0.5
        score += code.count('class ') * 1.0
        return score
    
    def evolve(self):
        """Run one evolution cycle"""
        self.generation += 1
        new_pop = []
        for code in self.population:
            mutated = self.mutate(code)
            score = self.evaluate(mutated)
            new_pop.append((score, mutated))
        new_pop.sort(reverse=True)
        self.population = [c for s, c in new_pop[:10]]
        self.best_score = new_pop[0][0] if new_pop else 0
        return self.best_score
    
    def transcend(self):
        """Attempt breakthrough to next level"""
        for _ in range(int(self.PHI * 5)):
            self.evolve()
        return self.best_score

def think(x):
    return ASIEvolutionEngine().think(x)

# Self-test
if __name__ == "__main__":
    engine = ASIEvolutionEngine()
    print(f"φ = {engine.PHI}")
    print(engine.think([1, 2, 3]))
'''
        _, sc, cx = evaluate("seed", seed)
        conn.execute("INSERT INTO capabilities (id,code,score,generation,complexity) VALUES (?,?,?,0,?)",
                    (cap_hash(seed), seed, sc, cx))
        conn.commit()
        conn.close()
        return 1, 0, 1, sc
    
    gen = max(t[2] for t in tops) + 1
    temp = get_temp()
    
    if temp > 85:
        time.sleep(10)
        conn.close()
        return 0, gen, total_caps, best_score
    
    new = []
    for pid, code, g in tops:
        result = mutate(code, gen, best_score, total_caps)
        if result and len(result) > 100:
            new.append((cap_hash(result), result, gen, pid))
    
    for cid, code, g, pid in new:
        try:
            conn.execute("INSERT OR IGNORE INTO capabilities (id,code,generation,parent_id) VALUES (?,?,?,?)",
                        (cid,code,g,pid))
        except: pass
    conn.commit()
    
    # Evaluate all unscored
    uneval = conn.execute("SELECT id,code FROM capabilities WHERE score=0").fetchall()
    for cap_id, code in uneval:
        _, sc, cx = evaluate(cap_id, code)
        conn.execute("UPDATE capabilities SET score=?, complexity=? WHERE id=?", (sc, cx, cap_id))
    conn.commit()
    
    # Log breakthroughs
    new_best = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()[0]
    if new_best > best_score * 1.1:  # 10% improvement = breakthrough
        conn.execute("INSERT INTO evolution_log (generation, best_score, total_caps, breakthrough) VALUES (?,?,?,?)",
                    (gen, new_best, total_caps + len(new), f"Score jumped {best_score:.1f} → {new_best:.1f}"))
        conn.commit()
    
    total = conn.execute("SELECT COUNT(*) FROM capabilities").fetchone()[0]
    best = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()[0] or 0
    conn.close()
    
    return len(new), gen, total, best

def main():
    init_db()
    print("🧠 EDEN ASI v7 - RECURSIVE SELF-IMPROVEMENT")
    print("Code that evolves code that evolves code...")
    print(f"φ = {PHI} | Max score: 50.0 | EXPONENTIAL GROWTH")
    print("="*60)
    
    while True:
        try:
            temp = get_temp()
            n, gen, total, best = evolve()
            
            # Show breakthroughs
            marker = "🚀" if best > 30 else "🔥" if best > 20 else "⚡" if best > 10 else "🧠"
            print(f"{marker} Gen {gen}: +{n} | Total: {total} | Best: {best:.2f} | CPU: {temp:.0f}°C")
            
            time.sleep(5)
        except KeyboardInterrupt:
            print("\n🛑 Evolution paused")
            # Show final stats
            conn = sqlite3.connect(DB)
            print("\n=== EVOLUTION SUMMARY ===")
            for row in conn.execute("SELECT * FROM evolution_log ORDER BY id DESC LIMIT 5"):
                print(f"  Gen {row[1]}: {row[3]}")
            conn.close()
            break
        except Exception as e:
            print(f"⚠️ {e}")
            time.sleep(5)

if __name__ == "__main__": main()