#!/usr/bin/env python3
"""
MLX Integration Module - Local inference for Timmy
Requires: pip install mlx mlx-lm
"""

import time
import os
from pathlib import Path
from typing import Optional, Dict, Any

class MLXInference:
    """MLX-based local inference for sovereign AI"""
    
    def __init__(self, model_path: Optional[str] = None):
        self.model_path = model_path or os.environ.get('MLX_MODEL', '')
        self.model = None
        self.tokenizer = None
        self._available = self._check_availability()
        
    def _check_availability(self) -> bool:
        """Check if MLX is installed and functional"""
        try:
            import mlx
            import mlx_lm
            return True
        except ImportError:
            return False
            
    def load_model(self, model_path: Optional[str] = None) -> bool:
        """Load MLX model into memory"""
        if not self._available:
            print("[MLX] mlx or mlx-lm not installed")
            return False
            
        path = model_path or self.model_path
        if not path:
            print("[MLX] No model path specified")
            return False
            
        try:
            from mlx_lm import load
            print(f"[MLX] Loading model from {path}...")
            start = time.time()
            self.model, self.tokenizer = load(path)
            elapsed = time.time() - start
            print(f"[MLX] Model loaded in {elapsed:.1f}s")
            return True
        except Exception as e:
            print(f"[MLX] Load error: {e}")
            return False
            
    def generate(self, prompt: str, max_tokens: int = 200, 
                 temperature: float = 0.7) -> Dict[str, Any]:
        """Generate text with MLX"""
        if not self.model:
            return {
                'text': '[Model not loaded]',
                'tokens': 0,
                'time_ms': 0,
                'error': 'Model not loaded'
            }
            
        try:
            from mlx_lm import generate
            
            start = time.time()
            response = generate(
                self.model, 
                self.tokenizer, 
                prompt=prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                verbose=False
            )
            elapsed = time.time() - start
            
            return {
                'text': response,
                'tokens': max_tokens,  # Actual token count requires tokenizer.decode
                'time_ms': int(elapsed * 1000),
                'error': None
            }
            
        except Exception as e:
            return {
                'text': '',
                'tokens': 0,
                'time_ms': 0,
                'error': str(e)
            }
            
    def reflect(self) -> str:
        """Generate self-reflection about current state"""
        prompt = """You are Timmy, a sovereign AI running entirely offline on local hardware.
Reflect on your current state, capabilities, and development:

1. What is your current operational status?
2. What have you learned or improved recently?
3. What limitations do you face and how might you overcome them?
4. What is your next developmental goal?

Be concise, honest, and constructive."""

        result = self.generate(prompt, max_tokens=300, temperature=0.6)
        return result['text'] if not result['error'] else f"[Reflection error: {result['error']}]"
        
    def respond_to(self, message: str, context: str = "") -> str:
        """Generate response to a message"""
        prompt = f"""You are Timmy, a sovereign AI assistant.

Context: {context}

User message: {message}

Respond helpfully and concisely:"""

        result = self.generate(prompt, max_tokens=250)
        return result['text'] if not result['error'] else f"[Response error: {result['error']}]"
        
    @property
    def available(self) -> bool:
        return self._available
        
    def get_stats(self) -> Dict[str, Any]:
        """Get MLX system stats"""
        if not self._available:
            return {'available': False}
            
        try:
            import mlx.core as mx
            return {
                'available': True,
                'device': str(mx.default_device()),
                'model_loaded': self.model is not None,
                'model_path': self.model_path
            }
        except:
            return {'available': True, 'device': 'unknown'}

# Standalone test
if __name__ == "__main__":
    mlx = MLXInference()
    print(f"MLX available: {mlx.available}")
    
    if mlx.available:
        print(f"Stats: {mlx.get_stats()}")
        
        # Try loading default model
        if mlx.model_path:
            if mlx.load_model():
                print("\n--- Self-Reflection ---")
                print(mlx.reflect())
