"""
Lux Meta Engine — Genesis Superior Browser (GSB) Layer 4
========================================================
Transcend the browser. This engine operates at the OS level, capturing the
entire virtual display and using OpenAGI's Lux foundation model to infer
human-parity mouse clicks, scrolls, and keypresses.

This implementation acts as the bridge to the Lux inference service, performing
physical OS interaction using PyAutoGUI.
"""

import os
import time
import logging
from typing import Dict, Any, Optional

try:
    import pyautogui
except ImportError:
    pyautogui = None

logger = logging.getLogger("genesis_v2.core.browser.lux_meta_engine")

class LuxMetaEngine:
    def __init__(self, display_id: str = ":0"):
        self.display_id = display_id
        # We assume Xvfb or a visible virtual display is active if we are running headless
        os.environ["DISPLAY"] = self.display_id
        
        if not pyautogui:
            logger.warning("PyAutoGUI not installed. Lux Meta Engine will run in simulation mode.")
            self.simulation_mode = True
        else:
            self.simulation_mode = False
            # Prevent PyAutoGUI from fail-safing if we don't want it to
            pyautogui.FAILSAFE = False

    async def capture_screen(self) -> bytes:
        """Capture the current OS-level screen."""
        if self.simulation_mode:
            logger.info("Simulation: Capturing screen stub.")
            return b"mock_image_bytes"
            
        screenshot = pyautogui.screenshot()
        # In a real scenario, we'd convert this to bytes/base64 for the vision model
        logger.info(f"Captured screen of size {screenshot.size}")
        return b"mock_image_bytes"

    async def infer_lux_action(self, image_bytes: bytes, instruction: str) -> Dict[str, Any]:
        """
        Send the screen and instruction to the Lux model.
        Returns coordinate map and action type (click, type, scroll).
        """
        logger.info(f"Sending screen to Lux Model with instruction: '{instruction}'")
        # TODO: Replace with actual OpenAGI Lux inference API call
        time.sleep(1) # Simulate inference latency
        
        # Mocking a successful identification
        return {
            "action": "click",
            "coordinates": {"x": 500, "y": 500},
            "confidence": 0.95
        }

    async def execute_os_action(self, action_data: Dict[str, Any]):
        """Execute the predicted action using PyAutoGUI."""
        action = action_data.get("action")
        
        if self.simulation_mode:
            logger.info(f"Simulation: Executing OS Action -> {action_data}")
            return
            
        if action == "click":
            coords = action_data.get("coordinates", {})
            x, y = coords.get("x", 0), coords.get("y", 0)
            logger.info(f"OS-Level Click at ({x}, {y})")
            
            # The 'Jitter Engine' concept: non-linear movement
            # For now, we use PyAutoGUI's tweening
            pyautogui.moveTo(x, y, duration=0.5, tween=pyautogui.easeInOutQuad)
            pyautogui.click()
            
        elif action == "type":
            text = action_data.get("text", "")
            logger.info(f"OS-Level Typing: {text}")
            pyautogui.write(text, interval=0.05)
            
        elif action == "scroll":
            amount = action_data.get("amount", -10)
            logger.info(f"OS-Level Scroll: {amount}")
            pyautogui.scroll(amount)

    async def run_instruction(self, instruction: str) -> Dict[str, Any]:
        """Complete workflow for a single instruction."""
        try:
            logger.info(f"Lux Meta Engine activating for: '{instruction}'")
            screen_bytes = await self.capture_screen()
            action_data = await self.infer_lux_action(screen_bytes, instruction)
            
            if action_data.get("confidence", 0) > 0.8:
                await self.execute_os_action(action_data)
                return {"status": "success", "action": action_data}
            else:
                return {"status": "failed", "error": "Low confidence from Lux model"}
                
        except Exception as e:
            logger.error(f"Lux Meta Engine execution failed: {e}")
            return {"status": "failed", "error": str(e)}