
import os
import time
import requests
import tarfile
import logging
from rich.console import Console
from rich.panel import Panel

from .auth import get_api_key

console = Console()
logger = logging.getLogger(__name__)

class Worker:
    """
    Executes training jobs fetched from the Langtrain backend.
    Designed to run in ephemeral environments like Google Colab.
    """
    
    API_URL = os.environ.get("LANGTUNE_API_URL", "https://api.langtrain.xyz")
    
    def __init__(self, token: str = None):
        self.api_key = token or get_api_key()
        if not self.api_key:
            raise ValueError("Worker requires an API key. Run 'langtune auth login' or pass --token.")

    def start(self, poll_interval: int = 5):
        """Start the worker loop."""
        console.print(Panel("[bold green]Langtrain Worker Started[/]\nPoints of Presence: Google Colab", title="Worker Status"))
        
        while True:
            try:
                job = self._poll_job()
                if job:
                    self._process_job(job)
                else:
                    console.print(".", end="")
                    time.sleep(poll_interval)
            except KeyboardInterrupt:
                console.print("\n[yellow]Worker stopped by user.[/]")
                break
            except Exception as e:
                console.print(f"\n[red]Worker Error: {e}[/]")
                time.sleep(poll_interval)

    def _poll_job(self):
        """Poll backend for pending jobs."""
        # Mocking the poll request
        # response = requests.get(f"{self.API_URL}/worker/poll", headers={"Authorization": f"Bearer {self.api_key}"})
        return None  # No jobs for MVP simulation

    def _process_job(self, job):
        """Execute a training job."""
        console.print(f"\n[bold cyan]Received Job: {job['id']}[/]")
        
        # 1. Download Bundle
        # self._download_bundle(job['bundle_url'])
        
        # 2. Extract & Train
        # DeviceManager will automatically pick up the GPU here (Colab T4)
        # train(config="config.json")
        
        # 3. Upload Results
        # self._upload_results(job['id'])
        
        console.print(f"[bold green]Job {job['id']} Completed[/]")

