"""Main entry point for computer agents."""

import asyncio
import logging
import os
from typing import AsyncGenerator, Optional

from computer import Computer
from .types import LLM, AgentLoop
from .types import AgentResponse
from .factory import LoopFactory
from .provider_config import DEFAULT_MODELS, ENV_VARS

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class ComputerAgent:
    """A computer agent that can perform automated tasks using natural language instructions."""

    def __init__(
        self,
        computer: Computer,
        model: LLM,
        loop: AgentLoop,
        max_retries: int = 3,
        screenshot_dir: Optional[str] = None,
        log_dir: Optional[str] = None,
        api_key: Optional[str] = None,
        save_trajectory: bool = True,
        trajectory_dir: str = "trajectories",
        only_n_most_recent_images: Optional[int] = None,
        verbosity: int = logging.INFO,
    ):
        """Initialize the ComputerAgent.

        Args:
            computer: Computer instance. If not provided, one will be created with default settings.
            max_retries: Maximum number of retry attempts.
            screenshot_dir: Directory to save screenshots.
            log_dir: Directory to save logs (set to None to disable logging to files).
            model: LLM object containing provider and model name. Takes precedence over provider/model_name.
            provider: The AI provider to use (e.g., LLMProvider.ANTHROPIC). Only used if model is None.
            api_key: The API key for the provider. If not provided, will look for environment variable.
            model_name: The model name to use. Only used if model is None.
            save_trajectory: Whether to save the trajectory.
            trajectory_dir: Directory to save the trajectory.
            only_n_most_recent_images: Maximum number of recent screenshots to include in API requests.
            verbosity: Logging level.
        """
        # Basic agent configuration
        self.max_retries = max_retries
        self.computer = computer
        self.queue = asyncio.Queue()
        self.screenshot_dir = screenshot_dir
        self.log_dir = log_dir
        self._retry_count = 0
        self._initialized = False
        self._in_context = False

        # Set logging level
        logger.setLevel(verbosity)

        # Setup logging
        if self.log_dir:
            os.makedirs(self.log_dir, exist_ok=True)
            logger.info(f"Created logs directory: {self.log_dir}")

        # Setup screenshots directory
        if self.screenshot_dir:
            os.makedirs(self.screenshot_dir, exist_ok=True)
            logger.info(f"Created screenshots directory: {self.screenshot_dir}")

        # Use the provided LLM object
        self.provider = model.provider
        actual_model_name = model.name or DEFAULT_MODELS.get(self.provider, "")
        self.provider_base_url = getattr(model, "provider_base_url", None)

        # Ensure we have a valid model name
        if not actual_model_name:
            actual_model_name = DEFAULT_MODELS.get(self.provider, "")
            if not actual_model_name:
                raise ValueError(
                    f"No model specified for provider {self.provider} and no default found"
                )

        # Get API key from environment if not provided
        actual_api_key = api_key or os.environ.get(ENV_VARS[self.provider], "")
        # Ollama and OpenAI-compatible APIs typically don't require an API key
        if (
            not actual_api_key
            and str(self.provider) not in ["ollama", "oaicompat"]
            and ENV_VARS[self.provider] != "none"
        ):
            raise ValueError(f"No API key provided for {self.provider}")

        # Create the appropriate loop using the factory
        try:
            # Let the factory create the appropriate loop with needed components
            self._loop = LoopFactory.create_loop(
                loop_type=loop,
                provider=self.provider,
                computer=self.computer,
                model_name=actual_model_name,
                api_key=actual_api_key,
                save_trajectory=save_trajectory,
                trajectory_dir=trajectory_dir,
                only_n_most_recent_images=only_n_most_recent_images,
                provider_base_url=self.provider_base_url,
            )
        except ValueError as e:
            logger.error(f"Failed to create loop: {str(e)}")
            raise

        # Initialize the message manager from the loop
        self.message_manager = self._loop.message_manager

        logger.info(
            f"ComputerAgent initialized with provider: {self.provider}, model: {actual_model_name}"
        )

    async def __aenter__(self):
        """Initialize the agent when used as a context manager."""
        logger.info("Entering ComputerAgent context")
        self._in_context = True

        # In case the computer wasn't initialized
        try:
            # Initialize the computer only if not already initialized
            logger.info("Checking if computer is already initialized...")
            if not self.computer._initialized:
                logger.info("Initializing computer in __aenter__...")
                # Use the computer's __aenter__ directly instead of calling run()
                await self.computer.__aenter__()
                logger.info("Computer initialized in __aenter__")
            else:
                logger.info("Computer already initialized, skipping initialization")

        except Exception as e:
            logger.error(f"Error initializing computer in __aenter__: {str(e)}")
            raise

        await self.initialize()
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """Cleanup agent resources if needed."""
        logger.info("Cleaning up agent resources")
        self._in_context = False

        # Do any necessary cleanup
        # We're not shutting down the computer here as it might be shared
        # Just log that we're exiting
        if exc_type:
            logger.error(f"Exiting agent context with error: {exc_type.__name__}: {exc_val}")
        else:
            logger.info("Exiting agent context normally")

        # If we have a queue, make sure to signal it's done
        if hasattr(self, "queue") and self.queue:
            await self.queue.put(None)  # Signal that we're done

    async def initialize(self) -> None:
        """Initialize the agent and its components."""
        if not self._initialized:
            # Always initialize the computer if available
            if self.computer and not self.computer._initialized:
                await self.computer.run()
            self._initialized = True

    async def run(self, task: str) -> AsyncGenerator[AgentResponse, None]:
        """Run a task using the computer agent.

        Args:
            task: Task description

        Yields:
            Agent response format
        """
        try:
            logger.info(f"Running task: {task}")
            logger.info(
                f"Message history before task has {len(self.message_manager.messages)} messages"
            )

            # Initialize the computer if needed
            if not self._initialized:
                await self.initialize()

            # Add task as a user message using the message manager
            self.message_manager.add_user_message([{"type": "text", "text": task}])
            logger.info(
                f"Added task message. Message history now has {len(self.message_manager.messages)} messages"
            )

            # Pass properly formatted messages to the loop
            if self._loop is None:
                logger.error("Loop not initialized properly")
                yield {"error": "Loop not initialized properly"}
                return

            # Execute the task and yield results
            async for result in self._loop.run(self.message_manager.messages):
                yield result

        except Exception as e:
            logger.error(f"Error in agent run method: {str(e)}")
            yield {
                "role": "assistant",
                "content": f"Error: {str(e)}",
                "metadata": {"title": "❌ Error"},
            }
