import datetime
import json
import logging
import os
import sys

from desktop_env.envs.desktop_env import DesktopEnv
from mm_agents.gpt_4v_agent import GPT4v_Agent

#  Logger Configs {{{ # 
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

datetime_str: str = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")

file_handler = logging.FileHandler(os.path.join("logs", "normal-{:}.log".format(datetime_str)), encoding="utf-8")
debug_handler = logging.FileHandler(os.path.join("logs", "debug-{:}.log".format(datetime_str)), encoding="utf-8")
stdout_handler = logging.StreamHandler(sys.stdout)
sdebug_handler = logging.FileHandler(os.path.join("logs", "sdebug-{:}.log".format(datetime_str)), encoding="utf-8")

file_handler.setLevel(logging.INFO)
debug_handler.setLevel(logging.DEBUG)
stdout_handler.setLevel(logging.INFO)
sdebug_handler.setLevel(logging.DEBUG)

formatter = logging.Formatter(
    fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s")
file_handler.setFormatter(formatter)
debug_handler.setFormatter(formatter)
stdout_handler.setFormatter(formatter)
sdebug_handler.setFormatter(formatter)

stdout_handler.addFilter(logging.Filter("desktopenv"))
sdebug_handler.addFilter(logging.Filter("desktopenv"))

logger.addHandler(file_handler)
logger.addHandler(debug_handler)
logger.addHandler(stdout_handler)
logger.addHandler(sdebug_handler)
#  }}} Logger Configs # 

logger = logging.getLogger("desktopenv.experiment")

PATH_TO_VM = r"C:\Users\tianbaox\Documents\Virtual Machines\Ubuntu\Ubuntu.vmx"


def run_one_example(example, agent, max_steps=10, example_trajectory_dir="exp_trajectory", recording=True):
    trajectory_recording_path = os.path.join(example_trajectory_dir, "trajectory.json")
    env = DesktopEnv(
        path_to_vm=PATH_TO_VM,
        action_space=agent.action_space,
        task_config=example
    )
    # reset the environment to certain snapshot
    observation = env.reset()
    done = False
    step_num = 0

    if recording:
        # send a request to the server to start recording
        env.controller.start_recording()

    while not done and step_num < max_steps:
        with open("accessibility_tree.xml", "w", encoding="utf-8") as f:
            f.write(observation["accessibility_tree"])
        actions = agent.predict(observation)
        step_num += 1
        for action in actions:
            # Capture the timestamp before executing the action
            action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
            logger.info("Step %d: %s", step_num, action)

            observation, reward, done, info = env.step(action)

            logger.info("Reward: %.2f", reward)
            logger.info("Done: %s", done)
            logger.info("Info: %s", info)

            # Save screenshot and trajectory information
            with open(os.path.join(example_trajectory_dir, f"step_{step_num}_{action_timestamp}.png"), "wb") as _f:
                with open(observation['screenshot'], "rb") as __f:
                    screenshot = __f.read()
                _f.write(screenshot)

            with open(trajectory_recording_path, "a") as f:
                f.write(json.dumps({
                    "step_num": step_num,
                    "action_timestamp": action_timestamp,
                    "action": action,
                    "reward": reward,
                    "done": done,
                    "info": info,
                    "screenshot_file": f"step_{step_num}_{action_timestamp}.png"
                }))
                f.write("\n")

            if done:
                logger.info("The episode is done.")
                break

    if recording:
        # send a request to the server to stop recording
        env.controller.end_recording(os.path.join(example_trajectory_dir, "recording.mp4"))

    result = env.evaluate()
    logger.info("Result: %.2f", result)

    # env.close()
    logger.info("Environment closed.")


if __name__ == "__main__":
    action_space = "pyautogui"
    example_class = "chrome"
    example_id = "7b6c7e24-c58a-49fc-a5bb-d57b80e5b4c3"
    gpt4_model = "gpt-4-vision-preview"
    gemini_model = "gemini-pro-vision"

    logger.info("Running example %s/%s", example_class, example_id)
    logger.info("Using model %s", gpt4_model)
    # logger.info("Using model %s", gemini_model)

    with open(f"evaluation_examples/examples/{example_class}/{example_id}.json", "r") as f:
        example = json.load(f)
    example["snapshot"] = "exp_setup4"

    api_key = os.environ.get("OPENAI_API_KEY")
    agent = GPT4v_Agent(api_key=api_key, model=gpt4_model, instruction=example['instruction'],
                        action_space=action_space, exp="a11y_tree")

    # api_key = os.environ.get("GENAI_API_KEY")
    # agent = GeminiPro_Agent(api_key=api_key, model=gemini_model, instruction=example['instruction'], action_space=action_space, exp="a11y_tree")

    root_trajectory_dir = "exp_trajectory"

    example_trajectory_dir = os.path.join(root_trajectory_dir, "a11y_tree", example_class, gpt4_model, example_id)
    # example_trajectory_dir = os.path.join(root_trajectory_dir, "a11y_tree", example_class, gemini_model, example_id)

    os.makedirs(example_trajectory_dir, exist_ok=True)

    run_one_example(example, agent, 15, example_trajectory_dir)