import os
import datetime
import json
import logging
from wrapt_timeout_decorator import *
logger = logging.getLogger("desktopenv.experiment")

# Open the JSON file
with open("./settings.json", "r") as file:
    # Load the JSON data from the file
    data = json.load(file)
time_limit = data["time_limit"]

@timeout(time_limit, use_signals=False)
def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
    agent.reset()
    obs = env.reset(task_config=example)
    done = False
    step_idx = 0
    env.controller.start_recording()

    while not done and step_idx < max_steps:
        actions = agent.predict(
            instruction,
            obs
        )
        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_idx + 1, action)

            obs, reward, done, info = env.step(action, args.sleep_after_execution)

            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_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
                        "wb") as _f:
                with open(obs['screenshot'], "rb") as __f:
                    screenshot = __f.read()
                _f.write(screenshot)

            with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
                f.write(json.dumps({
                    "step_num": step_idx + 1,
                    "action_timestamp": action_timestamp,
                    "action": action,
                    "reward": reward,
                    "done": done,
                    "info": info,
                    "screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
                }))
                f.write("\n")

            if done:
                logger.info("The episode is done.")
                break
        step_idx += 1
    result = env.evaluate()
    logger.info("Result: %.2f", result)
    scores.append(result)
    with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
        f.write(f"{result}\n")
    env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))