import json
from desktop_env.envs.desktop_env import DesktopEnv


def human_agent():
    """
    Runs the Gym environment with human input.
    """

    with open("evaluation_examples/examples/37608790-6147-45d0-9f20-1137bb35703d.json", "r") as f:
        example = json.load(f)

    env = DesktopEnv(
        # path_to_vm=r"""C:\Users\tianbaox\Downloads\Windows 10 x64\Windows 10 x64.vmx""",
        path_to_vm=r"""C:\Users\tianbaox\Documents\Virtual Machines\Win10\Win10.vmx""",
        #  path_to_vm="/home/yuri/vmware/Ubuntu 64-bit/Ubuntu 64-bit.vmx",
        action_space="computer_13",
        snapshot_path="base_setup2",
        # config=example["config"],
    )

    # reset the environment to certain snapshot
    observation = env.reset()
    done = False

    trajectory = [
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        },
        {"action_type": "CLICK", "parameters": {"button": "right", "num_clicks": 1}},
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        },
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        },
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        },
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        },
        {
            "action_type": "MOVE_TO",
            "parameters": {
                "x": 754,
                "y": 1057
            }
        }

    ]

    for i in range(len(trajectory)):
        # action = get_human_action()

        # action = {
        #     "action_type": 0,
        #     "click_type": 3,
        # }
        print(trajectory[i])

        observation, reward, done, info = env.step(trajectory[i], pause=5)
        print("Observation:", observation)
        print("Reward:", reward)
        print("Info:", info)

        print("================================\n")

        if done:
            print("The episode is done.")
            break

    env.close()
    print("Environment closed.")


if __name__ == "__main__":
    human_agent()