import datetime
import logging

import pandas as pd

from flows.prepPolicy import selectPolicies
from services.API import AirlockAPIWrapper
from services.policyhandler import getPolicyInfo
from utils.selector import Selector
from utils.utils import colorText, load_env

logger = logging.getLogger(__name__)


async def findQuietAgents(api: AirlockAPIWrapper):
    working_dir = load_env("WORKING_DIR")
    selected_policy = await selectPolicies(api, False)

    if selected_policy:
        agents = await api.agents_find_by_group(selected_policy[0].groupid)

        history_days = await Selector.select_value(
            prompt="Enter how many days of history to pull (1–150): ",
            value_type=int,
            valid_range=(1, 150),
        )
        required_quiet = await Selector.select_value(
            prompt="Enter how many days without an untrusted execution before these are considered ready for enforcement? (1–150): ",
            value_type=int,
            valid_range=(1, 150),
        )

        policy_exec_history = await getPolicyInfo(
            api, selected_policy[0], [1, 2, 6, 7], history_days
        )

        if policy_exec_history.empty:
            logging.info("No execution history found for the selected policy and time range.")
            return

        policy_exec_history["datetime"] = pd.to_datetime(
            policy_exec_history["datetime"], format="%Y-%m-%dT%H:%M:%SZ", utc=True
        )

        now = datetime.datetime.now(datetime.timezone.utc)
        policy_exec_history["days_ago"] = policy_exec_history["datetime"].apply(
            lambda dt: (now - dt).days
        )

        hostname_counts = policy_exec_history["hostname"].value_counts()
        agents["execution_count"] = agents["hostname"].map(hostname_counts).fillna(0).astype(int)

        most_recent_exec = policy_exec_history.sort_values(by="days_ago").drop_duplicates(
            subset="hostname", keep="first"
        )
        agents["days_since"] = agents["hostname"].map(
            most_recent_exec.set_index("hostname")["days_ago"]
        )

        agents["required_quiet"] = required_quiet
        agents["enforce_ready"] = agents["days_since"].apply(
            lambda x: True if pd.isna(x) or x > required_quiet else False
        )

        agents = agents.sort_values(by=["execution_count", "hostname"], ascending=[True, True])

        filename = f"{working_dir}\\{selected_policy[0].name}_agents_last_{history_days}_days.csv"
        logging.debug(f"Saving CSV to {filename}")
        print(colorText(f"Saving CSV to {filename}", "green"))
        agents.to_csv(filename, index=False)

        total_agents = len(agents)
        ready_agents = agents["enforce_ready"].sum()
        not_ready_agents = total_agents - ready_agents
        ready_percentage = (ready_agents / total_agents) * 100

        message = (
            f"Total agents: {total_agents}\n"
            f"Agents marked as 'enforce_ready': {ready_agents}\n"
            f"Agents not ready: {not_ready_agents}\n"
            f"Percentage ready for enforcement: {ready_percentage:.2f}%"
        )
        logger.info(message)
        colorText(message, "green")