import os
import time
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import BaseCallback
from flower_game_env import FlowerGameEnv


# ---- Dein Spielbereich (anpassen!) ----
monitor_area = {"top": 120, "left": 330, "width": 1900, "height": 1263}
env = FlowerGameEnv(monitor_area)
saved_model_name = "flower_bot"
zip_file = saved_model_name + ".zip"


class TimeBasedCheckpoint(BaseCallback):
    """
    Speichert das Modell alle 'save_every_secs' Sekunden in 'save_prefix' + Timestamp.
    """
    def __init__(self, save_every_secs=60, save_prefix=saved_model_name, verbose=1):
        super().__init__(verbose)
        self.save_every_secs = save_every_secs
        self.save_prefix = save_prefix
        self._last_save = time.time()

    def _on_step(self) -> bool:
        now = time.time()
        if now - self._last_save >= self.save_every_secs:
            fname = f"{self.save_prefix}"
            if self.verbose:
                print(f"[Autosave] Saving model to {fname}.zip")
            self.model.save(fname)
            self._last_save = now
        return True


# --- Laden, falls Datei vorhanden ---
if os.path.exists(zip_file):
    print(f"Lade existierendes Modell aus {zip_file}")
    model = PPO.load(zip_file, env=env)  # Weitertrainieren mit neuem Env
else:
    print("Starte neues Modell")
    # CNN + Dict-Observation → Verwende 'MultiInputPolicy'
    model = PPO("MultiInputPolicy", env, verbose=2)

# Trainieren mit Autosave (jede Minute)
model.learn(total_timesteps=500_000, callback=TimeBasedCheckpoint(100, "flower_bot"))

# Abschluss-Speicherstand
model.save("flower_bot_final")
print("Training fertig. Modell gespeichert: flower_bot_final.zip")