import torch

from train_anomaly_detector import Autoencoder, score_samples


checkpoint = torch.load("anomaly_detector.pt", weights_only=True)
model = Autoencoder(checkpoint["feature_count"])
model.load_state_dict(checkpoint["model_state"])

samples = torch.tensor(
    [
        [0.05, -0.04, 0.02, 0.01, -0.03, 0.04],
        [3.10, 2.85, 3.25, 2.95, 3.05, 3.15],
    ],
    dtype=torch.float32,
)
scores = score_samples(model, samples)
threshold = checkpoint["threshold"]

print(f"normal_sample_score={scores[0]:.6f}")
print(f"suspect_sample_score={scores[1]:.6f}")
print(f"threshold={threshold:.6f}")
decision = bool(scores[0] < threshold and scores[1] > threshold)
print(f"loaded_checkpoint_decision={decision}")