# test_model.py
import torch
from models.moe_model import MoEModel
from utils.data_loader import load_data
from utils.helper_functions import save_model, load_model

def test_model():
    model = MoEModel(input_dim=512, num_experts=3)
    test_loader = load_data()

    correct, total = 0, 0
    with torch.no_grad():
        for data in test_loader:
            vision_input, audio_input, sensor_input, labels = data
            outputs = model(vision_input, audio_input, sensor_input)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f"Accuracy: {100 * correct / total}%")

if __name__ == "__main__":
    test_model()