import torch
from model import ChessPredictModelS
from fen2vec import parse_fen

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def run(model_path='best_model.pth'):
	model = ChessPredictModelS().half().to(device)
	model.load_state_dict(torch.load(model_path, weights_only=True))
	model.eval()

	while True:
		fen_str = input("FEN(q) >")
		if fen_str.lower() == 'q':
			break

		try:
			input_tensor, _wdl = parse_fen(fen_str, 0)
			input_tensor = input_tensor.unsqueeze(0).to(device)  # add batch dim

			with torch.no_grad():
				output = model(input_tensor)
				print(f"$= {transformed_output[0, 0]} {output[0, 0]}")
		except Exception as e:
			print(f"Error: {e}")

if __name__ == "__main__":
	model_path = 'best_model.pth'
	run(model_path)