the best one minimax

import chess
import time

def ten_moves_rule(board):
    """Custom rule to evaluate a draw condition based on the last ten moves, considering no captures or pawn moves."""
    history = list(board.move_stack)
    if len(history) < 10:
        return False

    for move in history[-10:]:
        if board.is_capture(move):
            return False
        if board.piece_type_at(move.from_square) == chess.PAWN:
            return False
    return True

def evaluate_board(board, depth):
    """Evaluate the board state for minimax decision-making."""
    if board.is_checkmate():
        return -1000 + depth if board.turn == chess.WHITE else 1000 - depth
    elif board.is_stalemate():
        return 4
    elif board.is_insufficient_material():
        return 3
    elif ten_moves_rule(board):
        return 2
    return 1  # Default heuristic if none of the above conditions are met

def minimax(board, depth, alpha, beta, maximizing_player, depth2, depths, position_count, memo, start_time, last_print_time):
    current_time = time.time()
    if current_time - last_print_time[0] >= 1:
        elapsed_hours, remainder = divmod(current_time - start_time, 3600)
        elapsed_minutes, elapsed_seconds = divmod(remainder, 60)
        print(f"\r{int(elapsed_hours):02d}h {int(elapsed_minutes):02d}m {int(elapsed_seconds):02d}s", end='', flush=True)
        last_print_time[0] = current_time

    position_count[0] += 1
    if position_count[0] % 1000000 == 0:
        print(f"\nProzkoumano {position_count[0]} pozic.")

    key = (board.fen(), maximizing_player, depth, alpha, beta)
    if key in memo:
        return memo[key]

    if depth == 0 or board.is_game_over():
        eval = evaluate_board(board, depth2)
        memo[key] = ([], eval)
        return [], eval

    best_eval = float('-inf') if maximizing_player else float('inf')
    best_sequence = []

    for move in board.legal_moves:
        move_san = board.san(move)
        board.push(move)
        sequence, eval = minimax(board, depth - 1, alpha, beta, not maximizing_player, depth2 + 1, depths, position_count, memo, start_time, last_print_time)
        board.pop()

        if (maximizing_player and eval > best_eval) or (not maximizing_player and eval < best_eval):
            best_eval = eval
            best_sequence = [(move, move_san)] + sequence

        if maximizing_player:
            alpha = max(alpha, eval)
        else:
            beta = min(beta, eval)

        if beta <= alpha:
            break

    memo[key] = (best_sequence, best_eval)
    print(f"\rHloubka {depth} prozkoumána, čas: {time.time() - start_time:.2f}s", end='', flush=True)  # Print when a depth is fully explored
    return best_sequence, best_eval

start_time = time.time()
position_count = [0]
memo = {}
last_print_time = [start_time]
depths = []
start_fen = "7k/8/3Q4/5K2/8/8/8/8 w - - 0 1"
board = chess.Board(start_fen)

print("Počáteční šachovnice:")
print(board)
print("Počáteční FEN:", board.fen(), "\n")

sequence, final_score = minimax(board, 5, float('-inf'), float('inf'), True, 0, depths, position_count, memo, start_time, last_print_time)
print("\n\nOptimal move sequence:")
for move, san in sequence:
    print("Move:", san)
    board.push(move)
    print("Board:\n", board)
    print("FEN:", board.fen())
    print("Evaluation:", evaluate_board(board, 0), "\n")
print("Final evaluation score:", final_score)