very good best moves with ASCII boards

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):
    """Minimax algorithm with alpha-beta pruning."""
    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] = (None, eval)
        return None, eval

    best_move = None
    if maximizing_player:
        max_eval = float('-inf')
        for move in board.legal_moves:
            board.push(move)
            _, eval = minimax(board, depth - 1, alpha, beta, False, depth2 + 1, depths, position_count, memo, start_time, last_print_time)
            board.pop()
            if eval > max_eval:
                max_eval = eval
                best_move = move
            alpha = max(alpha, eval)
            if beta <= alpha:
                break
        memo[key] = (best_move, max_eval)
        return best_move, max_eval
    else:
        min_eval = float('inf')
        for move in board.legal_moves:
            board.push(move)
            _, eval = minimax(board, depth - 1, alpha, beta, True, depth2 + 1, depths, position_count, memo, start_time, last_print_time)
            board.pop()
            if eval < min_eval:
                min_eval = eval
                best_move = move
            beta = min(beta, eval)
            if beta <= alpha:
                break
        memo[key] = (best_move, min_eval)
        return best_move, min_eval

# Initialization and main execution logic
start_fen = "7k/8/3Q4/5K2/8/8/8/8 w - - 0 1"

start_fen = "7k/8/3Q4/5K2/8/8/8/8 w - - 0 1"

board = chess.Board(start_fen)

# Tisk počáteční šachovnice a jejího FEN
print("Počáteční šachovnice:")
print(board)
print("Počáteční FEN:", board.fen(), "\n")



sequence = []
depths = []
position_count = [0]
memo = {}
start_time = time.time()
last_print_time = [start_time]  # Initialize last print time

move_sequence = []
best_move, best_score = minimax(board, 3, float('-inf'), float('inf'), True, 0, depths, position_count, memo, start_time, last_print_time)
while best_move:
    move_sequence.append(best_move)
    board.push(best_move)
    print(board, "\nFEN:", board.fen(), "\n")  # Printing each board state with FEN
    if board.is_game_over():
        break
    best_move, _ = minimax(board, 5, float('-inf'), float('inf'), board.turn, 0, depths, position_count, memo, start_time, last_print_time)