FURTHER ENHANCE:
W/Full Revised Code

```
# advanced_cte.py

import chess
import chess.pgn
import random
import math
import copy
import sys
import time
from collections import defaultdict, Counter
from threading import Thread, Lock
from queue import Queue
import pickle
import os
import logging
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor, as_completed

# Optional: For machine learning integration
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from sklearn.ensemble import RandomForestClassifier

# Optional: For GUI implementation
import tkinter as tk
from tkinter import messagebox

# Configure logging
logging.basicConfig(level=logging.INFO, filename='cte_log.log',
                    format='%(asctime)s - %(levelname)s - %(message)s')

# ============================================
# Zobrist Hashing for Transposition Tables
# ============================================
class ZobristHashing:
    """
    Implements Zobrist Hashing for unique board position identification.
    """
    def __init__(self):
        self.zobrist_table = {}
        self.initialize_zobrist()
    
    def initialize_zobrist(self):
        """
        Initializes the Zobrist hashing table with random bitstrings.
        """
        random.seed(0)  # For reproducibility
        pieces = [chess.KING, chess.QUEEN, chess.ROOK, chess.BISHOP, chess.KNIGHT, chess.PAWN]
        colors = [chess.WHITE, chess.BLACK]
        for piece in pieces:
            for color in colors:
                for square in chess.SQUARES:
                    self.zobrist_table[(piece, color, square)] = random.getrandbits(64)
        # Castling rights
        self.zobrist_table['K'] = random.getrandbits(64)
        self.zobrist_table['Q'] = random.getrandbits(64)
        self.zobrist_table['k'] = random.getrandbits(64)
        self.zobrist_table['q'] = random.getrandbits(64)
        # En passant
        self.zobrist_table['en_passant'] = random.getrandbits(64)
        # Side to move
        self.zobrist_table['side_to_move'] = random.getrandbits(64)
        logging.info("Zobrist hashing table initialized.")
    
    def compute_hash(self, board):
        """
        Computes the Zobrist hash for the given board state.
        """
        h = 0
        for square in chess.SQUARES:
            piece = board.piece_at(square)
            if piece:
                h ^= self.zobrist_table[(piece.piece_type, piece.color, square)]
        # Castling rights
        if board.has_kingside_castling_rights(chess.WHITE):
            h ^= self.zobrist_table['K']
        if board.has_queenside_castling_rights(chess.WHITE):
            h ^= self.zobrist_table['Q']
        if board.has_kingside_castling_rights(chess.BLACK):
            h ^= self.zobrist_table['k']
        if board.has_queenside_castling_rights(chess.BLACK):
            h ^= self.zobrist_table['q']
        # En passant
        if board.ep_square:
            h ^= self.zobrist_table['en_passant']
        # Side to move
        if board.turn:
            h ^= self.zobrist_table['side_to_move']
        return h

# ============================================
# Transposition Table
# ============================================
class TranspositionTable:
    """
    Caches evaluations of board positions to avoid redundant computations.
    """
    def __init__(self):
        self.table = {}
        self.lock = Lock()
    
    def store(self, zobrist_hash, depth, score, flag, best_move):
        """
        Stores an entry in the transposition table.
        """
        with self.lock:
            self.table[zobrist_hash] = {'depth': depth, 'score': score, 'flag': flag, 'best_move': best_move}
    
    def lookup(self, zobrist_hash, depth):
        """
        Looks up an entry in the transposition table.
        Returns the cached score and best_move if available and sufficient depth.
        """
        with self.lock:
            entry = self.table.get(zobrist_hash, None)
            if entry and entry['depth'] >= depth:
                return entry['score'], entry['best_move']
            return None, None

# ============================================
# Neural Network Evaluator
# ============================================
class NeuralNetworkEvaluator:
    """
    Evaluates chess board positions using a neural network.
    """
    def __init__(self, model_path='nnue_evaluator.h5'):  # Update default path
        if os.path.exists(model_path):
            self.model = load_model(model_path)
            logging.info("Neural Network model loaded successfully from nnue_evaluator.h5.")
        else:
            # Initialize a simple model if none exists
            self.model = self.build_default_model()
            # Note: In practice, train the model with extensive chess data
            self.model.save(model_path)
            logging.warning("Default Neural Network model created. Consider training a sophisticated model for better performance.")
    
    def build_default_model(self):
        """
        Builds and compiles a default neural network model.
        """
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(128, activation='relu', input_shape=(64,)),
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dense(1, activation='tanh')  # Output between -1 (Black advantage) to 1 (White advantage)
        ])
        model.compile(optimizer='adam', loss='mean_squared_error')
        logging.info("Default Neural Network model built and compiled.")
        return model
    
    def board_to_input(self, board):
        """
        Converts the chess board to a 64-length input vector for the neural network.
        Positive values represent White pieces, negative values represent Black pieces.
        """
        piece_map = board.piece_map()
        input_vector = np.zeros(64)
        for square in chess.SQUARES:
            piece = piece_map.get(square)
            if piece:
                index = square
                # Encode pieces: Pawn=1, Knight=2, Bishop=3, Rook=4, Queen=5, King=6
                piece_value = {chess.PAWN:1, chess.KNIGHT:2, chess.BISHOP:3,
                              chess.ROOK:4, chess.QUEEN:5, chess.KING:6}.get(piece.piece_type, 0)
                input_vector[index] = piece_value if piece.color == chess.WHITE else -piece_value
        return input_vector.reshape(1, 64)
    
    def evaluate(self, board):
        """
        Evaluates the board position and returns a score.
        """
        try:
            input_vector = self.board_to_input(board)
            score = self.model.predict(input_vector, verbose=0)[0][0]
            logging.debug(f"NeuralNetworkEvaluator score: {score}")
            return score
        except Exception as e:
            logging.error(f"Error in NeuralNetworkEvaluator.evaluate: {e}")
            # Fallback to a simple material evaluation
            return self.simple_evaluate(board)
    
    def simple_evaluate(self, board):
        """
        Simple fallback evaluation based on material count.
        """
        material = 0
        piece_values = {
            chess.PAWN: 1,
            chess.KNIGHT: 3,
            chess.BISHOP: 3,
            chess.ROOK: 5,
            chess.QUEEN: 9,
            chess.KING: 0  # Kings are invaluable; not counted
        }
        for piece_type, value in piece_values.items():
            material += len(board.pieces(piece_type, chess.WHITE)) * value
            material -= len(board.pieces(piece_type, chess.BLACK)) * value
        logging.debug(f"Simple evaluation score: {material}")
        return material

# ============================================
# Psychological Profiler
# ============================================
class PsychologicalProfiler:
    """
    Profiles opponent's playing style based on move history.
    """
    def __init__(self):
        self.opponent_move_history = []
        self.move_frequencies = defaultdict(int)
        self.move_counter = 0
        self.opponent_style = "neutral"  # Possible styles: 'aggressive', 'defensive', 'positional'
        self.move_sequences = []
        self.sequence_length = 3  # For sequence-based prediction
    
    def record_move(self, move, board):
        """
        Records an opponent's move and updates profiling.
        """
        self.opponent_move_history.append(move)
        self.move_frequencies[move.uci()] += 1
        self.move_counter += 1
        self.update_move_sequences()
        self.update_opponent_style(move, board)
    
    def get_common_moves(self):
        """
        Retrieves the most frequently used moves by the opponent.
        """
        if not self.move_frequencies:
            return []
        max_freq = max(self.move_frequencies.values())
        common_moves = [move for move, freq in self.move_frequencies.items() if freq == max_freq]
        return common_moves
    
    def predict_opponent_move(self):
        """
        Predicts the opponent's next move based on common moves and move sequences.
        """
        # Sequence-based prediction
        if len(self.opponent_move_history) >= self.sequence_length:
            recent_sequence = tuple(self.opponent_move_history[-self.sequence_length:])
            possible_moves = [seq[-1] for seq in self.move_sequences if seq[:-1] == recent_sequence[:-1]]
            if possible_moves:
                move_counts = Counter(possible_moves)
                most_common = move_counts.most_common()
                highest_freq = most_common[0][1]
                common_moves = [move for move, freq in most_common if freq == highest_freq]
                return [chess.Move.from_uci(move) for move in common_moves]
        
        # Fallback to frequency-based prediction
        return [chess.Move.from_uci(move) for move in self.get_common_moves()]
    
    def update_move_sequences(self):
        """
        Updates the list of move sequences for sequence-based prediction.
        """
        if len(self.opponent_move_history) >= self.sequence_length:
            sequence = tuple(self.opponent_move_history[-self.sequence_length:])
            self.move_sequences.append(sequence)
    
    def update_opponent_style(self, move, board):
        """
        Determines the opponent's playing style based on recent moves.
        """
        if self.move_counter < 10:
            self.opponent_style = "neutral"
            return
        aggressive_moves = sum(1 for m in self.opponent_move_history if self.is_aggressive_move(m, board))
        defensive_moves = sum(1 for m in self.opponent_move_history if self.is_defensive_move(m, board))
        positional_moves = self.move_counter - aggressive_moves - defensive_moves
    
        styles = {
            'aggressive': aggressive_moves,
            'defensive': defensive_moves,
            'positional': positional_moves
        }
    
        # Determine the predominant style
        self.opponent_style = max(styles, key=styles.get)
        logging.info(f"Opponent style updated to: {self.opponent_style}")
    
    def is_aggressive_move(self, move, board):
        """
        Identifies if a move is aggressive (e.g., captures, checks).
        """
        return board.is_capture(move) or board.gives_check(move)
    
    def is_defensive_move(self, move, board):
        """
        Identifies if a move is defensive (e.g., protecting king, blocking).
        """
        # Example criteria: move squares where king is safer or blocking checks
        return board.is_defense_move(move)  # Requires defining is_defense_move method

# ============================================
# Enhanced Decision Tree with Quiescence Search
# ============================================
class EnhancedDecisionTree:
    """
    Implements the Minimax algorithm with alpha-beta pruning, transposition tables,
    iterative deepening, and quiescence search.
    """
    def __init__(self, evaluator, depth=4, use_transposition=True):
        self.depth = depth
        self.evaluator = evaluator
        self.transposition_table = TranspositionTable() if use_transposition else None
        self.zobrist = ZobristHashing()
    
    def minimax(self, board, depth, alpha, beta, maximizing_player):
        """
        Performs the Minimax search with alpha-beta pruning and quiescence search.
        """
        zobrist_hash = self.zobrist.compute_hash(board)
        if self.transposition_table:
            cached = self.transposition_table.lookup(zobrist_hash, depth)
            if cached:
                cached_score, cached_move = cached
                return cached_score, cached_move
    
        if depth == 0:
            return self.quiescence_search(board, alpha, beta, maximizing_player), None
    
        if board.is_game_over():
            return self.evaluate_game_over(board), None
    
        legal_moves = list(board.legal_moves)
        # Move Ordering Heuristics: Prioritize captures, checks, promotions
        legal_moves = self.order_moves(board, legal_moves)
    
        best_move = None
        if maximizing_player:
            max_eval = -math.inf
            for move in legal_moves:
                board.push(move)
                eval, _ = self.minimax(board, depth - 1, alpha, beta, False)
                board.pop()
                if eval > max_eval:
                    max_eval = eval
                    best_move = move
                alpha = max(alpha, eval)
                if beta <= alpha:
                    break
            if self.transposition_table and best_move:
                self.transposition_table.store(zobrist_hash, depth, max_eval, 'exact', best_move.uci())
            return max_eval, best_move
        else:
            min_eval = math.inf
            for move in legal_moves:
                board.push(move)
                eval, _ = self.minimax(board, depth - 1, alpha, beta, True)
                board.pop()
                if eval < min_eval:
                    min_eval = eval
                    best_move = move
                beta = min(beta, eval)
                if beta <= alpha:
                    break
            if self.transposition_table and best_move:
                self.transposition_table.store(zobrist_hash, depth, min_eval, 'exact', best_move.uci())
            return min_eval, best_move
    
    def quiescence_search(self, board, alpha, beta, maximizing_player):
        """
        Extends the search in non-quiet positions to avoid the horizon effect.
        Only captures and checks are considered in quiescence search.
        """
        score = self.evaluate_board(board)
        if maximizing_player:
            alpha = max(alpha, score)
            if alpha >= beta:
                return beta
        else:
            beta = min(beta, score)
            if beta <= alpha:
                return alpha
    
        # Generate "noisy" moves: captures, checks
        noisy_moves = [move for move in board.legal_moves if board.is_capture(move) or board.gives_check(move)]
        # Move ordering for quiescence search
        noisy_moves = self.order_moves(board, noisy_moves)
    
        for move in noisy_moves:
            board.push(move)
            eval = self.quiescence_search(board, alpha, beta, not maximizing_player)
            board.pop()
            if maximizing_player:
                alpha = max(alpha, eval)
                if alpha >= beta:
                    return beta
            else:
                beta = min(beta, eval)
                if beta <= alpha:
                    return alpha
        return score
    
    def evaluate_game_over(self, board):
        """
        Evaluates the board when the game is over.
        """
        result = board.result()
        if result == '1-0':
            return math.inf  # White wins
        elif result == '0-1':
            return -math.inf  # Black wins
        elif result == '1/2-1/2':
            return 0  # Draw
        else:
            return 0  # Game still ongoing (should not reach here)
    
    def order_moves(self, board, moves):
        """
        Orders moves based on heuristics to improve alpha-beta pruning efficiency.
        Prioritizes captures, checks, promotions.
        """
        move_scores = []
        for move in moves:
            score = 0
            if board.is_capture(move):
                captured_piece = board.piece_at(move.to_square)
                piece_value = {chess.PAWN:100, chess.KNIGHT:320, chess.BISHOP:330,
                              chess.ROOK:500, chess.QUEEN:900}.get(captured_piece.piece_type, 0)
                score += piece_value
            if board.gives_check(move):
                score += 50
            if move.promotion:
                score += 1000  # Prioritize promotions
            move_scores.append((score, move))
        # Sort moves descending by score
        sorted_moves = [move for score, move in sorted(move_scores, key=lambda x: x[0], reverse=True)]
        return sorted_moves
    
    def evaluate_board(self, board):
        """
        Evaluates the board using the evaluator.
        """
        return self.evaluator.evaluate(board)
    
    def select_move(self, board):
        """
        Selects the best move using iterative deepening and quiescence search.
        """
        best_move = None
        best_score = -math.inf
        for depth in range(1, self.depth + 1):
            score, move = self.minimax(board, depth, -math.inf, math.inf, board.turn)
            if move:
                best_move = move
                best_score = score
                logging.info(f"Depth {depth}: Selected move {board.san(move)} with score {score}")
            # Optional: Implement time constraints here
        return best_move

# ============================================
# Strategic Deception
# ============================================
class StrategicDeception:
    """
    Implements strategic deception by occasionally making suboptimal moves.
    """
    def __init__(self, deception_rate=0.15):
        """
        deception_rate: Probability of making a deceptive move.
        """
        self.deception_rate = deception_rate
    
    def maybe_deceive(self, board, best_move):
        """
        Decides whether to make a deceptive move instead of the best move.
        """
        try:
            if random.random() < self.deception_rate and len(list(board.legal_moves)) > 1:
                # Select a deceptive move: non-capturing, not giving check, not promoting
                deceptive_moves = [move for move in board.legal_moves if not board.is_capture(move)
                                   and not board.gives_check(move) and not move.promotion]
                if deceptive_moves:
                    deceived_move = random.choice(deceptive_moves)
                    logging.info(f"StrategicDeception applied: {board.san(deceived_move)} instead of {board.san(best_move)}")
                    return deceived_move
        except Exception as e:
            logging.error(f"Error in StrategicDeception.maybe_deceive: {e}")
        return best_move

# ============================================
# Tactical Pattern Recognizer
# ============================================
class TacticalPatternRecognizer:
    """
    Recognizes tactical patterns to exploit opportunities.
    """
    def __init__(self):
        # Define patterns as functions or rules
        self.patterns = [
            self.fork_pattern,
            self.pin_pattern,
            self.skewer_pattern,
            self.discovered_attack_pattern,
            self.check_pattern
        ]
    
    def recognize_patterns(self, board):
        """
        Checks for any recognized tactical patterns.
        """
        for pattern in self.patterns:
            if pattern(board):
                logging.info(f"Tactical pattern recognized: {pattern.__name__}")
                return True
        return False
    
    def fork_pattern(self, board):
        """
        Detects if the current player can create a fork.
        """
        try:
            for move in board.legal_moves:
                board.push(move)
                attackers = len(list(board.attackers(not board.turn, board.king(not board.turn))))
                board.pop()
                if attackers >= 2:
                    return True
        except:
            pass
        return False
    
    def pin_pattern(self, board):
        """
        Detects if any piece is pinned.
        """
        try:
            for square in chess.SQUARES:
                if board.is_pinned(board.turn, square):
                    return True
        except:
            pass
        return False
    
    def skewer_pattern(self, board):
        """
        Detects skewers. Placeholder for future implementation.
        """
        # Advanced implementation would require pattern matching or external libraries
        return False  # Placeholder
    
    def discovered_attack_pattern(self, board):
        """
        Detects discovered attacks. Placeholder for future implementation.
        """
        # Advanced implementation would require pattern matching or external libraries
        return False  # Placeholder
    
    def check_pattern(self, board):
        """
        Detects if delivering check creates a tactical advantage.
        """
        try:
            for move in board.legal_moves:
                board.push(move)
                if board.is_check():
                    board.pop()
                    return True
                board.pop()
        except:
            pass
        return False

# ============================================
# Scenario Simulator
# ============================================
class ScenarioSimulator:
    """
    Simulates various scenarios in multiple threads to predict opponent responses.
    """
    def __init__(self, evaluator, depth=2, num_threads=4):
        self.depth = depth
        self.evaluator = evaluator
        self.num_threads = num_threads
        self.lock = Lock()
        self.executor = ThreadPoolExecutor(max_workers=self.num_threads)
    
    def simulate_move(self, board, move, queue):
        """
        Simulates a single move and evaluates the resulting position.
        """
        try:
            simulated_board = board.copy()
            simulated_board.push(move)
            score = self.evaluator.evaluate_board(simulated_board)
            queue.put((move, score))
        except Exception as e:
            logging.error(f"Error in ScenarioSimulator.simulate_move: {e}")
    
    def simulate(self, board, moves):
        """
        Simulates multiple moves concurrently and returns their evaluations.
        """
        queue = Queue()
        futures = []
        for move in moves:
            futures.append(self.executor.submit(self.simulate_move, board, move, queue))
        for future in as_completed(futures):
            pass  # Wait for all simulations to complete
        results = []
        while not queue.empty():
            results.append(queue.get())
        return results

# ============================================
# Machine Learning Predictor
# ============================================
class MachineLearningPredictor:
    """
    Predicts opponent's moves using a machine learning model.
    """
    def __init__(self, model_path='move_predictor.pkl'):  # Update default path
        self.model_path = model_path
        if os.path.exists(self.model_path):
            with open(self.model_path, 'rb') as f:
                self.model = pickle.load(f)
            self.trained = True
            logging.info("Machine Learning model loaded successfully from move_predictor.pkl.")
        else:
            self.model = RandomForestClassifier(n_estimators=100)
            self.trained = False
            logging.warning("Machine Learning model not found. Consider training it with PGN data.")
    
    def prepare_training_data(self, games_pgn_path):
        """
        Prepares and trains the machine learning model using PGN game data.
        """
        X = []
        y = []
        try:
            with open(games_pgn_path, 'r') as pgn_file:
                while True:
                    game = chess.pgn.read_game(pgn_file)
                    if game is None:
                        break
                    board = game.board()
                    for move in game.mainline_moves():
                        features = self.extract_features(board)
                        X.append(features)
                        y.append(move.uci())
                        board.push(move)
            if X and y:
                self.train(X, y)
                logging.info("Machine Learning model trained with PGN data.")
            else:
                logging.warning("No data found in PGN file for training.")
        except Exception as e:
            logging.error(f"Error in MachineLearningPredictor.prepare_training_data: {e}")
    
    def train(self, X, y):
        """
        Trains the machine learning model.
        """
        try:
            self.model.fit(X, y)
            with open(self.model_path, 'wb') as f:
                pickle.dump(self.model, f)
            self.trained = True
            logging.info("Machine Learning model trained and saved successfully.")
        except Exception as e:
            logging.error(f"Error in MachineLearningPredictor.train: {e}")
    
    def extract_features(self, board):
        """
        Extracts features from the board for the machine learning model.
        Currently uses material balance, piece activity, and control of the center.
        """
        material = 0
        piece_values = {
            chess.PAWN:1,
            chess.KNIGHT:3,
            chess.BISHOP:3,
            chess.ROOK:5,
            chess.QUEEN:9,
            chess.KING:0  # Kings are invaluable; not counted
        }
        for piece_type, value in piece_values.items():
            material += len(board.pieces(piece_type, chess.WHITE)) * value
            material -= len(board.pieces(piece_type, chess.BLACK)) * value
        
        # Piece Activity: Number of legal moves for each side
        white_activity = len(list(board.legal_moves)) if board.turn else len(list(board.legal_moves))
        black_activity = len(list(board.legal_moves)) if not board.turn else len(list(board.legal_moves))
        if board.turn:
            activity = white_activity
        else:
            activity = black_activity
        
        # Control of the Center: Number of pieces controlling central squares
        center_squares = [chess.E4, chess.D4, chess.E5, chess.D5]
        white_control = sum(1 for sq in center_squares if len(list(board.attackers(chess.WHITE, sq))) > 0)
        black_control = sum(1 for sq in center_squares if len(list(board.attackers(chess.BLACK, sq))) > 0)
        center_control = white_control - black_control
        
        return [material, activity, center_control]
    
    def predict(self, board):
        """
        Predicts the opponent's next move based on the current board state.
        """
        if not self.trained:
            logging.warning("Machine Learning model is not trained. Skipping prediction.")
            return None
        try:
            features = self.extract_features(board)
            predicted_move_uci = self.model.predict([features])[0]
            move = chess.Move.from_uci(predicted_move_uci)
            if move in board.legal_moves:
                logging.info(f"Machine Learning Predictor suggests move: {board.san(move)}")
                return move
            else:
                logging.warning(f"Predicted move {move} is illegal.")
        except Exception as e:
            logging.error(f"Error in MachineLearningPredictor.predict: {e}")
        return None

# ============================================
# Advanced Psychological Profiler
# ============================================
class AdvancedPsychologicalProfiler(PsychologicalProfiler):
    """
    Extends PsychologicalProfiler to analyze sequences of moves for better prediction.
    """
    def __init__(self):
        super().__init__()
    
    # Additional methods can be implemented here for deeper profiling

# ============================================
# Opening Book
# ============================================
class OpeningBook:
    """
    Manages opening book moves loaded from a PGN file.
    """
    def __init__(self, book_path='opening_book.pgn'):
        self.book = {}
        if os.path.exists(book_path):
            self.load_book(book_path)
            logging.info("Opening book loaded successfully.")
        else:
            logging.warning(f"Opening book file '{book_path}' not found. Proceeding without it.")
    
    def load_book(self, book_path):
        """
        Loads the opening book from a PGN file.
        """
        try:
            with open(book_path, 'r') as pgn_file:
                while True:
                    game = chess.pgn.read_game(pgn_file)
                    if game is None:
                        break
                    board = game.board()
                    for move in game.mainline_moves():
                        board.push(move)
                        fen = board.fen()
                        if fen not in self.book:
                            self.book[fen] = []
                        self.book[fen].append(move)
            logging.info(f"Total openings loaded: {len(self.book)}")
        except Exception as e:
            logging.error(f"Error loading opening book: {e}")
    
    def get_opening_move(self, board):
        """
        Retrieves an opening move if available for the current board state.
        """
        try:
            fen = board.fen()
            moves = self.book.get(fen, [])
            if moves:
                move = random.choice(moves)
                logging.info(f"OpeningBook suggests move: {board.san(move)}")
                return move
        except Exception as e:
            logging.error(f"Error in OpeningBook.get_opening_move: {e}")
        return None

# ============================================
# Endgame Tablebase
# ============================================
class EndgameTablebase:
    """
    Utilizes precomputed endgame tablebases for perfect play in simplified positions.
    """
    def __init__(self, tablebase_path='endgame_tablebases/'):
        self.tablebase = {}
        if os.path.exists(tablebase_path):
            self.load_tablebases(tablebase_path)
            logging.info("Endgame tablebases loaded successfully.")
        else:
            logging.warning(f"Endgame tablebase directory '{tablebase_path}' not found. Proceeding without it.")
    
    def load_tablebases(self, tablebase_path):
        """
        Loads endgame tablebases from pickled files.
        """
        try:
            for filename in os.listdir(tablebase_path):
                if filename.endswith('.pkl'):
                    with open(os.path.join(tablebase_path, filename), 'rb') as f:
                        data = pickle.load(f)
                        self.tablebase.update(data)
            logging.info(f"Total endgame positions loaded: {len(self.tablebase)}")
        except Exception as e:
            logging.error(f"Error loading endgame tablebases: {e}")
    
    def get_endgame_move(self, board):
        """
        Retrieves an optimal endgame move if the current board state is in the tablebase.
        """
        try:
            fen = board.fen()
            move_uci = self.tablebase.get(fen, None)
            if move_uci:
                move = chess.Move.from_uci(move_uci)
                if move in board.legal_moves:
                    logging.info(f"EndgameTablebase suggests move: {board.san(move)}")
                    return move
        except Exception as e:
            logging.error(f"Error in EndgameTablebase.get_endgame_move: {e}")
        return None

# ============================================
# Cognitive Tactical Engine (CTE)
# ============================================
class CognitiveTacticalEngine:
    """
    Integrates all components to form the Cognitive Tactical Engine (CTE).
    """
    def __init__(self):
        self.zobrist = ZobristHashing()
        self.nn_evaluator = NeuralNetworkEvaluator()
        self.transposition_table = TranspositionTable()
        self.profiler = AdvancedPsychologicalProfiler()
        self.opening_book = OpeningBook()
        self.endgame_tablebase = EndgameTablebase()
        self.decision_tree = EnhancedDecisionTree(self.nn_evaluator, depth=4, use_transposition=True)
        self.deception = StrategicDeception(deception_rate=0.15)
        self.pattern_recognizer = TacticalPatternRecognizer()
        self.simulator = ScenarioSimulator(self.decision_tree, depth=3, num_threads=8)
        self.ml_predictor = MachineLearningPredictor()
        self.ml_predictor_loaded = False
        self.load_or_train_ml_model()
    
    def load_or_train_ml_model(self):
        """
        Loads the machine learning predictor if trained; otherwise, initializes it.
        """
        if self.ml_predictor.trained:
            self.ml_predictor_loaded = True
            logging.info("Machine Learning Predictor is ready.")
        else:
            # Placeholder: Train model if possible
            # To train, call self.ml_predictor.prepare_training_data with a valid PGN file
            logging.warning("Machine Learning Predictor is not trained with actual data.")
    
    def select_move(self, board):
        """
        Selects the best move by integrating various strategies and components.
        """
        try:
            # 1. Check if in opening book
            opening_move = self.opening_book.get_opening_move(board)
            if opening_move:
                return opening_move
    
            # 2. Check endgame tablebase
            endgame_move = self.endgame_tablebase.get_endgame_move(board)
            if endgame_move:
                return endgame_move
    
            # 3. Use Machine Learning Predictor
            ml_move = self.ml_predictor.predict(board)
            if ml_move and ml_move in board.legal_moves:
                return ml_move
    
            # 4. Use Decision Tree to select best move
            best_move = self.decision_tree.select_move(board)
    
            # 5. Apply Strategic Deception
            move = self.deception.maybe_deceive(board, best_move)
    
            # 6. Tactical Pattern Recognition
            if self.pattern_recognizer.recognize_patterns(board):
                capturing_moves = [m for m in board.legal_moves if board.is_capture(m)]
                if capturing_moves:
                    move = random.choice(capturing_moves)
                    logging.info(f"TacticalPatternRecognizer adjusted move to: {board.san(move)}")
    
            return move
        except Exception as e:
            logging.error(f"Error in CognitiveTacticalEngine.select_move: {e}")
            # Fallback to a random legal move
            return self.fallback_move(board)
    
    def fallback_move(self, board):
        """
        Selects a random legal move as a fallback strategy.
        """
        try:
            move = random.choice(list(board.legal_moves))
            logging.info(f"Fallback move selected: {board.san(move)}")
            return move
        except Exception as e:
            logging.error(f"Error in fallback_move: {e}")
            return None
    
    def play_move(self, board):
        """
        Executes the selected move on the board.
        """
        move = self.select_move(board)
        if move:
            board.push(move)
            logging.info(f"CTE played move: {board.san(move)}")
            return move
        logging.warning("CTE could not make a move.")
        return None

# ============================================
# Opponent Players
# ============================================
class RandomPlayer:
    """
    Opponent that selects random legal moves.
    """
    def __init__(self):
        pass
    
    def select_move(self, board):
        """
        Selects a random legal move.
        """
        try:
            move = random.choice(list(board.legal_moves)) if board.legal_moves else None
            if move:
                logging.info(f"RandomPlayer selected move: {board.san(move)}")
            else:
                logging.info("RandomPlayer has no legal moves.")
            return move
        except Exception as e:
            logging.error(f"Error in RandomPlayer.select_move: {e}")
            return None

class CheckmateOrCapturePlayer:
    """
    Opponent that prioritizes captures and checkmates.
    """
    def __init__(self):
        pass
    
    def select_move(self, board):
        """
        Selects a capture move if available; otherwise, selects a move leading to checkmate.
        If neither, selects a random move.
        """
        try:
            if not board.legal_moves:
                logging.info("CheckmateOrCapturePlayer has no legal moves.")
                return None
    
            legal_moves = list(board.legal_moves)
            capturing_moves = [move for move in legal_moves if board.is_capture(move)]
            if capturing_moves:
                # Prioritize captures that gain the most material
                capturing_moves.sort(key=lambda move: self.capture_value(board, move), reverse=True)
                move = capturing_moves[0]
                logging.info(f"CheckmateOrCapturePlayer selected capture move: {board.san(move)}")
                return move
    
            # Try to find a move that results in checkmate
            for move in legal_moves:
                board.push(move)
                if board.is_checkmate():
                    board.pop()
                    logging.info(f"CheckmateOrCapturePlayer found checkmate move: {board.san(move)}")
                    return move
                board.pop()
    
            # Otherwise, pick a random move
            move = random.choice(legal_moves)
            logging.info(f"CheckmateOrCapturePlayer selected random move: {board.san(move)}")
            return move
        except Exception as e:
            logging.error(f"Error in CheckmateOrCapturePlayer.select_move: {e}")
            return None
    
    def capture_value(self, board, move):
        """
        Assigns a value to a capture move based on the piece being captured.
        Higher-valued captures are prioritized.
        """
        try:
            captured_piece = board.piece_at(move.to_square)
            if captured_piece:
                piece_value = {chess.PAWN:100, chess.KNIGHT:320, chess.BISHOP:330,
                              chess.ROOK:500, chess.QUEEN:900}.get(captured_piece.piece_type, 0)
                return piece_value
            return 0
        except:
            return 0

# ============================================
# Graphical User Interface (GUI)
# ============================================
class ChessGUI:
    """
    Graphical User Interface for interacting with the CTE.
    """
    def __init__(self, root, engine, opponent):
        self.root = root
        self.engine = engine
        self.opponent = opponent
        self.board = chess.Board()
        self.canvas = tk.Canvas(root, width=480, height=480)
        self.canvas.pack()
        self.draw_board()
        self.canvas.bind("<Button-1>", self.on_click)
        self.selected_square = None
        self.update_board()
        self.game_over = False
    
    def draw_board(self):
        """
        Draws the chessboard squares.
        """
        colors = ["#F0D9B5", "#B58863"]  # Light and dark squares
        square_size = 60
        for i in range(8):
            for j in range(8):
                color = colors[(i + j) % 2]
                self.canvas.create_rectangle(j*square_size, i*square_size,
                                             (j+1)*square_size, (i+1)*square_size,
                                             fill=color, tags="square")
        logging.info("GUI chessboard drawn.")
    
    def update_board(self):
        """
        Updates the GUI to reflect the current board state.
        """
        self.canvas.delete("piece")
        for square in chess.SQUARES:
            piece = self.board.piece_at(square)
            if piece:
                x = (square % 8) * 60 + 30
                y = (7 - square // 8) * 60 + 30
                piece_unicode = self.get_piece_unicode(piece)
                self.canvas.create_text(x, y, text=piece_unicode,
                                        font=("Arial", 32), tags="piece")
        self.root.update_idletasks()
        logging.debug("GUI board updated.")
    
    def get_piece_unicode(self, piece):
        """
        Returns the Unicode character for a given piece.
        """
        unicode_pieces = {
            'P': '♙',
            'N': '♘',
            'B': '♗',
            'R': '♖',
            'Q': '♕',
            'K': '♔',
            'p': '♟︎',
            'n': '♞',
            'b': '♝',
            'r': '♜',
            'q': '♛',
            'k': '♚'
        }
        return unicode_pieces.get(piece.symbol(), '')
    
    def on_click(self, event):
        """
        Handles user clicks on the chessboard.
        """
        if self.game_over:
            return
        
        square_size = 60
        col = event.x // square_size
        row = event.y // square_size
        square = chess.square(col, 7 - row)
    
        if self.selected_square is None:
            if self.board.piece_at(square) and self.board.turn == chess.WHITE:
                self.selected_square = square
                logging.info(f"Player selected square: {chess.square_name(square)}")
        else:
            move = chess.Move(self.selected_square, square)
            if move in self.board.legal_moves:
                self.board.push(move)
                logging.info(f"Player moved: {self.board.san(move)}")
                self.update_board()
                self.root.update()
                
                if self.board.is_game_over():
                    self.display_game_over()
                    return
                                
                # Opponent's turn
                opponent_move = self.opponent.select_move(self.board)
                if opponent_move:
                    self.board.push(opponent_move)
                    logging.info(f"Opponent moved: {self.board.san(opponent_move)}")
                    self.update_board()
                    self.root.update()
        
                    if self.board.is_game_over():
                        self.display_game_over()
                        return
            else:
                logging.warning(f"Invalid move attempted: {move}")
            self.selected_square = None
    
    def display_game_over(self):
        """
        Displays a message indicating the game is over.
        """
        self.game_over = True
        result = self.board.result()
        if result == "1-0":
            message = "Game Over! White (Player) wins."
        elif result == "0-1":
            message = "Game Over! Black (Opponent) wins."
        elif result == "1/2-1/2":
            message = "Game Over! It's a draw."
        else:
            message = "Game Over!"
        messagebox.showinfo("Game Over", message)
        logging.info(f"Game Over: {message}")

# ============================================
# Command Line Interface (CLI)
# ============================================
class ChessEngineCLI:
    """
    Command Line Interface for interacting with the CTE.
    """
    def __init__(self, engine, opponent):
        self.engine = engine
        self.opponent = opponent
        self.board = chess.Board()
        self.game = chess.pgn.Game()
        self.node = self.game
    
    def start_game(self):
        """
        Starts a game between the CTE and the opponent.
        """
        while not self.board.is_game_over():
            print(self.board)
            print("")
    
            if self.board.turn == chess.WHITE:
                # CTE's turn
                move = self.engine.play_move(self.board)
                if move:
                    print(f"CTE played: {self.board.san(move)}\n")
                    self.node = self.node.add_variation(move)
                    # Record opponent's move for profiling if needed
                else:
                    print("CTE has no legal moves.\n")
            else:
                # Opponent's turn
                move = self.opponent.select_move(self.board)
                if move:
                    self.board.push(move)
                    self.node = self.node.add_variation(move)
                    logging.info(f"Opponent played move: {self.board.san(move)}")
                    print(f"Opponent played: {self.board.san(move)}\n")
                    # Record opponent's move for profiling
                else:
                    print("Opponent has no legal moves.\n")
        
        print("Game Over!")
        print(self.board)
        print("\nResult:", self.get_result())
        self.game.headers["Result"] = self.board.result()
        self.game.headers["White"] = "CTE"
        self.game.headers["Black"] = self.opponent.__class__.__name__
        self.save_game_log()
    
    def get_result(self):
        """
        Returns a string representation of the game result.
        """
        result = self.board.result()
        if result == "1-0":
            return "White (CTE) wins"
        elif result == "0-1":
            return "Black (Opponent) wins"
        elif result == "1/2-1/2":
            return "Draw"
        else:
            return "Game ongoing or terminated"
    
    def save_game_log(self):
        """
        Saves the game log to a PGN file.
        """
        pgn_filename = "game_log_cli.pgn"
        try:
            with open(pgn_filename, "w") as pgn_file:
                exporter = chess.pgn.FileExporter(pgn_file)
                self.game.accept(exporter)
            print(f"Game log saved to '{pgn_filename}'.")
            logging.info(f"Game log saved to '{pgn_filename}'.")
        except Exception as e:
            print(f"Error saving game log: {e}")
            logging.error(f"Error saving game log: {e}")

# ============================================
# Chess Engine: Combining All Components
# ============================================
def play_game(engine, opponent, mode='CLI'):
    """
    Facilitates the game between the engine and the opponent based on the selected mode.
    """
    if mode == 'CLI':
        cli = ChessEngineCLI(engine, opponent)
        cli.start_game()
    elif mode == 'GUI':
        root = tk.Tk()
        root.title("Cognitive Tactical Engine (CTE) vs. Opponent")
        gui = ChessGUI(root, engine, opponent)
        root.mainloop()
    else:
        logging.error("Invalid game mode selected.")
        print("Invalid game mode selected.")

# ============================================
# Main Function
# ============================================
def main():
    """
    Main function to initialize components and start the game.
    """
    print("Welcome to the Advanced Cognitive Tactical Engine (CTE)!\n")
    print("Select opponent type:")
    print("1. Random Player")
    print("2. Checkmate or Capture Player")
    opponent_choice = input("Enter choice (1 or 2): ")
    
    if opponent_choice == '1':
        opponent = RandomPlayer()
    elif opponent_choice == '2':
        opponent = CheckmateOrCapturePlayer()
    else:
        print("Invalid choice. Defaulting to Random Player.")
        opponent = RandomPlayer()
    
    cte = CognitiveTacticalEngine()
    
    print("\nSelect mode:")
    print("1. Command Line Interface (CLI)")
    print("2. Graphical User Interface (GUI)")
    mode_choice = input("Enter choice (1 or 2): ")
    
    if mode_choice == '1':
        play_game(cte, opponent, mode='CLI')
    elif mode_choice == '2':
        play_game(cte, opponent, mode='GUI')
    else:
        print("Invalid mode selected.")
        logging.error("Invalid mode selected by user.")

if __name__ == "__main__":
    main()