Further enhance, and add superficial superpositional method into the AI and add a more advanced chain-of-thought chess processing.

```
import chess
import chess.pgn
import g4f
from g4f.client import Client
import random
import re
import time
from typing import List, Tuple, Optional
import numpy as np

class NapoleonChessEngine:
    def __init__(self):
        self.board = chess.Board()
        self.client = Client(provider=g4f.Provider.Bixin123)
        self.depth = 5
        self.opening_book = self.create_opening_book()
        self.game_history = chess.pgn.Game()
        self.current_node = self.game_history
        self.personality_traits = [
            "Aggressive",
            "Tactically brilliant",
            "Risk-taker",
            "Center-focused",
            "Rapid development"
        ]
        self.piece_values = {
            chess.PAWN: 100,
            chess.KNIGHT: 320,
            chess.BISHOP: 330,
            chess.ROOK: 500,
            chess.QUEEN: 900,
            chess.KING: 20000
        }
        self.position_tables = self.create_position_tables()
        self.transposition_table = {}
        self.killer_moves = [[None, None] for _ in range(100)]
        self.history_table = np.zeros((64, 64), dtype=np.int32)

    def create_opening_book(self):
        # Expanded opening book
        return {
            "": ["e2e4", "d2d4", "c2c4", "g1f3"],
            "e2e4": ["e7e5", "c7c5", "e7e6", "c7c6"],
            "d2d4": ["d7d5", "g8f6", "e7e6", "c7c5"],
            "c2c4": ["e7e5", "c7c5", "g8f6", "e7e6"],
            "g1f3": ["d7d5", "g8f6", "c7c5", "e7e6"],
            "e2e4 e7e5": ["g1f3", "b1c3", "f2f4"],
            "d2d4 d7d5": ["c2c4", "g1f3", "b1c3"],
            "c2c4 e7e5": ["b1c3", "g1f3", "g2g3"],
            "g1f3 d7d5": ["d2d4", "c2c4", "g2g3"]
        }

    def create_position_tables(self):
        return {
            chess.PAWN: [
                0,  0,  0,  0,  0,  0,  0,  0,
                50, 50, 50, 50, 50, 50, 50, 50,
                10, 10, 20, 30, 30, 20, 10, 10,
                5,  5, 10, 25, 25, 10,  5,  5,
                0,  0,  0, 20, 20,  0,  0,  0,
                5, -5,-10,  0,  0,-10, -5,  5,
                5, 10, 10,-20,-20, 10, 10,  5,
                0,  0,  0,  0,  0,  0,  0,  0
            ],
            chess.KNIGHT: [
                -50,-40,-30,-30,-30,-30,-40,-50,
                -40,-20,  0,  0,  0,  0,-20,-40,
                -30,  0, 10, 15, 15, 10,  0,-30,
                -30,  5, 15, 20, 20, 15,  5,-30,
                -30,  0, 15, 20, 20, 15,  0,-30,
                -30,  5, 10, 15, 15, 10,  5,-30,
                -40,-20,  0,  5,  5,  0,-20,-40,
                -50,-40,-30,-30,-30,-30,-40,-50
            ],
            chess.BISHOP: [
                -20,-10,-10,-10,-10,-10,-10,-20,
                -10,  0,  0,  0,  0,  0,  0,-10,
                -10,  0,  5, 10, 10,  5,  0,-10,
                -10,  5,  5, 10, 10,  5,  5,-10,
                -10,  0, 10, 10, 10, 10,  0,-10,
                -10, 10, 10, 10, 10, 10, 10,-10,
                -10,  5,  0,  0,  0,  0,  5,-10,
                -20,-10,-10,-10,-10,-10,-10,-20
            ],
            chess.ROOK: [
                0,  0,  0,  0,  0,  0,  0,  0,
                5, 10, 10, 10, 10, 10, 10,  5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                0,  0,  0,  5,  5,  0,  0,  0
            ],
            chess.QUEEN: [
                -20,-10,-10, -5, -5,-10,-10,-20,
                -10,  0,  0,  0,  0,  0,  0,-10,
                -10,  0,  5,  5,  5,  5,  0,-10,
                -5,  0,  5,  5,  5,  5,  0, -5,
                0,  0,  5,  5,  5,  5,  0, -5,
                -10,  5,  5,  5,  5,  5,  0,-10,
                -10,  0,  5,  0,  0,  0,  0,-10,
                -20,-10,-10, -5, -5,-10,-10,-20
            ],
            chess.KING: [
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -20,-30,-30,-40,-40,-30,-30,-20,
                -10,-20,-20,-20,-20,-20,-20,-10,
                20, 20,  0,  0,  0,  0, 20, 20,
                20, 30, 10,  0,  0, 10, 30, 20
            ]
        }

    def get_legal_moves(self) -> List[str]:
        return list(map(self.board.san, self.board.legal_moves))

    def is_legal_move(self, move: str) -> bool:
        try:
            self.board.push_san(move)
            self.board.pop()
            return True
        except ValueError:
            return False

    def make_move(self, move: str) -> Optional[str]:
        try:
            move_obj = self.board.parse_san(move)
            self.board.push(move_obj)
            self.current_node = self.current_node.add_variation(move_obj)
            return None
        except ValueError:
            return "Invalid move. Please try again."

    def evaluate_position(self) -> int:
        if self.board.is_checkmate():
            return -99999 if self.board.turn else 99999
        if self.board.is_stalemate() or self.board.is_insufficient_material():
            return 0

        material = sum(len(self.board.pieces(piece_type, chess.WHITE)) * value -
                       len(self.board.pieces(piece_type, chess.BLACK)) * value
                       for piece_type, value in self.piece_values.items())

        position = sum(self.position_tables[piece_type][square] 
                       for piece_type in chess.PIECE_TYPES
                       for square in self.board.pieces(piece_type, chess.WHITE))
        position -= sum(self.position_tables[piece_type][chess.square_mirror(square)] 
                        for piece_type in chess.PIECE_TYPES
                        for square in self.board.pieces(piece_type, chess.BLACK))

        # Mobility
        mobility = len(list(self.board.legal_moves))
        if not self.board.turn:
            self.board.push(chess.Move.null())
            mobility -= len(list(self.board.legal_moves))
            self.board.pop()
        else:
            self.board.push(chess.Move.null())
            mobility += len(list(self.board.legal_moves))
            self.board.pop()

        # Pawn structure
        pawn_structure = self.evaluate_pawn_structure()

        # King safety
        king_safety = self.evaluate_king_safety()

        eval = material + position + mobility * 10 + pawn_structure + king_safety

        return eval if self.board.turn else -eval

    def evaluate_pawn_structure(self) -> int:
        white_pawns = self.board.pieces(chess.PAWN, chess.WHITE)
        black_pawns = self.board.pieces(chess.PAWN, chess.BLACK)
        
        doubled_pawns = sum(len(list(filter(lambda sq: sq & 7 == file, white_pawns))) > 1
                            for file in range(8))
        doubled_pawns -= sum(len(list(filter(lambda sq: sq & 7 == file, black_pawns))) > 1
                             for file in range(8))
        
        isolated_pawns = sum(not any(sq & 7 in (file - 1, file + 1) for sq in white_pawns)
                             for file in range(8) if any(sq & 7 == file for sq in white_pawns))
        isolated_pawns -= sum(not any(sq & 7 in (file - 1, file + 1) for sq in black_pawns)
                              for file in range(8) if any(sq & 7 == file for sq in black_pawns))
        
        return -doubled_pawns * 50 - isolated_pawns * 30

    def evaluate_king_safety(self) -> int:
        white_king_sq = self.board.king(chess.WHITE)
        black_king_sq = self.board.king(chess.BLACK)
        
        white_attackers = len(self.board.attackers(chess.BLACK, white_king_sq))
        black_attackers = len(self.board.attackers(chess.WHITE, black_king_sq))
        
        return (black_attackers - white_attackers) * 50

    def generate_smart_prompt(self) -> str:
        evaluation = self.evaluate_position()
        legal_moves = self.get_legal_moves()
        return f"""As Napoleon, analyze this chess position and suggest the best move:
FEN: {self.board.fen()}
Move history: {" ".join([move.uci() for move in self.board.move_stack])}
Legal moves: {", ".join(legal_moves)}

The current evaluation score is:
{evaluation} (A positive score means White is winning, while a negative score means Black is winning.)

Consider these aspects of my playstyle:
1. Aggressive control of the center
2. Swift development of pieces
3. Calculated risks for positional advantages
4. Emphasis on mobility and rapid attacks
5. Willingness to sacrifice for initiative

Analyze the position step by step:
1. Material balance
2. Piece activity and development
3. King safety
4. Pawn structure
5. Control of key squares and lines

Choose the best legal move from the provided options to maximize your winning chances. Ensure the move is valid. Explain your thought process and the reasons behind your choice in a step-by-step manner, reflecting Napoleon's strategic thinking.

Move: [Your chosen move]
Explanation:
1. [First step of your reasoning]
2. [Second step of your reasoning]
3. [Third step of your reasoning]
...
Conclusion: [Summarize why this move aligns with Napoleon's style and the current position]"""

    def get_napoleon_move(self) -> Optional[chess.Move]:
        prompt = self.generate_smart_prompt()
        response = self.client.chat.completions.create(
            model="gpt-4-turbo",
            messages=[{"role": "user", "content": prompt}]
        )
        content = response.choices[0].message.content
        move_match = re.search(r"Move:\s*(\S+)", content)
        explanation = re.search(r"Explanation:(.*?)Conclusion:", content, re.DOTALL)
        conclusion = re.search(r"Conclusion:\s*(.*)", content)
        
        if move_match and self.is_legal_move(move_match.group(1)):
            move = move_match.group(1)
            print(f"Napoleon's move: {move}")
            if explanation:
                print(f"Explanation:\n{explanation.group(1).strip()}")
            if conclusion:
                print(f"Conclusion: {conclusion.group(1).strip()}")
            return self.board.parse_san(move)
        return None

    def iterative_deepening(self, time_limit=5):
        best_move = None
        start_time = time.time()
        for current_depth in range(1, self.depth + 1):
            move, _ = self.negamax(current_depth, float('-inf'), float('inf'), 1)
            if move:
                best_move = move
            if time.time() - start_time > time_limit:
                break
        return best_move

    def negamax(self, depth, alpha, beta, color):
        alpha_orig = alpha

        # Transposition table lookup
        tt_entry = self.transposition_table.get(self.board.fen())
        if tt_entry and tt_entry['depth'] >= depth:
            if tt_entry['flag'] == 'EXACT':
                return tt_entry['move'], tt_entry['score']
            elif tt_entry['flag'] == 'LOWERBOUND':
                alpha = max(alpha, tt_entry['score'])
            elif tt_entry['flag'] == 'UPPERBOUND':
                beta = min(beta, tt_entry['score'])
            if alpha >= beta:
                return tt_entry['move'], tt_entry['score']

        if depth == 0 or self.board.is_game_over():
            return None, color * self.quiescence_search(alpha, beta)

        best_move = None
        max_score = float('-inf')
        for move in self.get_sorted_moves():
            self.board.push(move)
            _, score = self.negamax(depth - 1, -beta, -alpha, -color)
            score = -score
            self.board.pop()

            if score > max_score:
                max_score = score
                best_move = move

            alpha = max(alpha, score)
            if alpha >= beta:
                self.killer_moves[depth] = [self.killer_moves[depth][1], move]
                break

        # Transposition table store
        flag = 'EXACT'
        if max_score <= alpha_orig:
            flag = 'UPPERBOUND'
        elif max_score >= beta:
            flag = 'LOWERBOUND'
        self.transposition_table[self.board.fen()] = {
            'move': best_move,
            'score': max_score,
            'depth': depth,
            'flag': flag
        }

        return best_move, max_score

    def quiescence_search(self, alpha, beta):
        stand_pat = self.evaluate_position()
        if stand_pat >= beta:
            return beta
        if alpha < stand_pat:
            alpha = stand_pat

        for move in self.board.legal_moves:
            if self.board.is_capture(move):
                self.board.push(move)
                score = -self.quiescence_search(-beta, -alpha)
                self.board.pop()

                if score >= beta:
                    return beta
                if score > alpha:
                    alpha = score
        return alpha

    def get_sorted_moves(self):
        def move_value(move):
            if move == self.killer_moves[len(self.board.move_stack)][0]:
                return 9000
            if move == self.killer_moves[len(self.board.move_stack)][1]:
                return 8000
            if self.board.is_capture(move):
                return 7000 + self.mvv_lva(move)
            return self.history_table[move.from_square][move.to_square]

        return sorted(self.board.legal_moves, key=move_value, reverse=True)

    def mvv_lva(self, move):
        if self.board.is_en_passant(move):
            return self.piece_values[chess.PAWN]
        return (self.piece_values[self.board.piece_type_at(move.to_square)] * 100 -
                self.piece_values[self.board.piece_type_at(move.from_square)])

    def get_opening_move(self):
        move_history = " ".join([move.uci() for move in self.board.move_stack])
        if move_history in self.opening_book:
            return random.choice(self.opening_book[move_history])
        return None

    def play_engine_move(self):
        opening_move = self.get_opening_move()
        if opening_move:
            chosen_move = chess.Move.from_uci(opening_move)
        else:
            napoleon_move = self.get_napoleon_move()
            calculated_move = self.iterative_deepening()
            
            # 50/50 chance of choosing Napoleon's move or the calculated move
            chosen_move = napoleon_move if napoleon_move and random.random() < 0.5 else calculated_move
        
        if chosen_move:
            self.board.push(chosen_move)
            self.current_node = self.current_node.add_variation(chosen_move)
            self.history_table[chosen_move.from_square][chosen_move.to_square] += self.depth * self.depth
        return chosen_move

    def display_board(self):
        return str(self.board)

    def get_game_pgn(self):
        game = self.game_history.game()
        game.headers["Event"] = "Napoleon vs Human"
        game.headers["Site"] = "G4F Chess Engine"
        game.headers["Date"] = time.strftime("%Y.%m.%d")
        return str(game)

def main():
    engine = NapoleonChessEngine()
    print("Welcome to the Enhanced Napoleon Chess Engine!")
    print("Enter your moves in standard algebraic notation (e.g., e4, Nf3).")
    print("Napoleon's personality traits:", ", ".join(engine.personality_traits))

    while not engine.board.is_game_over():
        print(engine.display_board())
        print("FEN:", engine.board.fen())
        print("Evaluation:", engine.evaluate_position())
        print("Legal moves:", engine.get_legal_moves())
        
        if engine.board.turn == chess.WHITE:
            user_move = input("Your move: ")
            if user_move.lower() == 'quit':
                break
            response = engine.make_move(user_move)
            if response:
                print(response)
                continue
        else:
            engine_move = engine.play_engine_move()
            if not engine_move:
                print("Napoleon failed to make a move. Game over.")
                break
            print(f"Napoleon's move: {engine_move.uci()}")

    print("Game over!")
    print(engine.display_board())
    print("\nGame PGN:")
    print(engine.get_game_pgn())

if __name__ == "__main__":
    main()