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import numpy as np
import chess
import tensorflow as tf
from keras.models import load_model
class Model_handler :
def __init__(self) :
self.model = load_model("model.h5")
print("Model loaded and is ready to use !!!")
def predict(self, state, color) :
prediction = self.model(self.convert_state_to_input(state, color))
return self.convert_output_to_probs(state, color, prediction[1][0])
def convert_state_to_input(self, state, color) :
if type(state) == str :
temp = state.split("_")
temp_arr = np.zeros((8,8,12), dtype = np.float16)
arr2 = np.zeros((8,8,10), dtype = np.float16)
temp_arr, arr2 = self.convert_board_to_input(temp[-1], color)
for i in range(1,5) :
temp_arr = np.concatenate([self.convert_board_to_input(temp[-1-i], color, False), temp_arr], axis = 2)
return (np.expand_dims(temp_arr, axis = 0), np.expand_dims(arr2, axis = 0))
def convert_board_to_input(self, state, color, current = True) :
if current :
board = chess.Board(state)
board.turn = color
arr = np.zeros((8,8,12), dtype = np.float16)
arr2 = np.zeros((8,8,10), dtype = np.float16)
piece_to_value = self.get_piece_to_value(color)
piece_to_value2 = self.get_piece_to_value(color, False)
for i in range(64) :
if(board.piece_at(i) is not None) :
arr[i//8,i%8,piece_to_value[board.piece_at(i).symbol()]] = 1
for move in board.legal_moves :
square = move.to_square
arr[square//8, square%8, piece_to_value[board.piece_at(move.from_square).symbol()]] = 0.5
symbol = board.piece_at(move.from_square).symbol()
if move.promotion is not None :
arr2[move.promotion-2, move.from_square%8, 9] = 1
else :
arr2[square//8, square%8, piece_to_value2[symbol]] = 1
if(piece_to_value2[symbol] == 1 or piece_to_value2[symbol] == 3 or piece_to_value2[symbol] == 5) :
piece_to_value2[symbol] += 1
return (arr, arr2)
else :
arr = np.zeros((8,8,12), dtype = np.float16)
if len(state) == 0 :
return arr
board = chess.Board(state)
board.turn = color
piece_to_value = self.get_piece_to_value(color)
for i in range(64) :
if(board.piece_at(i) is not None) :
arr[i//8,i%8,piece_to_value[board.piece_at(i).symbol()]] = 1
return arr
def convert_output_to_probs(self, state, color, policy_output) :
policy = tf.reshape(policy_output, [8,8,10])
board = chess.Board(state.split("_")[-1])
board.turn = color
piece_to_value = self.get_piece_to_value(color,False)
move_dict = {}
for move in list(board.legal_moves) :
to_square = move.to_square
from_square = move.from_square
piece_type = piece_to_value[board.piece_at(from_square).symbol()]
if move.promotion is not None :
move_dict[move.uci()] = policy[move.promotion-2, from_square%8, 9]
else :
move_dict[move.uci()] = policy[to_square//8, to_square%8, piece_type]
if(piece_type == 1 or piece_type == 3 or piece_type == 5) :
piece_to_value[board.piece_at(from_square).symbol()] += 1
# print(list(board.legal_moves))
move = [item[0] for item in sorted(move_dict.items(), key = lambda x: x[1], reverse = True)][0]
print(move_dict[move])
return move
def get_piece_to_value(self, color, inp = True) :
if inp :
if(color == 1) :
return {
'P': 0, 'N': 1, 'B': 2, 'R': 3, 'Q': 4, 'K': 5,
'p': 6, 'n':7, 'b': 8, 'r': 9, 'q': 10, 'k': 11
}
return {
'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5,
'P': 6, 'N':7, 'B': 8, 'R': 9, 'Q': 10, 'K': 11
}
else :
if(color == 1) :
return {
'P': 0, 'N': 1, 'B': 3, 'R': 5, 'Q': 7, 'K': 8
}
return {
'p': 0, 'n': 1, 'b': 3, 'r': 5, 'q': 7, 'k': 8
} |