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app.py

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+import chess
+import chess.svg
+import gradio as gr
+import tempfile
+import tensorflow as tf
+import numpy as np
+
+# Initializing the board
+board = chess.Board()
+model = tf.keras.models.load_model('test.h5')
+
+
+def display_board(board_):
+    board_svg = chess.svg.board(board=board_)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".svg") as f:
+        f.write(board_svg.encode('utf-8'))
+        return f.name
+
+
+def one_hot_encode_pieces(piece):
+    pieces = list('rnbqkpRNBQKP.')
+    arr = np.zeros(len(pieces))
+    piece_to_index = {p: i for i, p in enumerate(pieces)}
+    index = piece_to_index[piece]
+    arr[index] = 1
+    return arr
+
+
+def encode_board(board__):
+    board_str = str(board__)
+    board_str = board_str.replace(" ", "")
+    board_list = []
+    for row in board_str.split("\n"):
+        row_list = []
+        for i in row:
+            row_list.append(one_hot_encode_pieces(i))
+        board_list.append(row_list)
+    return np.array(board_list)
+
+
+def play_nn(curr_fen, model):
+    board__ = chess.Board(curr_fen)
+    moves = []
+    input_vectors = []
+    for move in board__.legal_moves:
+        curr_board = board.copy()
+        moves.append(move)
+        curr_board.push(move)
+        input_vectors.append(encode_board(curr_board))
+
+    input_vectors = np.stack(input_vectors)
+    scores = model.predict(input_vectors, verbose=0)
+    if board.turn == chess.BLACK:
+        return moves[np.argmax(scores)]
+    else:
+        return moves[np.argmax(~scores)]
+
+
+def make_move(move):
+    global board
+    if not board.outcome():
+        if len(move) > 3:
+            turn = "White"
+            new_turn = "Black"
+            if chess.Move.from_uci(move) in board.legal_moves:
+                board.push(chess.Move.from_uci(move))
+                ai_move = str(play_nn(board.fen(), model))
+                board.push(chess.Move.from_uci(ai_move))
+                message = f"{turn} move: {move},move accepted\nAI moved: {ai_move}\n\n{turn}'s move."
+            else:
+                message = f"{turn} move: Illegal move. Please try again."
+            return message, display_board(board)
+    else:
+        winner="White" if board.outcome()==chess.WHITE else "Black"
+        return f"{winner} won",display_board(board)
+
+def reset_board():
+    global board
+    board = chess.Board()
+    return "Board reset.", display_board(board)
+
+
+# Setting up gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Chess board game")
+    with gr.Row():
+        with gr.Column():
+            move_input = gr.Textbox(placeholder="Enter your move in UCI format", lines=1)
+            move_button = gr.Button("Make move")
+            reset_button = gr.Button("Reset Board")
+            status_output = gr.Textbox(label='Status', interactive=False)
+        with gr.Column():
+            board_output = gr.Image(type='filepath', label='Chess Board',value=display_board(board))
+
+
+    move_button.click(make_move, inputs=move_input, outputs=[status_output, board_output])
+    reset_button.click(reset_board, inputs=None, outputs=[status_output, board_output])
+demo.launch()

requirements.txt

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+chess==1.10.0
+numpy==1.23.5
+tensorflow==2.15.0

test.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ccb7e8338114c82bad7d1031c4e1458e4214bb9fee2ff83d74a03b8aa3fa345d
+size 5152472