Source code of my JSDayES talk about AI and Chess

What you've just checkout is the final version of the code. If you want to replay, checkout the tag base_for_exercise.

You can replay the talk, advancing commit by commit from base_for_exercise.

All the code has to be implemented in main.js.

The files included in the project are the following:

index.html: where you can see everything happening
main.css: few custom styles
main.js: where you should code everything
js\chess.js: Chess.js library. Game engine
js\chessboard.js: Library to draw the chessboard on screen
js\jquery.js: dependency of chessboard.js
js\tfjs.js: TensorFlow.js
js\labels.js: Labels of all available movements in Chess for DNN model
js\boardstate.js: function that converts FEN string to an array for the DNN model.
models\chess-model-1.json: Tensorflow model that predicts next best move for a chessboard.
models\chess-model-1.weights.json: Tensorflow weights for the previous model.

Some tips for the different steps

Constructor receives two parameters:

fist one is the id of the HTML element where the chessboard is going to be drawn
for the second one use the string "start" or the next config object, and then implement:

let config = {
    draggable: true,
    position: 'start',
    onMouseoutSquare,
    onMouseoverSquare,
    onDragStart,
    onDrop,
    onSnapEnd
}

It is very easy to get a certain square because all the squares had a class like square-b4, being b4 the square coordinates.

These methods can be useful:

moves([options]): returns a list of legal moves from the current position. Options is optional and the attributes are: square to limit to movements of the piece in that square, and verbose, a boolean to indicate if you want FEN notation of movement or an object with {from,to}
move(move): to apply a move. returns null if movement is not valid. Accepts string or object (like the ones returned by moves)
undo(): undoes the last move
history(): returns a list containing the moves of the current game
fen(): returns the FEN string for the current position
game_over(): returns true if the game has ended via checkmate, stalemate, draw, threefold repetition, or insufficient material.

getBoardState(fenStr): given a FEN string, returns the array that should be used with the TFJS model.

tf.loadModel(modelUrl): loads the model and it's weights. Needs only the model url.
model.predict(tensor): make a prediction
tf.tensor4d(arr, [numBoards, 8,8,8]): the model has 888 (board size * 8 features)
tf.argMax(arr): return a tensor with a scalar of the position with the hightest value of the array