app.py

import gradio as gr
from io import StringIO
from model import DecoderTransformer, Tokenizer
from huggingface_hub import hf_hub_download
import torch
import chess
import chess.svg
import chess.pgn


vocab_size=33
n_embed=384
context_size=256
n_layer=6
n_head=6
dropout=0.2

device = 'cpu'

model_id = "philipp-zettl/chessPT"

model_path = hf_hub_download(repo_id=model_id, filename="chessPT.pkl")
tokenizer_path = hf_hub_download(repo_id=model_id, filename="tokenizer.json")

model = DecoderTransformer(vocab_size, n_embed, context_size, n_layer, n_head, dropout)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.to(device)
tokenizer = Tokenizer.from_pretrained(tokenizer_path)

def generate(prompt):
    model_input = torch.tensor(tokenizer.encode(prompt), dtype=torch.long, device=device).view((1, len(prompt)))
    pgn = tokenizer.decode(model.generate(model_input, max_new_tokens=4, context_size=context_size)[0].tolist())
    pgn_str = StringIO(pgn)
    game = chess.pgn.read_game(pgn_str)
    img = chess.svg.board(game.board())
    filename = f'moves-{pgn}.svg'
    with open(filename, 'w') as f:
        f.write(img)
    plot = f'<img src="{filename}">'
    return pgn, plot


with gr.Blocks() as demo:
    gr.Markdown("""
    # ChessPT
    Welcome to ChessPT.

    The **C**hess-**P**re-trained-**T**ransformer.

    The rules are simple: provide a PGN string of your current game, the engine will predict the next token!
    """)
    prompt = gr.Text(label="PGN")
    output = gr.Text(label="Next turn", interactive=False)
    img = gr.Markdown()
    submit = gr.Button("Submit")
    submit.click(generate, [prompt], [output, img])
    
    gr.Examples(
        [
            ["1. e4", ],
            ["1. e4 g6 2."],
        ],
        inputs=[prompt],
        outputs=[output, img],
        fn=generate
    )
demo.launch()