app.py

# %%
from transformers import AutoModelForCausalLM, GPT2LMHeadModel
import torch
import chess
import chess.pgn
import chess.svg
import gradio as gr
from modeling.utils import uci_to_board
from modeling.uci_tokenizers import UciTileTokenizer
import numpy as np
import io
import traceback
# %%
checkpoint_name = "austindavis/chess-gpt2-uci-8x8x512"
tokenizer = UciTileTokenizer()
model: GPT2LMHeadModel = AutoModelForCausalLM.from_pretrained(checkpoint_name)
model.requires_grad_(False)

# %%
# Initialize the chess board
board = chess.Board()
game:chess.pgn.GameNode = chess.pgn.Game()



game.headers["Event"] = "Example"

generate_kwargs = {
                "max_new_tokens": 3,
                "num_return_sequences": 10,
                "temperature": 0.5, 
                "output_scores": True,
                "output_logits": True,
                "return_dict_in_generate": True
                }

def make_move(input:str, node=game, board = board):
    # check for reset
    if input.lower() == 'reset':
        board.reset()
        node.root().variations.clear()
        return chess.svg.board(board=board), "New game!"
    
    # check for pgn
    if input[0] == '[' or input[:3] == '1. ':
        pgn = io.StringIO(input)
        game = chess.pgn.read_game(pgn)
        board.reset()
        node.root().variations.clear()

        for move in game.mainline_moves():
            board.push(move)
            node.add_variation(move)

        return chess.svg.board(board=board,lastmove=move), ""#str(node.root()).split(']')[-1].strip()


    try:
        move = chess.Move.from_uci(input)
        if move in board.legal_moves:
            board.push(move)

            while node.next() is not None:
                node = node.next()
            node = node.add_variation(move)

            # get computer's move

            prefix = ' '.join([x.uci() for x in board.move_stack])
            encoding = tokenizer(text=prefix,
                return_tensors='pt', 
                )['input_ids']

            output = model.generate(encoding, **generate_kwargs) # [b,p,v]
            new_tokens = tokenizer.batch_decode(output.sequences[:,-3:])
            unique_moves, unique_indices = np.unique([x[:4] if ' ' in x else x for x in new_tokens], return_index=True)
            unique_indices = torch.Tensor(list(unique_indices)).to(dtype=torch.int)
            logits = torch.stack(output.logits) # [token, batch, vocab]
            logits = logits[:,unique_indices]  # [token, batch, vocab]
            
            # select moves based on mean logit value for tokens 1 and 2
            logit_priority_order = logits.max(dim=-1).values.T[:,:2].mean(-1).topk(len(unique_indices)).indices
            priority_ordered_moves = unique_moves[logit_priority_order]
            
            # if there's only 1 option, we have to pack it back into a list
            if isinstance(priority_ordered_moves, str):
                priority_ordered_moves = [priority_ordered_moves]

            # test if any moves are valid
            for uci in priority_ordered_moves:
                move = chess.Move.from_uci(uci)
                if move in board.legal_moves:
                    board.push(move)
                    while node.next() is not None:
                        node = node.next()
                    node = node.add_variation(move)
                    return chess.svg.board(board=board,lastmove=move), ""#str(node.root()).split(']')[-1].strip()
            
            # no moves are valid
            bad_from_tiles = [chess.parse_square(x) for x in [x[:2] for x in unique_moves]]
            bad_to_tiles = [chess.parse_square(x) for x in [x[2:] for x in unique_moves]]
            arrows = [chess.svg.Arrow(tail, head, color="red") for (tail, head) in zip(bad_from_tiles, bad_to_tiles)]
            return chess.svg.board(board=board,arrows=arrows), '|'.join(unique_moves)
        else:
            return chess.svg.board(board=board,lastmove=move), f"Illegal move:  {input}"
    
    except ValueError:
        return chess.svg.board(board=board), f"Invalid UCI format:  {uci} {list(unique_moves)}"
    except Exception:
        return chess.svg.board(board=board), traceback.format_exc()

# Define the Gradio interface
iface = gr.Interface(
    fn=make_move,
    inputs="text",
    outputs=["html", "text"],
    examples=[['e2e4'], ['d2d4'], ['Reset']],
    title="Play Versus ChessGPT",
    description="Enter moves in UCI notation (e.g., e2e4 for pawn from e2 to e4). Enter 'reset' to restart the game.",
    allow_flagging='never',
)

# Launch the Gradio app
iface.launch()

# %%