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import gradio as gr
import chess
import chess.svg
from PIL import Image
import io
import cairosvg
from main import get_moves # your engine wrapper
def predict_from_image(img):
# Save uploaded image
img = img.convert("RGB")
img.save("board.png", format="PNG")
# Your function should return (fen, moves)
fen, moves = get_moves("board.png")
# Convert FEN -> SVG -> PNG for Gradio display
board = chess.Board(fen)
svg_data = chess.svg.board(board=board, size=350)
# convert svg to png (since Gradio Image widget expects raster)
png_bytes = cairosvg.svg2png(bytestring=svg_data)
board_img = Image.open(io.BytesIO(png_bytes))
moves_white = moves['white_moves']
moves_black=moves['black_moves']
white_moves_clean = [m[1] for m in moves_white]
black_moves_clean = [m[1] for m in moves_black]
return fen, "\n".join(white_moves_clean), "\n".join(black_moves_clean), board_img
with gr.Blocks() as demo:
gr.Markdown("""
# chess move predictor using alpha beta pruning
_Upload an image of an online chessboard and get the FEN & best moves._
⚠️ **Warning:** Please upload screenshots from online chess games (e.g., Lichess, Chess.com).
Physical/real-world chessboard photos are not supported yet.
""")
with gr.Row():
image = gr.Image(type="pil", label="Upload Chessboard Image")
board_display = gr.Image(type="pil", label="Detected Board")
fen_out = gr.Textbox(label="Detected FEN")
moves_out = gr.Textbox(label="Predicted Best Moves")
with gr.Row():
moves_white = gr.Textbox(label="White's Moves", lines=6)
moves_black = gr.Textbox(label="Black's Moves", lines=6)
btn = gr.Button("Analyze Board")
btn.click(
fn=predict_from_image,
inputs=image,
outputs=[fen_out, moves_white, moves_black, board_display]
)
demo.launch()
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