Update app.py

app.py

@@ -1,48 +1,54 @@
-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))
-
-    return fen, str(moves), board_img
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# ♟️ Chess AI from Image")
-
-    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")
-
-    btn = gr.Button("Analyze Board")
-
-    btn.click(
-        fn=predict_from_image,
-        inputs=image,
-        outputs=[fen_out, moves_out, board_display]
-    )
-
-demo.launch()
+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))
+    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 AI from Image")
+
+    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 Best Moves", lines=6)
+        moves_black = gr.Textbox(label="Black's Best 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()