app.py

import gradio as gr
import chess
import chess.svg
import io
import json
import numpy as np
import cv2
import os
import tempfile
from pathlib import Path

from preprocess import preprocess_image
from train import create_model

# On charge l'ordre des classes depuis le fichier généré par train.
try:
    with open('./class_indices.json', 'r') as f:
        class_indices = json.load(f)
    # Inverser pour avoir index -> nom
    PIECES = [None] * len(class_indices)
    for name, idx in class_indices.items():
        PIECES[idx] = name
    print(f"Ordre des classes chargé: {PIECES}")
except FileNotFoundError:
    # Si jamais le fichier n'est pas load correctement ou erreur
    PIECES = ['Bishop_Black', 'Bishop_White', 'Empty', 'King_Black', 'King_White', 'Knight_Black',
              'Knight_White', 'Pawn_Black', 'Pawn_White', 'Queen_Black', 'Queen_White', 'Rook_Black', 'Rook_White']
    print(f"Fichier class_indices.json non trouvé, utilisation ordre par défaut")

LABELS = {
    'Empty': '.',
    'Rook_White': 'R',
    'Rook_Black': 'r',
    'Knight_White': 'N',
    'Knight_Black': 'n',
    'Bishop_White': 'B',
    'Bishop_Black': 'b',
    'Queen_White': 'Q',
    'Queen_Black': 'q',
    'King_White': 'K',
    'King_Black': 'k',
    'Pawn_White': 'P',
    'Pawn_Black': 'p',
}

# On charge notre modele
print("Loading model...")
model = create_model()
model.load_weights('./model_weights.weights.h5')
print("Model loaded!")


def classify_image(img):
    # On donne une image d'une pièce unique, on la classifie en une seule classe definie (Son nom est PIECE)
    # Ici on normalise notre image comme dans notre entrainement (ici on fait un rescale=1/255)
    if img.max() > 1.0:
        img = img.astype(np.float32) / 255.0
    else:
        img = img.astype(np.float32)
    
    y_prob = model.predict(img.reshape(1, 300, 150, 3), verbose=0)
    y_pred = y_prob.argmax()
    return PIECES[y_pred]


def analyze_board(img):
    arr = []
    M = img.shape[0]//8
    N = img.shape[1]//8
    for y in range(M-1, img.shape[1], M):
        row = []
        for x in range(0, img.shape[1], N):
            sub_img = img[max(0, y-2*M):y, x:x+N]
            if y-2*M < 0:
                sub_img = np.concatenate(
                    (np.zeros((2*M-y, N, 3)), sub_img))
                sub_img = sub_img.astype(np.uint8)

            piece = classify_image(sub_img)
            row.append(LABELS[piece])
        arr.append(row)

    # Ajustement King-Queen detection
    blackKing = False
    whiteKing = False
    whitePos = (-1, -1)
    blackPos = (-1, -1)
    for i in range(8):
        for j in range(8):
            if arr[i][j] == 'K':
                whiteKing = True
            if arr[i][j] == 'k':
                blackKing = True
            if arr[i][j] == 'Q':
                whitePos = (i, j)
            if arr[i][j] == 'q':
                blackPos = (i, j)
    if not whiteKing and whitePos[0] >= 0:
        arr[whitePos[0]][whitePos[1]] = 'K'
    if not blackKing and blackPos[0] >= 0:
        arr[blackPos[0]][blackPos[1]] = 'k'

    return arr


def board_to_fen(board):
    with io.StringIO() as s:
        for row in board:
            empty = 0
            for cell in row:
                if cell != '.':
                    if empty > 0:
                        s.write(str(empty))
                        empty = 0
                    s.write(cell)
                else:
                    empty += 1
            if empty > 0:
                s.write(str(empty))
            s.write('/')
        s.seek(s.tell() - 1)
        s.write(' w KQkq - 0 1')
        return s.getvalue()


def analyze_chess_image(image_input):
    # Logique gradio pour notre main.
    if image_input is None:
        return "❌ No image provided", None
    
    try:
        # On sauvegarde temporairement 
        with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp:
            if isinstance(image_input, np.ndarray):
                cv2.imwrite(tmp.name, cv2.cvtColor(image_input, cv2.COLOR_RGB2BGR))
            else:
                image_input.save(tmp.name)
            temp_path = tmp.name
        
        # preprocess_image() utilise le modele LAPS
        img = preprocess_image(temp_path, save=False)
        
        # EXACT SAME as main.py
        arr = analyze_board(img)
        fen = board_to_fen(arr)
        
        # On génère l'echiquier
        board = chess.Board(fen)
        board_svg = chess.svg.board(board=board, size=400)
        
        # on clean le fichier temporairement sauvegarder
        os.unlink(temp_path)
        
        return f"{fen}", board_svg
    
    except Exception as e:
        import traceback
        print(traceback.format_exc())
        return f"Error: {str(e)}", None


# Build Gradio interface
with gr.Blocks(title="Chess Board picture -> FEN notation", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # ♟️ YOCO: You Only Look Once
    
    Upload a chess board image to automatically detect all pieces and get the FEN notation.
    """)
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(label="Upload chess board image", type="pil")
            submit_btn = gr.Button("Analyze Board", size="lg", variant="primary")
        
        with gr.Column():
            status_output = gr.Textbox(label="Result", interactive=False, lines=2)
            board_output = gr.HTML(label="Board Visualization")
    
    submit_btn.click(
        fn=analyze_chess_image,
        inputs=image_input,
        outputs=[status_output, board_output]
    )

if __name__ == "__main__":
    demo.launch()