app.py

import os
import cv2
import numpy as np
import gradio as gr
import spaces

# ============================================
# 1. بناء/تدريب النموذج لحفظه محلياً
# ============================================
model_path = 'mnist_cnn_model.keras'

def ensure_model_exists():
    if not os.path.exists(model_path):
        print("⚠️ جاري تدريب النموذج وحفظه لأول مرة...")
        import tensorflow as tf
        from tensorflow.keras import datasets, layers, models

        model = models.Sequential([
            layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.MaxPooling2D((2, 2)),
            layers.Flatten(),
            layers.Dense(128, activation='relu'),
            layers.Dropout(0.5),
            layers.Dense(10, activation='softmax')
        ])
        model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
        
        (x_train, y_train), _ = datasets.mnist.load_data()
        x_train = x_train.reshape((60000, 28, 28, 1)).astype('float32') / 255.0
        model.fit(x_train, y_train, epochs=3, validation_split=0.1, verbose=1)
        model.save(model_path)
        print("✅ تم حفظ النموذج بنجاح.")

ensure_model_exists()

# تحميل بيانات الاختبار للأمثلة العشوائية
import tensorflow as tf
(_, _), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
test_images_norm = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255.0

# ============================================
# 2. دالة معالجة الصورة الآمنة
# ============================================
def process_image_for_mnist(image_input):
    # التحقق الآمن من الصفر/الغياب بدون تقييم المصفوفة كـ Boolean
    if image_input is None:
        return None, None

    if isinstance(image_input, dict):
        image = image_input.get('composite', None)
        if image is None:
            image = image_input.get('background', None)
        if image is None and len(image_input) > 0:
            image = list(image_input.values())[0]
    else:
        image = image_input

    if image is None or not isinstance(image, np.ndarray) or image.size == 0:
        return None, None

    if len(image.shape) == 3 and image.shape[-1] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)

    if len(image.shape) == 3:
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        gray = image.copy()

    resized = cv2.resize(gray, (28, 28), interpolation=cv2.INTER_AREA)
    _, binary = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    if np.sum(binary == 255) > np.sum(binary == 0):
        binary = 255 - binary

    normalized = binary.astype('float32') / 255.0
    reshaped = normalized.reshape(1, 28, 28, 1)

    return normalized, reshaped

loaded_model = None

# ============================================
# 3. دالة التوقع ببيئة ZeroGPU المضمونة
# ============================================
@spaces.GPU
def predict_sketch(image):
    global loaded_model
    try:
        # إصلاح سبب الخطأ: استخدام size أو is None بشكل صريح
        if image is None:
            return {}, None, "⚠️ يرجى الرسم في المربع أو اختيار مثال أولاً!"

        normalized, reshaped = process_image_for_mnist(image)

        if normalized is None:
            return {}, None, "⚠️ تعذر معالجة الصورة المدخلة."

        import tensorflow as tf
        
        gpus = tf.config.list_physical_devices('GPU')
        if gpus:
            try:
                for gpu in gpus:
                    tf.config.experimental.set_memory_growth(gpu, True)
            except RuntimeError:
                pass

        if loaded_model is None:
            loaded_model = tf.keras.models.load_model(model_path)

        preds = loaded_model(reshaped, training=False).numpy()[0]
        predicted_class = int(np.argmax(preds))
        confidence = float(np.max(preds))

        probabilities = {str(i): float(preds[i]) for i in range(10)}

        display_img = (normalized * 255).astype(np.uint8)
        display_img = cv2.resize(display_img, (140, 140), interpolation=cv2.INTER_NEAREST)

        top3_idx = np.argsort(preds)[-3:][::-1]
        top3_str = "\n".join([f"   {i+1}. الرقم {idx}: {preds[idx]:.2%}" for i, idx in enumerate(top3_idx)])

        debug_info = f"""📊 نتائج التوقع :
- الرقم المتوقع: {predicted_class}
- نسبة الثقة: {confidence:.2%}

🔝 أعلى 3 احتمالات:
{top3_str}"""

        return probabilities, display_img, debug_info

    except Exception as e:
        return {}, None, f"❌ خطأ أثناء التوقّع: {str(e)}"

# ============================================
# 4. دالة اختيار مثال عشوائي (تمت إعادتها)
# ============================================
def random_example():
    idx = np.random.randint(0, len(test_images))
    img = test_images[idx]
    img_large = cv2.resize(img, (280, 280), interpolation=cv2.INTER_NEAREST)
    img_rgb = np.stack([img_large] * 3, axis=2)
    return img_rgb, f"🎲 تم اختيار رقم عشوائي (الرقم الحقيقي: {test_labels[idx]})"

# ============================================
# 5. بناء واجهة Gradio
# ============================================
with gr.Blocks(title="MNIST") as demo:
    gr.Markdown("# 🧠 التعرف على الأرقام المكتوبة بخط اليد ")
    
    with gr.Row():
        with gr.Column(scale=1):
            sketch = gr.Sketchpad(label="✏️ منطقة الرسم")
            with gr.Row():
                submit_btn = gr.Button("🔮 توقع", variant="primary")
                random_btn = gr.Button("🎲 مثال عشوائي", variant="secondary")
            info = gr.Textbox(label="📌 معلومات التصحيح", interactive=False, lines=8)
            
        with gr.Column(scale=1):
            output = gr.Label(num_top_classes=3, label="📊 احتمالات التوقع")
            processed_img = gr.Image(label="🖼️ الصورة بعد المعالجة", image_mode="L")

    # ربط الأحداث
    submit_btn.click(
        fn=predict_sketch,
        inputs=sketch,
        outputs=[output, processed_img, info]
    )
    
    random_btn.click(
        fn=random_example,
        inputs=[],
        outputs=[sketch, info]
    )

demo.launch()