import tensorflow as tf
from tensorflow.keras import layers, Model

def create_unet(input_shape=(256, 256, 1)):
    """Creates a U-Net model for pneumothorax segmentation."""
    inputs = layers.Input(input_shape)

    # Encoder
    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = layers.Dropout(0.5)(conv4)
    pool4 = layers.MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = layers.Dropout(0.5)(conv5)

    # Decoder
    up6 = layers.Conv2DTranspose(512, 2, strides=(2, 2), padding='same')(drop5)
    merge6 = layers.concatenate([drop4, up6], axis=3)
    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv6)

    up7 = layers.Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv6)
    merge7 = layers.concatenate([conv3, up7], axis=3)
    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv7)

    up8 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv7)
    merge8 = layers.concatenate([conv2, up8], axis=3)
    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv8)

    up9 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv8)
    merge9 = layers.concatenate([conv1, up9], axis=3)
    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv9)
    conv9 = layers.Conv2D(2, 3, activation='relu', padding='same')(conv9)
    conv10 = layers.Conv2D(1, 1, activation='sigmoid')(conv9)

    return Model(inputs, conv10)