import tensorflow as tf
import tf_keras
model_mri = tf_keras.models.load_model('model')
model_xray = tf_keras.models.load_model('xray_resnet_model')

def load_image_with_path(path):
    img = tf.io.read_file(path)
    img = tf.image.decode_image(img, channels=3)
    img = tf.image.resize(img, size=[256, 256])
    img = img / 255.
    return img

def makepredictions(path):
    img = load_image_with_path(path)
    predictions = model_mri.predict(tf.expand_dims(img, axis=0))
    a = int(tf.argmax(tf.squeeze(predictions)))
    if a == 0:
        a = "Result : Glioma Tumor"
    elif a == 1:
        a = "Result : Meningioma Tumor"
    elif a == 2:
        a = "Result : No Tumor"
    else:
        a = "Result : Pituitary Tumor"
    return a
    # {'glioma': 0, 'meningioma': 1, 'notumor': 2, 'pituitary': 3}

def xray_predict(path):
    img = load_image_with_path(path)
    predications = model_xray.predict(tf.expand_dims(img, axis=0))
    a = int(tf.argmax(tf.squeeze(predications)))
    xray_classes = ['COVID19','NORMAL', 'PNEUMONIA',' TURBERCULOSIS']
    a = xray_classes[a]

    return a
    # {'COVID19': 0, 'NORMAL': 1, 'PNEUMONIA': 2, 'TURBERCULOSIS': 3}