import tensorflow as tf
from tensorflow import keras

def img_scaler(image, max_dim = 512):

  #Casts tensor to a new data type
  original_shape = tf.cast(tf.shape(image)[:-1], tf.float32)

  #Creates scale constant for the image based on imput max_dim
  scale_ratio = max_dim / max(original_shape)

  #Casts tensor to a new data type
  new_shape = tf.cast(original_shape * scale_ratio, tf.int32)

  #Resizes image
  return tf.image.resize(image, new_shape)

def load_img(image_path, content=True, max_dim = 512):

  if content:
    img = image_path
    print("image_path:")
    print(image_path)
    #Read contents of the input filename
    ####img = tf.keras.preprocessing.image.array_to_img(image_path)
    print("img")
    print(img)
    #img = tf.io.read_file(image_path)
    #print(img)
  

    #Detect whether an image is a BMP, GIF, JPEG, or PNG, 
    #performs the appropriate operation
    #convert the input bytes string into a Tensor of type dtype 
    ##img = tf.image.decode_image(img, channels=3)

    #Convert image to dtype
    img = tf.image.convert_image_dtype(img, tf.float32)

    #Scale the image using the created scaler function
    img = img_scaler(img, max_dim)

    #Adds a fourth dimension to the Tensor because the model requires a 4-dimensional Tensor
    return img[tf.newaxis, :]
    
  else:
    #Read contents of the input filename
    img = tf.io.read_file(image_path)

    #Detect whether an image is a BMP, GIF, JPEG, or PNG, 
    #performs the appropriate operation
    #convert the input bytes string into a Tensor of type dtype 
    img = tf.image.decode_image(img, channels=3)

    #Convert image to dtype
    img = tf.image.convert_image_dtype(img, tf.float32)

    #Scale the image using the created scaler function
    img = img_scaler(img, max_dim)

    #Adds a fourth dimension to the Tensor because the model requires a 4-dimensional Tensor
    return img[tf.newaxis, :]