AttributeError: 'list' object has no attribute 'shape' while converting to array

I was trying to predict classes but it is giving me this error.

--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in 30 im=ImageGrab.grab(bbox=(205,45,585,555)) 31 im.save('img.jpg') ---> 32 predictions = new_model.predict([prepare('img.jpg')]) 33 y=CATEGORIES[np.argmax(predictions[0][0])] 34

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 1011
max_queue_size=max_queue_size, 1012 workers=workers, -> 1013 use_multiprocessing=use_multiprocessing) 1014 1015 def reset_metrics(self):

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs) 496 model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose, 497 steps=steps, callbacks=callbacks, max_queue_size=max_queue_size, --> 498 workers=workers, use_multiprocessing=use_multiprocessing, **kwargs) 499 500

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs) 424 max_queue_size=max_queue_size, 425 workers=workers, --> 426 use_multiprocessing=use_multiprocessing) 427 total_samples = _get_total_number_of_samples(adapter) 428 use_sample = total_samples is not None

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing) 644 standardize_function = None 645 x, y, sample_weights = standardize( --> 646 x, y, sample_weight=sample_weights) 647 elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter: 648 standardize_function = standardize

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset) 2344 # First, we build the model on the fly if necessary. 2345 if not self.inputs: -> 2346 all_inputs, y_input, dict_inputs = self._build_model_with_inputs(x, y) 2347 is_build_called = True 2348 else:

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _build_model_with_inputs(self, inputs, targets) 2570 else:
2571 cast_inputs = inputs -> 2572 self._set_inputs(cast_inputs) 2573 return processed_inputs, targets, is_dict_inputs 2574

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _set_inputs(self, inputs, outputs, training) 2645 first layer isn't FeatureLayer. 2646 """ -> 2647 inputs = self._set_input_attrs(inputs) 2648 2649 if outputs is None:

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

~\anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _set_input_attrs(self, inputs) 2684 input_shape = (None,) 2685 else: -> 2686 input_shape = (None,) + tuple(inputs.shape[1:]) 2687 self._build_input_shape = input_shape 2688

AttributeError: 'list' object has no attribute 'shape'

import cv2
import tensorflow as tf
import numpy as np

CATEGORIES = ["gas","back","both"]


def prepare(filepath):
    IMG_SIZE = 256
    img_array = cv2.imread(filepath)
    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
    img = np.reshape(new_array,[1,256,256,3])
    return img


model = tf.keras.models.load_model("trained.model")

prediction = model.predict_classes([prepare('img.jpg')])
print(CATEGORIES[int(prediction[0])])