Input 0 of layer "dense" is incompatible with the layer: expected axis -1of input shape to have value 784, but received input with shape (None, 2352)

Нейросеть, распознающая элементы гардероба, которые, пришли ей со стороны бэкенда(Django). Но при models.predict(input_arr) выдаёт такую ошибку: Input 0 of layer "dense" is incompatible with the layer: expected axis -1of input shape to have value 784, but received input with shape (None, 2352).

views.py(Получение картинки со стороны юзера):

class AIView(View):

def get(self, request, *args, **kwargs):
    return render(request, 'reviews_ali/video.html', {})

def post(self, request, *args, **kwargs):
    image = request.FILES.get('image')
    print(image)
    new_model_image = ImageAI.objects.create(photo=image)
    new_model_image.slug = f'{new_model_image.id}_{random.randint(1, 99999999)}'
    new_model_image.save()
    nerve = neuroview(new_model_image, image)
    return render(request, 'reviews_ali/artificial_dme.html', {'nerve': nerve})

ai.py

from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from .models import ImageAI
import random

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat',
            'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
category = {0: 'T-shirt', 1: 'Trouser', 2:'Pullover', 3:'Dress', 4:'Coat', 5:'Sandal', 6:'Shirt', 7:'Sneaker', 8:'Bag', 9:'Ankle boot'}


'''Отображение'''
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])

plt.imshow(img, cmap=plt.cm.binary)

predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
 color = 'blue'
else:
 color = 'red'

plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
   100 * np.max(predictions_array),
   class_names[true_label]),
   color=color)

def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)

thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')


def neuroview(query_img, image_name):

 '''Создание модели и слоёв'''
 model = keras.Sequential([
     keras.layers.Flatten(input_shape=(28, 28)),
     keras.layers.Dense(128, activation=tf.nn.relu),
     keras.layers.Dense(10, activation=tf.nn.softmax)
 ])

 '''Компилирование'''
 model.compile(
     optimizer=tf.optimizers.Adam(),  # Оптимизатор
     loss='sparse_categorical_crossentropy',  # Функция потерь
     metrics=['accuracy']  # Метрики
 )

 '''Обучение модели'''
 model.fit(train_images, train_labels, epochs=5)
 test_loss, test_acc = model.evaluate(test_images, test_labels)  # Оценка точности
 data = {
     'test_accuracy': int(test_acc * 100)
 }

 image_path = 'C:\\Users\\cerf\\Desktop\\Python\\Работы и проекты\\ai_website\\media\\' + str(image_name)
 image = tf.keras.preprocessing.image.load_img(image_path)
 input_arr = tf.keras.preprocessing.image.img_to_array(image)
 input_arr = np.array([input_arr])

 input_arr = np.expand_dims(input_arr, axis=-1)
 predictions = model.predict(input_arr)
 data['predictions'] = predictions
 data['predict_value'] = category.get(np.argmax(predictions))
 plot_image(0, predictions, test_labels, input_arr)
 _ = plt.xticks(rotation=45)

 print(data)
 new_model_image = ImageAI.objects.create(photo=plt.savefig('ai_image.png'))
 new_model_image.slug = f'{new_model_image.id}_{random.randint(1, 99999999)}'
 new_model_image.save()
 data['model'] = new_model_image
 return data```

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