Help!! Unable to predict CNN model after training: How to resolve incompatible layers?

[JacobOfCorns]

Help!! I can't predict my model because it's giving me the error:
ValueError: Input 0 of layer dense_3 is incompatible with the layer: expected axis -1 of input shape to have value 100352 but received input with shape [None, 131072]

I just finished training a CNN using a ResNet50 architecture and a few top layers.
This is the code I used for creating the model...

base_model = ResNet50(weights = None, include_top=False, input_shape=(200, 200, 3))

x = base_model.output
x = Flatten()(x)
x = Dropout(0.2)(x)
x = Dense(32, activation='relu')(x)
x = Dense(16, activation='relu')(x)
predictions = Dense(num_class, activation='softmax')(x)

# The model to be trained
model = Model(inputs=base_model.input, outputs=predictions)

model.compile(loss='binary_crossentropy', 
              optimizer='rmsprop', 
              metrics=['accuracy'])

callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_acc', verbose=1)]
model.summary()

As you can see, I only used a few layers on top of the ResNet50 architecture. I also used an image size input of 200, 200, 3
Went it calls back, this is the summary for the FLATTEN to the 2ND LAST DENSE LAYER.

__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 100352)       0           conv5_block3_out[0][0]           
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 100352)       0           flatten_1[0][0]                  
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 32)           3211296     dropout_1[0][0]                  
__________________________________________________________________________________________________

The dense layer expects an input of shape 100,352 but instead it receives an input of 131,072!! Hence, the value error when running the predict code via...

img_path = 'train/10_right.jpeg'
img = image.load_img(img_path, target_size =(256,256))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

preds = model.predict(x)