keras.model.predict raise ValueError: Error when checking input - tensorflow

Question

I trained a basic Neural Network model on the MNIST dataset. Here's the code to the training: (imports omitted)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data(path='mnist.npz')
x_train, x_test = x_train/255.0, x_test/255.0
#1st Define the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape = (28,28)), #input layer
tf.keras.layers.Dense(512, activation=tf.nn.relu), #main computation layer
tf.keras.layers.Dropout(0.2), #Dropout layer to avoid overfitting
tf.keras.layers.Dense(10, activation=tf.nn.softmax) #output layer / Softmax is a classifier AF
])
#2nd Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
#3rd Fit the model
model.fit(x_train, y_train, epochs=5)
#4th Save the model
model.save('models/mnistCNN.h5')
#5th Evaluate the model
model.evaluate(x_test, y_test)
I wanted to see how this model works with my own inputs, so I wrote a prediction script with help from this post. My prediction code is: (imports omitted)
model = load_model('models/mnistCNN.h5')
for i in range(3):
img = Image.open(str(i+1) + '.png').convert("L")
img = img.resize((28,28))
im2arr = np.array(img)
im2arr = im2arr/255
im2arr = im2arr.reshape(1, 28, 28, 1)
y_pred = model.predict(im2arr)
print('For Image',i+1,'Prediction = ',y_pred)
First, I don't understand the purpose of this line:
im2arr = im2arr.reshape(1, 28, 28, 1)
If some one could shed light on why this line necessary, that would be of great help.
Second, this very line throws the following error:
ValueError: Error when checking input: expected flatten_input to have 3 dimensions, but got array with shape (1, 28, 28, 1)
What am I missing here?

Answer 1

First dimension is used for batch size. It is added by keras.model internally. So this line just adds it to image array.
im2arr = im2arr.reshape(1, 28, 28, 1)
The error you get is because a single example from mnist dataset, that you used for training has shape (28, 28), so as your input layer. To get rid of this error you need to change this line to
im2arr = img.reshape((1, 28, 28))