CNN trained on MNIST data set always show same output for all inputs - tensorflow

Question

I am using a Keras CNN for handwritten digit recognition. I downloaded dataset from Kaggle.The preprocessing is :
train = pd.read_csv("./input/train.csv")
test = pd.read_csv("./input/test.csv")
Y_train = train["label"]
X_train = train.drop(labels = ["label"],axis = 1)
X_train = X_train / 255.0
test = test / 255.0
X_train = X_train.values.reshape(-1,28,28,1)
test = test.values.reshape(-1,28,28,1)
Y_train = to_categorical(Y_train, num_classes = 10)
random_seed = 2
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)
My model looks like:
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu', input_shape = (28,28,1)))
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
epochs = 1
batch_size = 86
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180)
zoom_range = 0.1, # Randomly zoom image
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=False, # randomly flip images
vertical_flip=False) # randomly flip images
datagen.fit(X_train)
history = model.fit(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs,
validation_data = (X_val,Y_val),
verbose = 2,
steps_per_epoch=X_train.shape[0] // batch_size,
callbacks=[learning_rate_reduction])
Predicting my input:
def predict(image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.resize(image, (28, 28))
image = image.astype('float32')
image = image.reshape(1, 28, 28, 1)
image /= 255
model = load_model('./model.h5')
pred = model.predict(image, batch_size=1)
print("Predicted Number: ", pred.argmax())
predict(cv2.imread('./testImages/1.png'))
What am I doing wrong?
Desired result is the digit that is provided as an input image, instead I get the same output (i.e. digit 8) for every input.