Why does my TensorFlow model loose its accuracy after loading

So I am training on the MNIST dataset and the code is down below. The issue is that, on the first run, it calculates everything and gives me a fair accuracy. But on the second run (when it's supposed to load from the saved file) the accuracy drops considerably. Is it something wrong with my code or any practices I'm not following here ?

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
from os import environ, sep
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
MODELFILENAME = 'TF_ZTH_02_model' + sep
labels = {
    0:'T-shirt/Top',
    1:'Trouser',
    2:'Pullover',
    3:'Dress',
    4:'Coat',
    5:'Sandal',
    6:'Shirt',
    7:'Sneaker',
    8:'Bag',
    9:'Ankle Boot'
}

def main():
    fashionmnist = keras.datasets.fashion_mnist
    (trainimages, trainlabels), (testimages, testlabels) = fashionmnist.load_data()
    trainimages, testimages = trainimages/255., testimages/255.
    
    try:
        #try load model
        model = keras.models.load_model(MODELFILENAME)
    
    #files doesn't exist, train model
    except:
        
        #activation functions
        #relu - rectified linear unit - return value if its greater than 0 or 0
        #softmax - picks biggest number in set
        model = keras.Sequential([
            keras.layers.Flatten(input_shape=(28, 28)), #size of image 
            keras.layers.Dense(128, activation=tf.nn.relu), 
            keras.layers.Dense(10, activation=tf.nn.softmax) #ten clothing
        ])

        model.compile(
            optimizer = 'adam',
            loss = 'sparse_categorical_crossentropy',
            metrics = 'accuracy'
        )

        model.fit(trainimages, trainlabels, epochs=5)
        
        #save to file
        model.save(MODELFILENAME)
    
    testloss, testacc = model.evaluate(testimages, testlabels)
    print('\nEvaluation, loss and accuracy : ', testloss, testacc)
    predictions = model.predict(testimages)
#    predictions = model.predict(np.asarray([testimages[0]]))
    
    while True:
        x = int(input('\nEnter image number (<%d) : '%len(testimages)))
        print('\nPredictions : ',
              predictions[x],
              predictions[x].argmax(),
              labels[predictions[x].argmax()]
             )
        print('Actual : ', testlabels[x], labels[testlabels[x]])
        plt.ioff()
        plt.imshow(testimages[x])
        plt.title(labels[predictions[x].argmax()])
        plt.show()
    
    #but this ds has objects centered
    #in the case of an unprocessed ds, you'd need to SPOT FEATURES
    #with the help of convolutional networks

try:
    main()
except Exception as e:
    print(e)
finally:
    input()

Output on First Run

Output on Second Run