predictor.py

from keras.preprocessing import image
from keras.models import load_model
import os
import numpy as np

categories = ['Apple Braeburn', 'Apple Granny Smith', 'Apricot', 'Avocado', 'Banana', 'Blueberry', 'Cactus fruit', 'Cantaloupe', 'Cherry', 'Clementine', 'Corn', 'Cucumber Ripe', 'Grape Blue', 'Kiwi', 'Lemon', 'Limes', 'Mango', 'Onion White', 'Orange', 'Papaya', 'Passion Fruit', 'Peach', 'Pear', 'Pepper Green', 'Pepper Red', 'Pineapple', 'Plum', 'Pomegranate', 'Potato Red', 'Raspberry', 'Strawberry', 'Tomato', 'Watermelon']
model = load_model('fruitModel.h5')
img_width, img_height = 150,150
folder_path='../input/fruit-recognition/test/test'

images=[]
for img in os.listdir(folder_path):
    img = os.path.join(folder_path, img)
    img = image.load_img(img, target_size=(img_width, img_height))  
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    images.append(img)

# stack up images list to pass for prediction
images = np.vstack(images)
classes = np.argmax(model.predict(images), axis=-1)
prediction = []

prediction = [categories[i] for i in classes]
print(*prediction)