How to use the ResNet50 model from Keras Applications trained on ImageNet to make a prediction on an image.

resnet50_predict.py

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet')

img_path = 'Data/Jellyfish.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)

# decode the results into a list of tuples (class, description, probability)
print('Predicted:', decode_predictions(preds, top=3)[0])

I cant import ResNet50

Unresolved reference 'ResNet50'
Unresolved reference 'preprocess_input'
Unresolved reference 'decode_predictions'

thank you for your Help

from keras.applications import ResNet50
from keras.applications.densenet import preprocess_input, decode_predictions
from keras.utils import load_img,img_to_array
import numpy as np

model = ResNet50(weights='imagenet')

img_path = 'Data/Jellyfish.jpg'
img = load_img(img_path, target_size=(224, 224))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)

decode the results into a list of tuples (class, description, probability)

print('Predicted:', decode_predictions(preds, top=3)[0])

this was very helpful! many thanks