# Normalize img_array = img_array / 255.0
# Load a pre-trained model (example: VGG16) model = keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Resize the image img = img.resize((224, 224)) # Assuming a 224x224 input for a model like VGG16
import tensorflow as tf from tensorflow import keras from PIL import Image import numpy as np
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# Normalize img_array = img_array / 255.0
# Load a pre-trained model (example: VGG16) model = keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) A51A0007 jpg
# Resize the image img = img.resize((224, 224)) # Assuming a 224x224 input for a model like VGG16 # Normalize img_array = img_array / 255
import tensorflow as tf from tensorflow import keras from PIL import Image import numpy as np A51A0007 jpg
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