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from PIL import Image
import numpy as np
from tensorflow import keras
import os
def check(image):
# Load the image
# Preprocess the image
image = image.resize((300, 300)) # Resize the image to the desired dimensions
image = np.array(image) # Convert the image to a numpy array
image = image.astype('float32') / 255.0 # Normalize pixel values between 0 and 1
# Expand dimensions and create a batch
image = np.expand_dims(image, axis=0)
model = keras.models.load_model('.\\image_classify.keras')
# Make predictions
predictions = model.predict(image)
return predictions
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