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from PIL import Image
from tensorflow import cast
import tensorflow
from tensorflow.image import resize
import numpy as np
from tensorflow.keras.models import load_model
import os
def preprocess_img(image):
image = cast(image, tensorflow.float32)
image = image / 255.
image = resize(image, [200, 200])
return image
def predict_defect(image):
reference = {0:"Crazing", 1:"Inclusion", 2:"Patches", 3:"Pitted", 4:"Rolled", 5:"Scratches"}
image = preprocess_img(image)
subdir='model_files'
path = os.path.join(subdir,"Metal_surface_defect_detector.h5")
model=load_model(path)
result = np.argmax(model.predict(np.array([image])))
# return {reference[i]: float(result[i]) for i in range(6)}
return reference[result]
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