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import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import os
class PredictionPipeline:
def __init__(self, filename):
self.filename = filename
def predict(self):
model = load_model(os.path.join("model", "model.h5"))
imagename = self.filename
test_image = image.load_img(imagename, target_size=(150, 150))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = np.argmax(model.predict(test_image), axis=1)
if result[0] == 0:
prediction = "Cyst"
return [{"image": prediction}]
elif result[0] == 1:
prediction = "Normal"
return [{"image": prediction}]
elif result[0] == 2:
prediction = "Stone"
return [{"image": prediction}]
else:
prediction = "Tumor"
return [{"image": prediction}]
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