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import tensorflow as tf
from tensorflow import keras
from keras.models import load_model
from PIL import Image
class PredictionPipeline():
def __init__(self) -> None:
self.CLASS_NAMES = ['Malaria Infected cell', 'Healthy Cell']
self.IMG_SIZE = 224
def predict(self, input_img):
# Loading ResNet152v2 model
resnet_152v2_model = load_model('model_resnet152v2.h5')
# Image Preprocessing
image = Image.open(input_img)
image = tf.cast(image, dtype=tf.float32)
image = image / 255.0
input_tensor = tf.expand_dims(tf.image.resize(image, [self.IMG_SIZE, self.IMG_SIZE]), axis=0)
# Making Predictions
try:
resnet_152v2_y_probs = resnet_152v2_model.predict(input_tensor)
except ValueError as err:
return [[-1]], err, err, err
else:
return tf.round(resnet_152v2_y_probs), resnet_152v2_y_probs
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