resnet_model.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

class ResNetClassifier:
  def __init__(self):
    self.model = keras.applications.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
  
  def preprocess_image(self, image):
    img = keras.preprocessing.image.array_to_img(image)
    img = img.resize((224, 224))
    img_array = keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    img_array = keras.applications.resnet50.preprocess_input(img_array)
    return img_array

  def classify_image(self, image):
    # Preprocess the image
    img_array = self.preprocess_image(image)

    # Classify the image
    predictions = self.model.predict(img_array)
    predicted_classes = keras.applications.imagenet_utils.decode_predictions(predictions, top=3)[0]

    return predicted_classes