input: Write a python function to predict the class of an image using a pre-trained model.
  assert predict_image_class("cat_image.jpg", "model") in ["cat", "dog", "bird"]
  assert predict_image_class("dog_image.jpg", "model") in ["cat", "dog", "bird"]
  assert predict_image_class("bird_image.jpg", "model") in ["cat", "dog", "bird"]
output: def predict_image_class(image_path, model_path):
      from tensorflow.keras.models import load_model
      from tensorflow.keras.preprocessing.image import load_img, img_to_array
      import numpy as np
      
      # Load the pre-trained model
      model = load_model(model_path)
      
      # Load and preprocess the image
      image = load_img(image_path, target_size=(224, 224))  # Assuming the model expects 224x224 images
      image_array = img_to_array(image)
      image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
      image_array = image_array / 255.0  # Normalize the image
  
      # Predict the class
      predictions = model.predict(image_array)
      class_index = np.argmax(predictions, axis=1)[0]
  
      # Map the class index to the class name
      class_names = ["cat", "dog", "bird"]
      predicted_class = class_names[class_index]
  
      return predicted_class