text: import numpy as np 
  import pandas as pd 
  import os
  import cv2
  import efficientnet.tfkeras
  from tensorflow import keras
  import tensorflow as tf
  from config import *
  from utils import *
  import math
  from datetime import datetime
  
  model = keras.models.load_model('./models/model.hdf5')
  
  X_test = pd.read_csv(test_path)['image_id'].apply(lambda id: os.path.join(images_path, id + '.jpg'))
  
  test_dataset = (
      tf.data.Dataset
      .from_tensor_slices(X_test)
      .map(load_dataset)
      .batch(batch_size)
  )
  
  steps = math.ceil(X_test.shape[0]/batch_size)
  probs = model.predict(test_dataset, verbose=1, steps=steps)
  
  sub = pd.read_csv(sub_path)
  sub.loc[:, 'healthy':] = probs
  submission_path = os.path.join('.', 'output', datetime.now().strftime("%Y%m%d_%H%M"))
  if not os.path.exists(submission_path):
      os.makedirs(submission_path)
  sub.to_csv(os.path.join(submission_path, 'submission.csv'), index=False)
  sub.head()
  