text: import os
  
  import numpy as np
  import pandas as pd
  from PIL import Image
  from annoy import AnnoyIndex
  from torchvision import transforms
  from sklearn.neighbors import BallTree
  from torch.utils.data import Dataset, DataLoader
  
  
  EXAMPLE_DIR = "/home/ubuntu/Final-Project-Group8/Recommendations/example_images/jeans"
  EXAMPLE_TYPE = "jeans"
  STORE_DIR = "/home/ubuntu/Final-Project-Group8/Recommendations/banana_republic_images"
  
  
  def generate_image_mapping_csv(image_dir, csv_name):
      """Returns a csv file mapping image_id and image_label for a given image directory."""
      image_id = [i for i in range(len(os.listdir(image_dir)))]
      image_label = os.listdir(image_dir)
      image_dict = {"image_id": image_id, "image_label": image_label}
      image_df = pd.DataFrame(image_dict)
      image_df.to_csv("/home/ubuntu/Final-Project-Group8/Recommendations/{}.csv".format(csv_name))
  
  
  # Generate image mapping csv files
  generate_image_mapping_csv(EXAMPLE_DIR, "{}_example_image".format(EXAMPLE_TYPE))
  EXAMPLE_CSV_PATH = "/home/ubuntu/Final-Project-Group8/Recommendations/{}_example_image.csv".format(EXAMPLE_TYPE)
  generate_image_mapping_csv(STORE_DIR, "banana_republic_images")
  STORE_CSV_PATH = "/home/ubuntu/Final-Project-Group8/Recommendations/banana_republic_images.csv"
  
  
  class FashionDataset(Dataset):
      """A dataset for the fashion images and fashion image labels.
  
      Arguments:
          Image directory path
          Image transformation
          Information csv path
      """
      def __init__(self, img_dir_path, img_transform, info_csv_path):
          self.img_dir_path = img_dir_path
          self.img_transform = img_transform
          self.info_csv_path = info_csv_path
          self.df = pd.read_csv(self.info_csv_path, header=0).reset_index(drop=True)
          self.img_id = self.df['image_id']
          self.img_label = self.df['image_label']
  
      def __getitem__(self, index):
          img = Image.open(os.path.join(self.img_dir_path, str(self.img_label[index])))
          img = img.convert('RGB')
          if self.img_transform is not None:
              img = self.img_transform(img)
          img = np.asarray(img)
          return index, img
  
      def __len__(self):
          return self.img_id.shape[0]
  
  
  def create_data_loader(img_dir, info_csv_path, batch_size):
       """Returns an image loader for the model."""
       img_transform = transforms.Compose([transforms.Resize((50, 50), interpolation=Image.BICUBIC)])
       img_dataset = FashionDataset(img_dir, img_transform, info_csv_path)
       data_loader = DataLoader(img_dataset, batch_size=batch_size, shuffle=False, num_workers=12, pin_memory=True)
       return data_loader
  
  
  # Create data loader
  example_loader = create_data_loader(EXAMPLE_DIR, EXAMPLE_CSV_PATH, batch_size=1)
  store_loader = create_data_loader(STORE_DIR, STORE_CSV_PATH, batch_size=500)
  store_df = pd.read_csv(STORE_CSV_PATH)
  
  
  np.random.seed(42)
  MODEL_NAME = "baseline"
  
  
  # Get example flattened images
  for batch_idx, (img_idx, img_features) in enumerate(example_loader):
      example_image = img_features.reshape((img_features.shape[0], -1))
  
  
  # Get store flattened images
  for batch_idx, (img_idx, img_features) in enumerate(store_loader):
      store_images = img_features.reshape((img_features.shape[0], -1))
  
  
  # Scikit-learn KNN
  with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "w") as file:
      file.write("Model: {}, Example Type: {}, Scikit-learn KNN \n".format(MODEL_NAME, EXAMPLE_TYPE))
  tree = BallTree(store_images)
  dist, ind = tree.query(example_image, k=5)
  print(dist) # Distances to 5 closest neighbors
  with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "a") as file:
      file.write("Distance to 5 closest neighbors: {} \n".format(dist))
  for i in ind:
      for idx in i:
          print(store_df['image_label'][idx])
          with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "a") as file:
              file.write("Recommendation: {} \n".format(store_df['image_label'][idx]))
  
  
  # Annoy KNN
  with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "a") as file:
      file.write("Model: {}, Example Type: {}, Annoy KNN \n".format(MODEL_NAME, EXAMPLE_TYPE))
  # Index store
  store_item = AnnoyIndex(store_images.size()[1], 'angular')
  for i in range(store_images.size()[0]):
      store_item.add_item(i, store_images[i])
  store_item.build(500) # More trees gives higher precision when querying
  store_item.save('store_items.ann')
  # Index example
  example_item = AnnoyIndex(example_image.size()[1], 'angular')
  example_item.load('store_items.ann')
  recommendations = example_item.get_nns_by_item(0, 5)
  dist_recommendations = example_item.get_nns_by_item(0, 5, include_distances=True)
  print(dist_recommendations)
  with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "a") as file:
      file.write("Distance to 5 closest neighbors: {} \n".format(dist_recommendations))
  for recommendation in recommendations:
      print(store_df['image_label'][recommendation])
      with open("{}_{}_recommendations.txt".format(MODEL_NAME, EXAMPLE_TYPE), "a") as file:
          file.write("Recommendation: {} \n".format(store_df['image_label'][recommendation]))
  