Update app.py

app.py

@@ -7,19 +7,11 @@ from keras.models import Model
 from datasets import load_dataset
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
-import pickle
 
 autoencoder = load_model("autoencoder_model.keras")
 encoded_images = np.load("X_encoded_compressed.npy")
 
-
-# dataset with the split index
-with open("dataset_with_split.pkl", "rb") as f:
-    data = pickle.load(f)
-
-dataset = data['dataset']
-split = data['split']
-
+dataset = load_dataset("eybro/images-split")
 
 num_clusters = 10  # Choose the number of clusters
 kmeans = KMeans(n_clusters=num_clusters, random_state=42)
@@ -69,7 +61,7 @@ def inference(image):
     """"""
     # input_image = process_image(image)
 
-    input_image = encoded_images[1010]
+    input_image = encoded_images[2000]
     
     nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)