Update app.py

app.py

@@ -9,6 +9,7 @@ from sklearn.cluster import KMeans
 
 autoencoder = load_model("autoencoder_model.keras")
 encoded_images = np.load("X_encoded_compressed.npy")
+print(encoded_images.shape)
 dataset = load_dataset('eybro/images')
 
 num_clusters = 10  # Choose the number of clusters
@@ -57,8 +58,10 @@ def process_image(image):
     return pooled_array  # Shape: (1, n_features)
     
 def inference(image):
+    """"""
+    # input_image = process_image(image)
 
-    input_image = process_image(image)
+    input_image = encoded_images[1010]
     
     nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)