Update app.py

app.py

@@ -28,16 +28,15 @@ loaded_autoencoder = tf.keras.models.load_model(
 )
 
 def process_image(input_img):
-    # Convert PIL Image to numpy array and normalize
-    img = input_img.convert("RGB")
-    img = img.resize((WIDTH, HEIGHT))  # adjust size as needed
+    # Convert PIL Image to grayscale and resize
+    img = input_img.convert("L")  # Convert to grayscale (single channel)
+    img = img.resize((WIDTH, HEIGHT))  # Adjust size as needed
     img_array = tf.keras.preprocessing.image.img_to_array(img) / 255.0
-    img_array = img_array[None, ...]  # add batch dimension
-    print("img_array shpae: ", img_array.shape)
+    img_array = img_array[None, ..., 0:1]  # Add batch dimension and keep single channel
+    print("img_array shape: ", img_array.shape)
     # Run inference
     output_array = loaded_autoencoder.predict(img_array)
     output_img = tf.keras.preprocessing.image.array_to_img(output_array[0])
-
     return output_img
 
 custom_css = """