Update app.py

app.py

@@ -33,22 +33,22 @@ model_file = download_model()
 model = tf.keras.models.load_model(model_file)
 
 # Perform image classification for single class output
-def predict_class(image):
-    img = tf.cast(image, tf.float32)
-    img = tf.image.resize(img, [input_shape[0], input_shape[1]])
-    img = tf.expand_dims(img, axis=0)
-    prediction = model.predict(img)
-    class_index = tf.argmax(prediction[0]).numpy()
-    predicted_class = labels[class_index]
-    return predicted_class
-
-# Perform image classification for multy class output
 # def predict_class(image):
 #     img = tf.cast(image, tf.float32)
 #     img = tf.image.resize(img, [input_shape[0], input_shape[1]])
 #     img = tf.expand_dims(img, axis=0)
 #     prediction = model.predict(img)
-#     return prediction[0]
+#     class_index = tf.argmax(prediction[0]).numpy()
+#     predicted_class = labels[class_index]
+#     return predicted_class
+
+# Perform image classification for multy class output
+def predict_class(image):
+    img = tf.cast(image, tf.float32)
+    img = tf.image.resize(img, [input_shape[0], input_shape[1]])
+    img = tf.expand_dims(img, axis=0)
+    prediction = model.predict(img)
+    return prediction[0]
 
 # UI Design for single class output
 # def classify_image(image):