handle low accuracy results

app.py

@@ -88,8 +88,12 @@ def classify_and_visualize(image):
     predicted_class = tf.argmax(prediction[0], axis=-1)
     confidence = np.max(prediction[0])
 
-    class_name = class_names[predicted_class.numpy()]
-    bounded_image = edge_and_cut(image, 200, 400)
+    if confidence < 0.60:
+        class_name = "The image you uploaded might not be in the dataset. Try making your leaf background white."
+        bounded_image = image
+    else:
+        class_name = class_names[predicted_class.numpy()]
+        bounded_image = edge_and_cut(image, 200, 400)
 
     return class_name, confidence, bounded_image