saving to dataset

app.py

@@ -205,7 +205,7 @@ def flag(input_image,correct_result):
 def main():
     TITLE = "# MNIST Adversarial: Try to fool this MNIST model"
     description = """This project is about dynamic adversarial data collection (DADC). 
-    The basic idea is to do data collection by collecting “adversarial data”, the kind of data that is difficult for a model to predict correctly. 
+    The basic idea is to collect “adversarial data” - the kind of data that is difficult for a model to predict correctly. 
     This kind of data is presumably the most valuable for a model, so this can be helpful in low-resource settings where data is hard to collect and label.
 
     ### What to do:
@@ -216,9 +216,9 @@ def main():
     """
 
     MODEL_IS_WRONG = """
-    > Did the model get it wrong? Choose the correct prediction below and flag it. 
-    
-    When you flag it, the instance is saved to our dataset and the model is trained on it.
+    ---
+
+    > Did the model get it wrong? Choose the correct prediction below and flag it. When you flag it, the instance is saved to our dataset and the model is trained on it.
     """
     #block = gr.Blocks(css=BLOCK_CSS)
     block = gr.Blocks()