adding iput box

app.py

@@ -197,8 +197,8 @@ if __name__ == "__main__":
     demo = gr.Blocks()
 
     with demo:
-        gr.Markdown("<h1 align='center'>Reactive Perturbation Defocusing (Rapid) for Textual Adversarial Defense</h1>")
-        gr.Markdown("<h3 align='center'>Clarifications</h2>")
+        gr.Markdown("<h1 align='center'>Detection and Correction based on Word Importance Ranking</h1>")
+        gr.Markdown("<h2 align='center'>Clarifications</h2>")
         gr.Markdown("""
     - This demo has no mechanism to ensure the adversarial example will be correctly repaired by Rapid. The repair success rate is actually the performance reported in the paper.
     - The adversarial example and repaired adversarial example may be unnatural to read, while it is because the attackers usually generate unnatural perturbations. Rapid does not introduce additional unnatural perturbations.
@@ -209,7 +209,7 @@ if __name__ == "__main__":
         with gr.Group():
             with gr.Row():
                 input_dataset = gr.Radio(
-                    choices=["SST2", "Amazon", "Yahoo", "AGNews10K"],
+                    choices=["SST2", "IMDB", "MR", "AGNews10K"],
                     value="SST2",
                     label="Select a testing dataset and an adversarial attacker to generate an adversarial example.",
                 )
@@ -218,22 +218,22 @@ if __name__ == "__main__":
                     value="TextFooler",
                     label="Choose an Adversarial Attacker for generating an adversarial example to attack the model.",
                 )
-            with gr.Group(visible=False):
+            with gr.Group(visible=True):
 
                 with gr.Row():
                     input_sentence = gr.Textbox(
                         placeholder="Input a natural example...",
                         label="Alternatively, input a natural example and its original label (from above datasets) to generate an adversarial example.",
-                        visible=False
+                        visible=True
                     )
                     input_label = gr.Textbox(
                         placeholder="Original label, （must be a integer, because we use digits to represent labels in training）", 
                         label="Original Label",
-                        visible=False
+                        visible=True
                     )
                 gr.Markdown(
                     "<h3 align='center'>To input an example, please select a dataset which the example belongs to or resembles.</h2>",
-                    visible=False
+                    visible=True
                 )
 
         msg_text = gr.Textbox(