addin DCWIR parameters

app.py

@@ -46,10 +46,9 @@ def init():
     for attacker in ["pwws", "bae", "textfooler", "deepwordbug"]:
         for dataset in [
             "agnews10k",
-            "amazon",
             "sst2",
-            "yahoo",
-            # 'imdb'
+            "MR",
+            'imdb'
         ]:
             if "tad-{}".format(dataset) not in tad_classifiers:
                 tad_classifiers[
@@ -75,7 +74,7 @@ def generate_adversarial_example(dataset, attacker, text=None, label=None):
             text, label = get_agnews_example()
         elif "sst2" in dataset.lower():
             text, label = get_sst2_example()
-        elif "amazon" in dataset.lower():
+        elif "MR" in dataset.lower():
             text, label = get_amazon_example()
         # elif "yahoo" in dataset.lower():
             # text, label = get_yahoo_example()
@@ -200,10 +199,9 @@ if __name__ == "__main__":
         gr.Markdown("<h1 align='center'>Detection and Correction based on Word Importance Ranking (DCWIR) </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.
-    - To our best knowledge, Reactive Perturbation Defocusing is a novel approach in adversarial defense. Rapid significantly (>10% defense accuracy improvement) outperforms the state-of-the-art methods.
-    - The DeepWordBug is an unknown attacker to the adversarial detector and reactive defense module. DeepWordBug has different attacking patterns from other attackers and shows the generalizability and robustness of Rapid.
+    - 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 user must know the resulted output for sake of demonstration.
+    - The adversarial example and corrected adversarial example may be unnatural to read, while it is because the attackers usually generate unnatural perturbations. 
+    - All the proposed attacks are Black Box attack where the attacker has no access to the model parameters.
     """)
         gr.Markdown("<h2 align='center'>Natural Example Input</h2>")
         with gr.Group():
@@ -233,7 +231,19 @@ if __name__ == "__main__":
                 gr.Markdown(
                     "<h3 align='center'>To input an example, please select a dataset which the example belongs to or resembles.</h2>",
                 )
-
+        with gr.Row():
+            wir_percentage = gr.Textbox(
+                placeholder="Enter percentage from WIR...",
+                label="Percentage from WIR",
+            )
+            frequency_threshold = gr.Textbox(
+                placeholder="Enter frequency threshold...",
+                label="Frequency Threshold",
+            )
+            max_candidates = gr.Textbox(
+                placeholder="Enter maximum number of candidates...",
+                label="Maximum Number of Candidates",
+            )
         msg_text = gr.Textbox(
             label="Message",
             placeholder="This is a message box to show any error messages.",