Update app.py

app.py

@@ -5,9 +5,8 @@ import gradio as gr
 import nltk
 import pandas as pd
 import requests
-from flask import Flask
 
-from anonymous_demo import TADCheckpointManager
+from pyabsa import TADCheckpointManager
 from textattack.attack_recipes import (
     BAEGarg2019,
     PWWSRen2019,
@@ -35,8 +34,6 @@ attack_recipes = {
     "clare": CLARE2020,
 }
 
-app = Flask(__name__)
-
 
 def init():
     nltk.download("omw-1.4")
@@ -50,6 +47,7 @@ def init():
             "agnews10k",
             "amazon",
             "sst2",
+            # "yahoo",
             # 'imdb'
         ]:
             if "tad-{}".format(dataset) not in tad_classifiers:
@@ -78,6 +76,8 @@ def generate_adversarial_example(dataset, attacker, text=None, label=None):
             text, label = get_sst2_example()
         elif "amazon" in dataset.lower():
             text, label = get_amazon_example()
+        elif "yahoo" in dataset.lower():
+            text, label = get_yahoo_example()
         elif "imdb" in dataset.lower():
             text, label = get_imdb_example()
 
@@ -98,13 +98,13 @@ def generate_adversarial_example(dataset, attacker, text=None, label=None):
             # with defense
             result = tad_classifiers["tad-{}".format(dataset.lower())].infer(
                 attack_result.perturbed_result.attacked_text.text
-                + "!ref!{},{},{}".format(
+                + "$LABEL${},{},{}".format(
                     attack_result.original_result.ground_truth_output,
                     1,
                     attack_result.perturbed_result.output,
                 ),
                 print_result=True,
-                defense="pwws",
+                defense=attacker,
             )
 
     if result:
@@ -112,7 +112,7 @@ def generate_adversarial_example(dataset, attacker, text=None, label=None):
         classification_df["is_repaired"] = result["is_fixed"]
         classification_df["pred_label"] = result["label"]
         classification_df["confidence"] = round(result["confidence"], 3)
-        classification_df["is_correct"] = result["ref_label_check"]
+        classification_df["is_correct"] = str(result["pred_label"]) == str(label)
 
         advdetection_df = {}
         if result["is_adv_label"] != "0":
@@ -186,12 +186,17 @@ def check_gpu():
 
 
 if __name__ == "__main__":
-    # init()
+    try:
+        init()
+    except Exception as e:
+        print(e)
+        print("Failed to initialize the demo. Please try again later.")
 
     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("""
     - 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.
@@ -202,13 +207,13 @@ if __name__ == "__main__":
         with gr.Group():
             with gr.Row():
                 input_dataset = gr.Radio(
-                    choices=["SST2", "AGNews10K", "Yahoo", "Amazon"],
+                    choices=["SST2", "Amazon", "Yahoo", "AGNews10K"],
                     value="SST2",
                     label="Select a testing dataset and an adversarial attacker to generate an adversarial example.",
                 )
                 input_attacker = gr.Radio(
                     choices=["BAE", "PWWS", "TextFooler", "DeepWordBug"],
-                    value="PWWS",
+                    value="TextFooler",
                     label="Choose an Adversarial Attacker for generating an adversarial example to attack the model.",
                 )
             with gr.Group():
@@ -258,7 +263,7 @@ if __name__ == "__main__":
                     )
                     output_repaired_label = gr.Textbox(label="Predicted Label of the Repaired Adversarial Example")
 
-        gr.Markdown("<h2 align='center'>Example Comparisons</p>")
+        gr.Markdown("<h2 align='center'>Example Difference (Comparisons)</p>")
         gr.Markdown("""
         <p align='center'>The (+) and (-) in the boxes indicate the added and deleted characters in the adversarial example compared to the original input natural example.</p>
             """)
@@ -320,4 +325,4 @@ if __name__ == "__main__":
             ],
         )
 
-    demo.queue(concurrency_count=10).launch()
+    demo.queue(2).launch()