Update app.py

app.py

@@ -1,15 +1,5 @@
-# -*- coding: utf-8 -*-
-"""FULL WORKFLOW
 
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1I9KysFRYpSZfkL7FIxbodBYUgudQRejA
-"""
-
-
-
-!pip install transformers faiss-cpu gradio sentence-transformers nlpaug scikit-learn
+# !pip install transformers faiss-cpu gradio sentence-transformers nlpaug scikit-learn
 
 # rag_module.py
 
@@ -22,18 +12,10 @@ from transformers import AutoTokenizer, T5ForConditionalGeneration
 import gradio as gr
 
 from rag_pipeline import RAGPipeline
-
 from adversarial_framework import *
 
-# Example Usage:
-rag = RAGPipeline()
-#print(rag.generate_answer("Who is the president of NACOS?"))
-
+adv_pipeline = AdversarialAttackPipeline(answer_generator=RAGPipeline().generate_answer)
 
-
-adv_pipeline = AdversarialAttackPipeline(answer_generator=rag.generate_answer)
-
-# Gradio function
 def gradio_wrapper(query, method, k):
     stats_text, auc, fig, pert_q, pert_r, adv_r = adv_pipeline.evaluate_adversarial_robustness(
         query=query,
@@ -50,7 +32,6 @@ def gradio_wrapper(query, method, k):
         f"🔴 Directly Perturbed Response of Normal Output:\n\n{adv_r}"
     )
 
-# Build Interface
 gr.Interface(
     fn=gradio_wrapper,
     inputs=[
@@ -68,7 +49,4 @@ gr.Interface(
     ],
     title="Adversarial Testing on RAGiant System",
     description="Evaluate robustness against textual attacks and visualize degradation with ARI & PSC-AUC."
-).launch()
-
-
-
+).launch()