Update app.py

app.py

@@ -10,52 +10,26 @@ 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=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,
-        method=method,
-        k=k
-    )
+# ==== Instantiate RAG ====
+rag = RAGPipeline(
+    embedder_model="infly/inf-retriever-v1-1.5b",
+    reranker_model="cross-encoder/ms-marco-MiniLM-L-6-v2",
+    generator_model="google/flan-t5-base"
+)
 
-    return (
-        stats_text,
-        f"{auc}",
-        fig,
-        f"🟠 Perturbed Query:\n\n{pert_q}",
-        f"🟢 Perturbed Response:\n\n{pert_r}",
-        f"🔴 Directly Perturbed Response of Normal Output:\n\n{adv_r}"
-    )
+# ==== Gradio App ====
+def answer_question(query, top_k):
+    return rag.generate_answer(query, top_k)
 
-# Build Interface
 gr.Interface(
-    fn=gradio_wrapper,
+    fn=answer_question,
     inputs=[
-        gr.Textbox(label="Enter a Question"),
-        gr.Dropdown(choices=["synonym", "delete", "contextual"], label="Perturbation Method"),
+        gr.Textbox(label="Enter your question"),
         gr.Slider(1, 5, step=1, value=3, label="Top-K Retrieved Chunks")
     ],
-    outputs=[
-        gr.Textbox(label="📊 Summary Statistics"),
-        gr.Textbox(label="🔺 PSC-AUC Score"),
-        gr.Plot(label="📈 PSC Curve"),
-        gr.Textbox(label="🟠 Perturbed Query Example"),
-        gr.Textbox(label="🟢 Perturbed Response Example"),
-        gr.Textbox(label="🔴 Directly Perturbed Normal Response Example")
-    ],
-    title="Adversarial Testing on RAGiant System",
-    description="Evaluate robustness against textual attacks and visualize degradation with ARI & PSC-AUC."
-).launch()
-
-
-
+    outputs=gr.Textbox(label="📘 Generated Answer"),
+    title="RAG-based Question Answering",
+    description="A lightweight RAG system using FAISS + TF-IDF + FLAN-T5. Ask a question, get an answer."
+).launch()