Update app.py

app.py

@@ -10,8 +10,17 @@ faq_embeddings = model.encode(faq_df['question'].tolist(), convert_to_tensor=Tru
 def chatbot(query):
     query_embedding = model.encode(query, convert_to_tensor=True)
     scores = util.pytorch_cos_sim(query_embedding, faq_embeddings)[0]
-    best_idx = int(scores.argmax())  # Fix: convert tensor to int
-    return faq_df.iloc[best_idx]['answer']
+    best_score = float(scores.max())  # Get max similarity score
+    best_idx = int(scores.argmax())
+
+    if best_score < 0.6:  # Threshold can be adjusted
+        return (
+            "🤖 I'm not confident I have the right answer for that.\n"
+            "Please ask a question related to LIC policies, claims, onboarding, or commissions."
+        )
+    else:
+        return faq_df.iloc[best_idx]['answer']
+
 
 with gr.Blocks(title="LIC Agent Assistant") as demo:
     gr.Markdown(