Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 from sentence_transformers import SentenceTransformer, util
 
-# Load FAQs
+# Load FAQ data
 faq_df = pd.read_csv("lic_faq.csv")
 model = SentenceTransformer('all-MiniLM-L6-v2')
 faq_embeddings = model.encode(faq_df['question'].tolist(), convert_to_tensor=True)
@@ -10,10 +10,40 @@ 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 = scores.argmax()
+    best_idx = int(scores.argmax())  # Fix: convert tensor to int
     return faq_df.iloc[best_idx]['answer']
 
-iface = gr.Interface(fn=chatbot, inputs="text", outputs="text",
-                     title="LIC Agent Assistant",
-                     description="Ask about LIC claims, policies, onboarding, etc.")
-iface.launch()
+# Custom theme and layout using Gradio Blocks
+custom_theme = gr.themes.Base(
+    primary_hue="blue",
+    secondary_hue="gray",
+    font=["Inter", "sans-serif"],
+    radius_size=8,
+    spacing_size=4
+)
+
+with gr.Blocks(theme=custom_theme, title="LIC Agent Assistant") as demo:
+    gr.Markdown(
+        """
+        # 🧑‍💼 LIC Agent Assistant Chatbot
+        Welcome! Ask any question related to LIC policies, claims, commissions, onboarding, or KYC.
+        """
+    )
+
+    with gr.Row():
+        user_input = gr.Textbox(
+            lines=2,
+            placeholder="Ask your LIC agent question here...",
+            label="Your Question"
+        )
+    with gr.Row():
+        output = gr.Textbox(
+            label="Answer",
+            placeholder="Bot response will appear here...",
+            lines=5
+        )
+
+    submit_btn = gr.Button("Get Answer", variant="primary")
+    submit_btn.click(fn=chatbot, inputs=user_input, outputs=output)
+
+demo.launch()