Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import pandas as pd
 from sentence_transformers import SentenceTransformer, util
 
-# Load FAQ data
+# Load FAQ
 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)
 
-# Policy suggestion mapping
+# Policy tips
 policy_suggestions = {
     "term": "💡 You might consider LIC Tech Term Plan for pure protection at low cost.",
     "money back": "💡 LIC Money Back Policy is great for periodic returns along with insurance.",
@@ -16,15 +16,15 @@ policy_suggestions = {
     "pension": "💡 LIC Jeevan Akshay and PM Vaya Vandana Yojana are best for pension seekers."
 }
 
-# Handle chat
+# Chat handler
 def chatbot(history, query):
     query_lower = query.lower().strip()
 
-    # Handle greetings
+    # Greetings
     if query_lower in ["hi", "hello", "hey", "good morning", "good evening"]:
-        response = "👋 Hello! I’m your LIC Assistant. Ask me anything about LIC policies, claims, onboarding, or commission."
+        response = "👋 Hello! I’m your LIC Assistant. Ask me anything about policies, claims, onboarding, or commission."
     else:
-        # Embedding + similarity
+        # Semantic match
         query_embedding = model.encode(query, convert_to_tensor=True)
         scores = util.pytorch_cos_sim(query_embedding, faq_embeddings)[0]
         best_score = float(scores.max())
@@ -35,7 +35,7 @@ def chatbot(history, query):
         else:
             response = faq_df.iloc[best_idx]['answer']
 
-        # Policy suggestion
+        # Add policy suggestion
         for keyword, suggestion in policy_suggestions.items():
             if keyword in query_lower:
                 response += f"\n\n{suggestion}"
@@ -44,22 +44,25 @@ def chatbot(history, query):
     history.append((query, response))
     return history, history
 
-# Gradio UI
+# UI
 with gr.Blocks(title="LIC Agent Chatbot") as demo:
     gr.Markdown(
         "<h1 style='text-align:center;color:#0D47A1;'>🧑‍💼 LIC Agent Assistant</h1>"
         "<p style='text-align:center;'>Ask me anything about policies, claims, commissions, onboarding, and KYC.</p>"
     )
 
-    chatbot_ui = gr.Chatbot(label="LIC Assistant", height=450)
+    chatbot_ui = gr.Chatbot(label="LIC Assistant", height=450, bubble_full_width=False, avatar_images=("🧑", "🤖"))
     with gr.Row():
-        msg = gr.Textbox(placeholder="E.g., What is the commission for ULIP?", label="Your Question", scale=8)
+        msg = gr.Textbox(placeholder="Ask your question here...", label=None, scale=8, show_label=False)
         send = gr.Button("Send", variant="primary", scale=2)
-    clear = gr.Button("Clear Chat")
+    with gr.Row():
+        mic = gr.Audio(source="microphone", type="filepath", label="🎤 Voice Input (optional)")
+        clear = gr.Button("Clear Chat")
 
     state = gr.State([])
 
     send.click(fn=chatbot, inputs=[state, msg], outputs=[chatbot_ui, state])
+    mic.change(lambda x: ("Voice input received (text not yet processed)", x), inputs=mic, outputs=[msg, mic])
     clear.click(lambda: ([], []), None, [chatbot_ui, state], queue=False)
 
 demo.launch()