Update app.py

app.py

@@ -7,46 +7,50 @@ 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)
 
-def chatbot(query):
+# Sample policies to suggest (add more as needed)
+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.",
+    "endowment": "💡 LIC New Endowment Plan offers savings and insurance benefits together.",
+    "ulip": "💡 LIC SIIP and Nivesh Plus are good ULIP options with market-linked returns.",
+    "pension": "💡 LIC Jeevan Akshay and PM Vaya Vandana Yojana are best for pension seekers."
+}
+
+def chatbot(history, query):
     query_embedding = model.encode(query, convert_to_tensor=True)
     scores = util.pytorch_cos_sim(query_embedding, faq_embeddings)[0]
-    best_score = float(scores.max())  # Get max similarity score
+    best_score = float(scores.max())
     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."
-        )
+    if best_score < 0.6:
+        response = "🤖 I'm not confident I have the right answer for that. Please ask about LIC policies, claims, commissions, onboarding, or KYC."
     else:
-        return faq_df.iloc[best_idx]['answer']
+        response = faq_df.iloc[best_idx]['answer']
 
+    # Policy recommendation based on keywords
+    query_lower = query.lower()
+    for keyword, suggestion in policy_suggestions.items():
+        if keyword in query_lower:
+            response += f"\n\n{suggestion}"
+            break
 
-with gr.Blocks(title="LIC Agent Assistant") as demo:
+    history.append((query, response))
+    return history, history
+
+# UI using Gradio Blocks
+with gr.Blocks(title="LIC Agent Chatbot") as demo:
     gr.Markdown(
-        """
-        <h1 style='text-align: center; color: #1a237e;'>🧑‍💼 LIC Agent Assistant Chatbot</h1>
-        <p style='text-align: center;'>Ask questions about LIC policies, commissions, claims, onboarding, and more!</p>
-        """,
-        elem_id="header"
+        "<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>"
     )
 
-    with gr.Row():
-        with gr.Column(scale=1):
-            user_input = gr.Textbox(
-                label="Your Question",
-                placeholder="E.g., How do I file a claim?",
-                lines=2
-            )
-            submit_btn = gr.Button("Get Answer", variant="primary")
-
-        with gr.Column(scale=1):
-            output = gr.Textbox(
-                label="Answer",
-                placeholder="Response will appear here...",
-                lines=6
-            )
-
-    submit_btn.click(fn=chatbot, inputs=user_input, outputs=output)
+    chatbot_ui = gr.Chatbot(label="LIC Assistant", height=450)
+    msg = gr.Textbox(label="Your Question", placeholder="E.g., What is the commission for ULIP?")
+    clear = gr.Button("Clear Chat")
+
+    state = gr.State([])
+
+    msg.submit(chatbot, [state, msg], [chatbot_ui, state])
+    clear.click(lambda: ([], []), None, [chatbot_ui, state], queue=False)
 
 demo.launch()