app.py

import gradio as gr
import pandas as pd
from sentence_transformers import SentenceTransformer, util

# Load FAQ
try:
    faq_df = pd.read_csv("lic_faq.csv", encoding="utf-8")
except UnicodeDecodeError:
    faq_df = pd.read_csv("lic_faq.csv", encoding="ISO-8859-1")

model = SentenceTransformer('all-MiniLM-L6-v2')
faq_embeddings = model.encode(faq_df['question'].tolist(), convert_to_tensor=True)

# Policy recommendations
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_lower = query.lower().strip()

    # Handle greetings
    if query_lower in ["hi", "hello", "hey", "good morning", "good evening"]:
        response = "👋 Hello! I’m your LIC Assistant. Ask me anything about policies, claims, onboarding, or commission."
    else:
        query_embedding = model.encode(query, convert_to_tensor=True)
        scores = util.pytorch_cos_sim(query_embedding, faq_embeddings)[0]
        best_score = float(scores.max())
        best_idx = int(scores.argmax())

        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:
            response = faq_df.iloc[best_idx]['answer']

        for keyword, suggestion in policy_suggestions.items():
            if keyword in query_lower:
                response += f"\n\n{suggestion}"
                break

    history.append((query, response))
    return history, history

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, bubble_full_width=False, avatar_images=("🧑", "🤖"))

    with gr.Row():
        msg = gr.Textbox(placeholder="Ask your question here...", show_label=False, scale=8)
        send = gr.Button("Send", variant="primary", scale=2)

    clear = gr.Button("Clear Chat")

    state = gr.State([])

    send.click(fn=chatbot, inputs=[state, msg], outputs=[chatbot_ui, state])
    clear.click(lambda: ([], []), None, [chatbot_ui, state], queue=False)

demo.launch()