import plotly.express as px import streamlit as st import pandas as pd from ai_assistant import get_ai_response def get_score_rating(s): if s >= 0.75: return "HIGH" elif 0.4 <= s < 0.75: return "MEDIUM" elif s < 0.4: return "LOW" def get_cov_rating(c): if c >= 4: return "Sufficient Coverage" elif 2 <= c < 4: return "Insufficient Coverage" elif c < 2: return "Significantly Insufficient Coverage" @st.cache_data def get_cust_data_dict(cust_name="Wong Ling Yit"): data = pd.read_csv("data/yoda_data.csv") poe_data = pd.read_csv("data/yoda_poe.csv") reasons_df = pd.read_csv("data/yoda_reasonings.csv") temp = data[data["cust_name"] == cust_name] temp_poe = poe_data[poe_data["cust_name"] == cust_name] temp_reason = reasons_df[reasons_df["cust_name"] == cust_name] if len(temp) != 7 or \ len(temp_poe) != 1 or \ len(temp_reason) != 5: temp = data[data["cust_name"] == "Wong Ling Yit"] temp_poe = poe_data[poe_data["cust_name"] == "Wong Ling Yit"] temp_reason = reasons_df[reasons_df["cust_name"] == "Wong Ling Yit"] temp = temp.rename(columns={ "prod_cat": "Product Category", "cov_level": "Coverage Level", "prop_score": "Score", "recom_products": "Recommended Product" }) temp["Coverage Rating"] = temp["Coverage Level"].apply( lambda c: get_cov_rating(c) ) temp["Score Rating"] = temp["Score"].apply( lambda s: get_score_rating(s) ) cov_rating_map = dict(zip( temp["Product Category"], temp["Coverage Rating"] )) score_rating_map = dict(zip( temp["Product Category"], temp["Score Rating"] )) radar_df = pd.DataFrame({ "Product Category": [ "Retirement", "Protection", "Savings", "CI", "Investment", "Legacy", "Medical" ] }) radar_df = pd.merge(radar_df, temp, on="Product Category", how="inner") temp = temp.sort_values("Score", ascending=False).reset_index(drop=True) top_categories = temp[:3]["Product Category"].tolist() top_recom_products = temp[:3]["Recommended Product"].tolist() top_products = [] for c, p in zip(top_categories, top_recom_products): product_msg = f"{c}: {p}" top_products.append(product_msg) top_score = temp.iloc[0]["Score"] score_rating = get_score_rating(top_score) top_score_msg = f"{top_score:.2f} - {score_rating}" poe_findings = temp_poe.iloc[0]["poe_findings"] temp_reason = temp_reason.sort_values("r_index", ascending=True).reset_index(drop=True) temp_reason_ls = temp_reason["reasonings"].tolist() return (radar_df, temp, top_products, top_score_msg, poe_findings, temp_reason_ls, cov_rating_map, score_rating_map) st.title("Persona: Financial Consultant - Leads follow-up") st.header("Lead selection", divider="blue") st.subheader("My customers - Hot Lead🔥") cust_option = st.selectbox( label="Customer options", options=( "Darek Cieslinski", "Wong Ling Yit"), label_visibility="collapsed" ) ## "Wei Shan Chin", "Wong Chen Mey", "Tan Li Lin", "Prabhavathi Bharadwaj", ## "Deren Meng", "Anthony Finch", "Ariel CL Ong", "Darek Cieslinski" data_pack = get_cust_data_dict(cust_name=cust_option) radar_df = data_pack[0] df = data_pack[1] top_products = data_pack[2] score_msg = data_pack[3] poe_findings = data_pack[4] model_reasons = data_pack[5] cov_rating_map = data_pack[6] score_rating_map = data_pack[7] view_1, view_2 = st.columns(2, gap="medium") with view_1: st.subheader("Coverage level") fig = px.line_polar(radar_df, r="Coverage Level", theta="Product Category", line_close=True) fig.update_layout( margin=dict(l=60, r=40, t=20, b=20), ) fig.update_traces(fill="toself") st.plotly_chart(fig, theme="streamlit", use_container_width=True) with view_2: st.subheader("Propensity to buy") fig = px.bar(df, x="Product Category", y="Score") fig.update_layout( margin=dict(l=60, r=40, t=50, b=20), ) st.plotly_chart(fig, theme="streamlit", use_container_width=True) st.write("") st.write("***Expand to see more details.***") with st.expander("Recent engagement.."): st.subheader("Financial Needs Analysis (FNA)", divider="blue") st.write("") st.write("Date: 15/06/2022 - Protection need for family") st.write("") st.write("Date: 18/02/2019 - Critical Illness coverage gap of S$50,000") st.divider() st.subheader("Last policies purchased", divider="blue") st.write("") st.write("Date: 02/12/2017 - Purchased Protection Plan - PRUActive LinkGuard for self") st.write("") st.write("Date: 08/11/2013 - Purchased Savings Plan - PRUWealth Plus (SGD) for daughter") st.divider() st.write("") st.header("Insights", divider="blue") st.markdown( f""" **Recommended Products:** - {top_products[0]} - {top_products[1]} - {top_products[2]} **Top LIA Coverage Gap:** - {poe_findings} **Propensity to buy score:** - {score_msg} """ ) st.header("Reasonings", divider="blue") st.write("") st.markdown( f""" **Model Reasonings:** - {model_reasons[0]} - {model_reasons[1]} - {model_reasons[2]} - {model_reasons[3]} - {model_reasons[4]} """ ) st.write("") st.header("Sales pitch", divider="blue") list_of_cust_tabs = st.tabs(tabs=["Summary", "Assistant"]) summary_tab = list_of_cust_tabs[0] pitch_tab = list_of_cust_tabs[1] about_this_cust = f""" Opportunities =============== In terms of current coverage level, - Retirement: {cov_rating_map["Retirement"]} - Protection: {cov_rating_map["Protection"]} - Savings: {cov_rating_map["Savings"]} - Critical Illness: {cov_rating_map["CI"]} - Investment: {cov_rating_map["Investment"]} - Legacy: {cov_rating_map["Legacy"]} - Medical: {cov_rating_map["Medical"]} In terms of likelihood to buy, - Retirement: {score_rating_map["Retirement"]} - Protection: {score_rating_map["Protection"]} - Savings: {score_rating_map["Savings"]} - Critical Illness: {score_rating_map["CI"]} - Investment: {score_rating_map["Investment"]} - Legacy: {score_rating_map["Legacy"]} - Medical: {score_rating_map["Medical"]} Recent engagements =================== Financial Needs Analysis (FNA): Date: 15/06/2022 - Protection need for family Date: 18/02/2019 - Critical Illness coverage gap of S$50,000 Last policies purchased: Date: 02/12/2017 - Purchased Protection Plan - PRUActive LinkGuard for self Date: 08/11/2013 - Purchased Savings Plan - PRUWealth Plus (SGD) for daughter Insights ========= Recommended Products: - {top_products[0]} - {top_products[1]} - {top_products[2]} Top LIA Coverage Gap: - {poe_findings} Propensity to buy score: {score_msg} Predictive model reasonings =========================== - {model_reasons[0]} - {model_reasons[1]} - {model_reasons[2]} - {model_reasons[3]} - {model_reasons[4]} """.strip() with summary_tab: txt = st.text_area( "About this customer", about_this_cust, height=500 ) with pitch_tab: st.write("Suggest sales pitch for this customer") generate_button = st.button("Generate") if generate_button: placeholder = st.empty() full_response = "" stream = get_ai_response(about_this_cust) for chunk in stream: token = chunk.choices[0].delta.content if token is not None: # full_response += token full_response += token.replace("\n", " \n") \ .replace("$", "\$") # .replace("\[", "$$") placeholder.markdown(full_response) placeholder.markdown(full_response) print(full_response)