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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", "Anthony Finch", "Ariel CL Ong",
"Deren Meng", "Prabhavathi Bharadwaj", "Tan Li Lin",
"Wei Shan Chin", "Wong Chen Mey", "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 - PRUActive LinkGuard purchased for self")
st.write("")
st.write("Date: 08/11/2013 - PRUWealth Plus (SGD) purchased 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$50,000
Last policies purchased:
Date: 02/12/2017 - PRUActive LinkGuard purchased for self
Date: 08/11/2013 - PRUWealth Plus (SGD) purchased for daughter
Insights
=========
Recommended Products:
- {top_products[0]}
- {top_products[1]}
- {top_products[2]}
Top LIA Coverage Gap:
- {poe_findings}
Propensity to buy score: {score_msg}
Predictive model reasonings
===========================
- {model_reasons[0]}
- {model_reasons[1]}
- {model_reasons[2]}
- {model_reasons[3]}
- {model_reasons[4]}
""".strip()
with summary_tab:
txt = st.text_area(
"About this customer",
about_this_cust,
height=500
)
with pitch_tab:
st.write("Suggest sales pitch for this customer")
generate_button = st.button("Generate")
if generate_button:
placeholder = st.empty()
full_response = ""
stream = get_ai_response(about_this_cust)
for chunk in stream:
token = chunk.choices[0].delta.content
if token is not None:
# full_response += token
full_response += token.replace("\n", " \n") \
.replace("$", "\$")
# .replace("\[", "$$")
placeholder.markdown(full_response)
placeholder.markdown(full_response)
print(full_response) |