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results.py
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results.py
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import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from projection import calculate_projection
def show_results_page():
st.title("UK Financial Planning Tool - Results")
inputs = st.session_state.user_inputs
age_inputs = st.session_state.age_inputs
# Combine user and partner net worth breakdowns
combined_net_worth_breakdown = {}
for category in set(inputs["user_net_worth_breakdown"].keys()) | set(
inputs["partner_net_worth_breakdown"].keys()
):
combined_value = (
inputs["user_net_worth_breakdown"].get(category, {"value": 0})["value"]
+ inputs["partner_net_worth_breakdown"].get(category, {"value": 0})["value"]
)
combined_growth = max(
inputs["user_net_worth_breakdown"].get(category, {"growth": 0})["growth"],
inputs["partner_net_worth_breakdown"].get(category, {"growth": 0})[
"growth"
],
)
combined_net_worth_breakdown[category] = {
"value": combined_value,
"growth": combined_growth,
}
total_annual_income = inputs["user_annual_income"] + inputs["partner_annual_income"]
total_annual_expenses = (
inputs["user_annual_expenses"] + inputs["partner_annual_expenses"]
)
years_to_project = age_inputs["life_expectancy"] - age_inputs["current_age"]
# Calculate projection
projection, category_projections = calculate_projection(
combined_net_worth_breakdown,
total_annual_income,
total_annual_expenses,
inputs["inflation_rate"],
years_to_project,
age_inputs["retirement_age"],
age_inputs["current_age"],
)
# Create DataFrame for plotting
df = pd.DataFrame(
{
"Year": range(age_inputs["current_age"], age_inputs["life_expectancy"] + 1),
"Total Net Worth": projection,
**{category: values for category, values in category_projections.items()},
}
)
# Plot total net worth
fig_total = px.line(
df,
x="Year",
y="Total Net Worth",
title="Projected Combined Net Worth Over Lifetime",
)
fig_total.update_layout(yaxis_title="Net Worth (£)")
fig_total.add_vline(
x=age_inputs["retirement_age"],
line_dash="dash",
line_color="red",
annotation_text="Retirement Age",
annotation_position="top right",
)
st.plotly_chart(fig_total)
# Plot breakdown of net worth
fig_breakdown = go.Figure()
for category in category_projections.keys():
fig_breakdown.add_trace(
go.Scatter(x=df["Year"], y=df[category], name=category, stackgroup="one")
)
fig_breakdown.update_layout(
title="Net Worth Breakdown Over Time", yaxis_title="Net Worth (£)"
)
st.plotly_chart(fig_breakdown)
# Display key metrics
retirement_net_worth = projection[
age_inputs["retirement_age"] - age_inputs["current_age"]
]
final_net_worth = projection[-1]
st.header("Key Metrics")
col1, col2 = st.columns(2)
with col1:
st.metric("Net Worth at Retirement", f"£{retirement_net_worth:,.0f}")
st.metric("Final Net Worth", f"£{final_net_worth:,.0f}")
with col2:
annual_retirement_income = retirement_net_worth * 0.04 # Using the 4% rule
st.metric(
"Estimated Annual Retirement Income (4% Rule)",
f"£{annual_retirement_income:,.0f}",
)
years_of_expenses = final_net_worth / total_annual_expenses
st.metric("Years of Expenses Covered", f"{years_of_expenses:.1f}")
# Display current combined figures
st.header("Current Combined Figures")
col1, col2 = st.columns(2)
with col1:
st.metric("Total Annual Income", f"£{total_annual_income:,.0f}")
st.metric("Total Annual Expenses", f"£{total_annual_expenses:,.0f}")
with col2:
total_net_worth = sum(
data["value"] for data in combined_net_worth_breakdown.values()
)
st.metric("Total Current Net Worth", f"£{total_net_worth:,.0f}")
savings_rate = (
(total_annual_income - total_annual_expenses) / total_annual_income * 100
)
st.metric("Savings Rate", f"{savings_rate:.1f}%")
# Display net worth breakdown
st.header("Current Net Worth Breakdown")
breakdown_df = pd.DataFrame(
[
{
"Category": category,
"Value": data["value"],
"Growth Rate": f"{data['growth']*100:.1f}%",
"Liquidity": "Liquid" if data.get("is_liquid", False) else "Non-liquid"
}
for category, data in combined_net_worth_breakdown.items()
]
)
breakdown_df = breakdown_df.sort_values("Value", ascending=False)
st.table(breakdown_df)
# Retirement readiness assessment
st.header("Retirement Readiness Assessment")
if retirement_net_worth >= total_annual_expenses * 25:
st.success("You're on track for a comfortable retirement!")
elif retirement_net_worth >= total_annual_expenses * 15:
st.warning(
"You're making progress, but might want to consider increasing your savings."
)
else:
st.error(
"You may need to significantly increase your savings or adjust your retirement plans."
)