backupapp.py

import streamlit as st
import pandas as pd
import random
import plotly.graph_objects as go
import plotly.express as px

# Define the states and conditions of interest
states = ["Minnesota", "Florida", "California"]
top_n = 10

# Define the list dictionary of top 10 health conditions descending by cost, with emojis
health_conditions = [
    {"condition": "💔 Heart disease", "emoji": "💗", "spending": 214.3},
    {"condition": "🤕 Trauma-related disorders", "emoji": "🚑", "spending": 198.6},
    {"condition": "🦀 Cancer", "emoji": "🎗️", "spending": 171.0},
    {"condition": "🧠 Mental disorders", "emoji": "🧘", "spending": 150.8},
    {"condition": "🦴 Osteoarthritis and joint disorders", "emoji": "🏥", "spending": 142.4},
    {"condition": "💉 Diabetes", "emoji": "🩸", "spending": 107.4},
    {"condition": "🫁 Chronic obstructive pulmonary disease and asthma", "emoji": "🫀", "spending": 91.0},
    {"condition": "🩺 Hypertension", "emoji": "💉", "spending": 83.9},
    {"condition": "🔬 Hyperlipidemia", "emoji": "🔬", "spending": 83.9},
    {"condition": "🦴 Back problems", "emoji": "🧍", "spending": 67.0}
]

# Create a DataFrame from the list dictionary
df_top_conditions = pd.DataFrame(health_conditions)

# Calculate the total spending
total_spending = round(df_top_conditions["spending"].sum(), 1)

# Define the roll function
def roll():
    # Generate a thousand 10-sided dice rolls for each health condition
    rolls = [random.randint(1, 10) for _ in range(1000)]
    # Calculate the frequency of each variant
    frequencies = [rolls.count(i) for i in range(1, 11)]
    return frequencies

# Define the sunburst chart
fig_sunburst = go.Figure(go.Sunburst(
    labels=df_top_conditions["emoji"] + " " + df_top_conditions["condition"],
    parents=[""] * top_n,
    values=df_top_conditions["spending"],
    maxdepth=2
))

# Customize the layout of the sunburst chart
fig_sunburst.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)")

# Display the sunburst chart and variants per condition in the Streamlit app
st.plotly_chart(fig_sunburst)
for index, row in df_top_conditions.iterrows():
    frequencies = roll()
    fig_bar = px.bar(x=[f"Variant {i}" for i in range(1, 11)], y=frequencies[:10], labels={'x': 'Variant', 'y': 'Frequency'})
    fig_bar.update_layout(title=f"Variants of {row['condition']} ({row['emoji']})")
    st.plotly_chart(fig_bar)