app.py

import streamlit as st
import plotly.express as px
import pandas as pd

# Define the states and conditions of interest with emojis
states = ["Minnesota 🌲", "Florida 🌴", "California 🌞"]
top_n = 10

# Define the list dictionary of top 10 health conditions descending by cost with emojis and Wikipedia links
health_conditions = [
    {
        "condition": "Heart disease ❤️", 
        "spending": 214.3, 
        "definition": "Heart disease is when your heart has problems working properly. It can cause chest pain and other symptoms. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Heart_disease)."
    },
    {
        "condition": "Trauma-related disorders 🤕", 
        "spending": 198.6, 
        "definition": "Trauma-related disorders are mental health conditions that can occur after someone experiences a traumatic event, like a car accident or natural disaster. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Trauma_(medicine))."
    },
    {
        "condition": "Cancer 🦀", 
        "spending": 171.0, 
        "definition": "Cancer is a disease where your body's cells grow out of control. It can cause lumps or tumors and make you feel sick. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Cancer)."
    },
    {
        "condition": "Mental disorders 🧠", 
        "spending": 150.8, 
        "definition": "Mental disorders are conditions that affect your mood, behavior, or thinking. They can make it hard to function normally. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Mental_disorder)."
    },
    {
        "condition": "Osteoarthritis and joint disorders 🦴", 
        "spending": 142.4, 
        "definition": "Osteoarthritis and joint disorders are conditions that can cause pain and stiffness in your joints. They often happen as you get older. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Osteoarthritis)."
    },
    {
        "condition": "Diabetes 🍬", 
        "spending": 107.4, 
        "definition": "Diabetes is a condition where your body has trouble processing sugar. It can cause high blood sugar levels and make you feel sick. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Diabetes)."
    },
    {
        "condition": "Chronic obstructive pulmonary disease and asthma 🫁", 
        "spending": 91.0, 
        "definition": "Chronic obstructive pulmonary disease (COPD) and asthma are conditions that can make it hard to breathe. They can cause coughing, wheezing, and other symptoms. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Chronic_obstructive_pulmonary_disease)."
    },
    {
        "condition": "Hypertension 🩺", 
        "spending": 83.9, 
        "definition": "Hypertension is another word for high blood pressure. It can put you at risk for heart disease and other health problems. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Hypertension).
},
{
    "condition": "Hyperlipidemia 🍔", 
    "spending": 83.9, 
    "definition": "Hyperlipidemia is a condition where you have high levels of fat in your blood. It can put you at risk for heart disease and other health problems. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Hyperlipidemia)."
},
{
    "condition": "Back problems 👨‍⚕️", 
    "spending": 67.0, 
    "definition": "Back problems can cause pain and stiffness in your back. They can be caused by a variety of factors, like poor posture or injuries. Learn more at [Wikipedia](https://en.wikipedia.org/wiki/Back_pain)."
}
]

#Total the spending values
total_spending = sum([hc["spending"] for hc in health_conditions])

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

#Create a new field showing the state with the most cases per year for each condition
df_top_conditions['most_cases_state'] = ''
for i, row in df_top_conditions.iterrows():
    condition = row['condition'].split(' ')[0]
    condition_df = pd.read_csv(f'{condition}.csv')
    condition_df = condition_df[condition_df['state'].isin(states)]
    most_cases_state = condition_df.groupby('state')['cases'].sum().idxmax()
    df_top_conditions.at[i, 'most_cases_state'] = f'{most_cases_state} 🏆'

#Create the treemap graph using Plotly Express
fig = px.treemap(df_top_conditions, path=["condition"], values="spending", color='most_cases_state')

#Set the title of the graph
fig.update_layout(title=f"Top {top_n} Health Conditions in {', '.join(states)} by Spending (Total: ${total_spending}B)")

#Display the graph in Streamlit
st.plotly_chart(fig)

#Display definitions of each condition
st.markdown("## Condition Definitions")
for hc in health_conditions:
    st.markdown(f"### {hc['condition']}")
    st.markdown(hc['definition'])