Update app.py

app.py

@@ -1,14 +1,56 @@
-import streamlit as st, pandas as pd, random, plotly.graph_objects as go, plotly.express as px
-states, top_n = ["Minnesota", "Florida", "California"], 10
-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}]
+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)
-def roll(): return [random.randint(1, 10) for _ in range(1000)]
-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))
+
+# 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, labels={'x': 'Variant', 'y': 'Frequency'})
+    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)