Update app.py

app.py

@@ -4,53 +4,60 @@ 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():
+def health_game():
+    # 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, treatment recommendation and potential savings
+    health_conditions = [
+        {"condition": "💔 Heart disease", "emoji": "💗", "spending": 214.3, "treatment": "Regular checkups with a cardiologist", "savings": "$1000"},
+        {"condition": "🤕 Trauma-related disorders", "emoji": "🚑", "spending": 198.6, "treatment": "Counseling and physical therapy", "savings": "$500"},
+        {"condition": "🦀 Cancer", "emoji": "🎗️", "spending": 171.0, "treatment": "Early detection and treatment", "savings": "$2000"},
+        {"condition": "🧠 Mental disorders", "emoji": "🧘", "spending": 150.8, "treatment": "Therapy and medication", "savings": "$1500"},
+        {"condition": "🦴 Osteoarthritis and joint disorders", "emoji": "🏥", "spending": 142.4, "treatment": "Low-impact exercise and physical therapy", "savings": "$800"},
+        {"condition": "💉 Diabetes", "emoji": "🩸", "spending": 107.4, "treatment": "Regular checkups and medication", "savings": "$1200"},
+        {"condition": "🫁 Chronic obstructive pulmonary disease and asthma", "emoji": "🫀", "spending": 91.0, "treatment": "Inhalers and breathing exercises", "savings": "$600"},
+        {"condition": "🩺 Hypertension", "emoji": "💉", "spending": 83.9, "treatment": "Lifestyle changes and medication", "savings": "$900"},
+        {"condition": "🔬 Hyperlipidemia", "emoji": "🔬", "spending": 83.9, "treatment": "Lifestyle changes and medication", "savings": "$700"},
+        {"condition": "🦴 Back problems", "emoji": "🧍", "spending": 67.0, "treatment": "Physical therapy and exercise", "savings": "$400"}
+    ]
+
+    # 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():
+        rolls = [random.randint(1, 10) for _ in range(1000)]
+        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)
+    condition_idx = st.selectbox("Select your current health condition", df_top_conditions.index)
+    row = df_top_conditions.loc[condition_idx]
+
+    st.write(f"Based on the severity of your {row['condition']}, we recommend {row['treatment']} for early treatment. This could save you up to {row['savings']} in healthcare costs.")
+
     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)
+
+
+health_game()