Update app.py

app.py

@@ -1,23 +1,35 @@
 import streamlit as st
 import plotly.express as px
-import pandas as pd
-
-# Load the data
-df = pd.read_csv("health_conditions_data.csv")
 
 # Define the states and conditions of interest
 states = ["Minnesota", "Florida", "California"]
 top_n = 10
 
-# Filter the data by the selected states and get the top n conditions
-df_filtered = df[df["state"].isin(states)]
-df_top_conditions = df_filtered.groupby("condition")["count"].sum().nlargest(top_n)
+# Define the list dictionary of top 10 health conditions descending by cost
+health_conditions = [
+    {"condition": "Heart disease", "spending": 214.3},
+    {"condition": "Trauma-related disorders", "spending": 198.6},
+    {"condition": "Cancer", "spending": 171.0},
+    {"condition": "Mental disorders", "spending": 150.8},
+    {"condition": "Osteoarthritis and joint disorders", "spending": 142.4},
+    {"condition": "Diabetes", "spending": 107.4},
+    {"condition": "Chronic obstructive pulmonary disease and asthma", "spending": 91.0},
+    {"condition": "Hypertension", "spending": 83.9},
+    {"condition": "Hyperlipidemia", "spending": 83.9},
+    {"condition": "Back problems", "spending": 67.0}
+]
+
+# 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 the treemap graph using Plotly Express
-fig = px.treemap(df_top_conditions, path=["condition"], values="count")
+fig = px.treemap(df_top_conditions, path=["condition"], values="spending")
 
 # Set the title of the graph
-fig.update_layout(title="Top 10 Health Conditions in {} by Count".format(", ".join(states)))
+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)