Update app.py

app.py

@@ -143,6 +143,8 @@ def solution():
     """)
 
 
+import streamlit as st
+
 def perform_eda():
     st.title("Exploratory Data Analysis")
     st.write("""
@@ -154,6 +156,28 @@ def perform_eda():
     # Show the Power BI dashboard
     power_bi()
 
+    # Add insights and recommendations
+    display_insights_and_recommendations()
+
+def display_insights_and_recommendations():
+    st.subheader("Data Insights and Recommendations")
+    st.write("""
+    From the dashboard, you can now appreciate the serious income inequality problem. Explore key insights and actionable recommendations for stakeholders to fight income inequality.
+    """)
+
+    # Table with insights and recommendations
+    st.table([
+        ["🎓 Higher education levels positively correlate with higher income.", "Invest in accessible and quality education, including scholarships and vocational training, for lower-income communities."],
+        ["👩‍🎓 Women are more likely below the income threshold than men.", "Support gender equality programs addressing wage disparities and encouraging women in STEM fields."],
+        ["👥 Income inequality exists across all employment statuses.", "Implement policies and programs supporting stable employment, job training, and entrepreneurship."],
+        ["🌍 Racial income disparities: Foster diversity and inclusion in workplaces.", "Promote equal opportunities, diversity training, and an inclusive work environment."],
+        ["🌐 Foreigners concentrated below the income threshold.", "Review immigration policies to ensure fair treatment and integration into the workforce."],
+        ["🏢 Majority below threshold in 'Unknown' occupations.", "Research challenges in different occupations and implement targeted support programs."],
+        ["💸 Nonfilers have higher representation below the threshold.", "Evaluate tax policies for fairness and consider incentives for low-income individuals."],
+        ["📊 Data-driven insights are crucial for addressing income inequality.", "Continue investing in data collection and analysis to inform evolving policies."]
+    ])
+
+# Define the Power BI display
 def power_bi():
     """
     Embeds the Power BI report with specified dimensions and full-screen height.
@@ -187,25 +211,6 @@ def power_bi():
         """, unsafe_allow_html=True)
 
 
-    # Add insights and recommendations
-    st.subheader("Data Insights and Recommendations")
-    st.write("""
-    From the dashboard, you can now appreciate the serious income inequality problem. Explore key insights and actionable recommendations for stakeholders to fight income inequality.
-    """)
-
-    # Table with insights and recommendations
-    st.table([
-        ["🎓 Higher education levels positively correlate with higher income.", "Invest in accessible and quality education, including scholarships and vocational training, for lower-income communities."],
-        ["👩‍🎓 Women are more likely below the income threshold than men.", "Support gender equality programs addressing wage disparities and encouraging women in STEM fields."],
-        ["👥 Income inequality exists across all employment statuses.", "Implement policies and programs supporting stable employment, job training, and entrepreneurship."],
-        ["🌍 Racial income disparities: Foster diversity and inclusion in workplaces.", "Promote equal opportunities, diversity training, and an inclusive work environment."],
-        ["🌐 Foreigners concentrated below the income threshold.", "Review immigration policies to ensure fair treatment and integration into the workforce."],
-        ["🏢 Majority below threshold in 'Unknown' occupations.", "Research challenges in different occupations and implement targeted support programs."],
-        ["💸 Nonfilers have higher representation below the threshold.", "Evaluate tax policies for fairness and consider incentives for low-income individuals."],
-        ["📊 Data-driven insights are crucial for addressing income inequality.", "Continue investing in data collection and analysis to inform evolving policies."]
-    ])
-
-
 
 def prediction():