Update app.py

app.py

@@ -147,30 +147,96 @@ def generate_sample_data():
 
 # --- Page Rendering Functions ---
 def render_dashboard():
-    st.header("📊 Performance Dashboard")
+    st.header("📊 Comprehensive Business Performance Dashboard")
+    
+    # Generate sample data with more complex structure
     data = generate_sample_data()
+    data['Profit_Margin'] = data['Revenue'] * np.random.uniform(0.1, 0.3, len(data))
     
+    # Top-level KPI Section
     col1, col2, col3, col4 = st.columns(4)
     with col1:
-        st.metric("Total Revenue", f"${data['Revenue'].sum():,.2f}")
+        st.metric("Total Revenue", 
+                  f"${data['Revenue'].sum():,.2f}", 
+                  delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%")
     with col2:
-        st.metric("Total Users", f"{data['Users'].sum():,}")
+        st.metric("Total Users", 
+                  f"{data['Users'].sum():,}", 
+                  delta=f"{data['Users'].pct_change().mean()*100:.2f}%")
     with col3:
-        st.metric("Avg Engagement", f"{data['Engagement'].mean():.2%}")
+        st.metric("Avg Engagement", 
+                  f"{data['Engagement'].mean():.2%}", 
+                  delta=f"{data['Engagement'].pct_change().mean()*100:.2f}%")
     with col4:
-        st.metric("Active Days", len(data))
+        st.metric("Profit Margin", 
+                  f"{data['Profit_Margin'].mean():.2%}", 
+                  delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%")
     
+    # Visualization Grid
     col1, col2 = st.columns(2)
+    
     with col1:
-        st.subheader("Revenue Trend")
-        fig = px.line(data, x='Date', y='Revenue')
-        st.plotly_chart(fig, use_container_width=True)
+        st.subheader("Revenue & Profit Trends")
+        fig_revenue = go.Figure()
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Revenue'], 
+            mode='lines', 
+            name='Revenue',
+            line=dict(color='blue')
+        ))
+        fig_revenue.add_trace(go.Scatter(
+            x=data['Date'], 
+            y=data['Profit_Margin'], 
+            mode='lines', 
+            name='Profit Margin',
+            line=dict(color='green')
+        ))
+        fig_revenue.update_layout(height=350)
+        st.plotly_chart(fig_revenue, use_container_width=True)
     
     with col2:
-        st.subheader("User Engagement by Category")
-        fig = px.scatter(data, x='Date', y='Engagement', 
-                        size='Users', color='Category')
-        st.plotly_chart(fig, use_container_width=True)
+        st.subheader("User Engagement Analysis")
+        fig_engagement = px.scatter(
+            data, 
+            x='Users', 
+            y='Engagement', 
+            color='Category', 
+            size='Revenue',
+            hover_data=['Date'],
+            title='User Engagement Dynamics'
+        )
+        fig_engagement.update_layout(height=350)
+        st.plotly_chart(fig_engagement, use_container_width=True)
+    
+    # Category Performance
+    st.subheader("Category Performance Breakdown")
+    category_performance = data.groupby('Category').agg({
+        'Revenue': 'sum',
+        'Users': 'sum',
+        'Engagement': 'mean'
+    }).reset_index()
+    
+    fig_category = px.bar(
+        category_performance, 
+        x='Category', 
+        y='Revenue', 
+        color='Engagement',
+        title='Revenue by Category with Engagement Overlay'
+    )
+    st.plotly_chart(fig_category, use_container_width=True)
+    
+    # Bottom Summary
+    st.subheader("Quick Insights")
+    insights_col1, insights_col2 = st.columns(2)
+    
+    with insights_col1:
+        st.metric("Top Performing Category", 
+                  category_performance.loc[category_performance['Revenue'].idxmax(), 'Category'])
+    
+    with insights_col2:
+        st.metric("Highest Engagement Category", 
+                  category_performance.loc[category_performance['Engagement'].idxmax(), 'Category'])
 
 def render_analytics():
     st.header("🔍 Data Analytics")