Update app.py

app.py

@@ -147,96 +147,118 @@ def generate_sample_data():
 
 # --- Page Rendering Functions ---
 def render_dashboard():
-    st.header("📊 Comprehensive Business Performance Dashboard")
+    st.header("📊 Advanced Business Intelligence 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))
+    # Enhanced Data Generation with Predictive Elements
+    def generate_predictive_data():
+        dates = pd.date_range(start='2024-01-01', end='2024-02-15', freq='D')
+        base_data = pd.DataFrame({
+            'Date': dates,
+            'Revenue': np.random.normal(1000, 100, len(dates)),
+            'Users': np.random.randint(100, 200, len(dates)),
+            'Engagement': np.random.uniform(0.5, 0.9, len(dates)),
+            'Category': np.random.choice(['Digital', 'Physical', 'Service'], len(dates))
+        })
+        
+        # Simple predictive modeling
+        base_data['Predicted_Revenue'] = base_data['Revenue'] * np.linspace(1, 1.2, len(dates))
+        base_data['Revenue_Trend'] = np.where(base_data['Predicted_Revenue'] > base_data['Revenue'], 'Positive', 'Negative')
+        
+        return base_data
+
+    # Data Preparation
+    data = generate_predictive_data()
+    
+    # Sidebar Filters
+    st.sidebar.header("Dashboard Filters")
+    selected_categories = st.sidebar.multiselect(
+        "Select Categories", 
+        options=data['Category'].unique(),
+        default=data['Category'].unique()
+    )
+    
+    date_range = st.sidebar.date_input(
+        "Select Date Range", 
+        [data['Date'].min(), data['Date'].max()]
+    )
+    
+    # Filter Data
+    filtered_data = data[
+        (data['Category'].isin(selected_categories)) & 
+        (data['Date'].between(date_range[0], date_range[1]))
+    ]
     
-    # Top-level KPI Section
+    # KPI Metrics
     col1, col2, col3, col4 = st.columns(4)
     with col1:
         st.metric("Total Revenue", 
-                  f"${data['Revenue'].sum():,.2f}", 
-                  delta=f"{data['Revenue'].pct_change().mean()*100:.2f}%")
+                  f"${filtered_data['Revenue'].sum():,.2f}", 
+                  delta=f"{filtered_data['Revenue'].pct_change().mean()*100:.2f}%")
     with col2:
         st.metric("Total Users", 
-                  f"{data['Users'].sum():,}", 
-                  delta=f"{data['Users'].pct_change().mean()*100:.2f}%")
+                  f"{filtered_data['Users'].sum():,}", 
+                  delta=f"{filtered_data['Users'].pct_change().mean()*100:.2f}%")
     with col3:
         st.metric("Avg Engagement", 
-                  f"{data['Engagement'].mean():.2%}", 
-                  delta=f"{data['Engagement'].pct_change().mean()*100:.2f}%")
+                  f"{filtered_data['Engagement'].mean():.2%}")
     with col4:
-        st.metric("Profit Margin", 
-                  f"{data['Profit_Margin'].mean():.2%}", 
-                  delta=f"{data['Profit_Margin'].pct_change().mean()*100:.2f}%")
+        st.metric("Predicted Trend", 
+                  filtered_data['Revenue_Trend'].mode()[0])
     
-    # Visualization Grid
+    # Advanced Visualizations
     col1, col2 = st.columns(2)
     
     with col1:
-        st.subheader("Revenue & Profit Trends")
-        fig_revenue = go.Figure()
-        fig_revenue.add_trace(go.Scatter(
-            x=data['Date'], 
-            y=data['Revenue'], 
+        st.subheader("Revenue Forecast")
+        forecast_fig = go.Figure()
+        forecast_fig.add_trace(go.Scatter(
+            x=filtered_data['Date'], 
+            y=filtered_data['Revenue'], 
             mode='lines', 
-            name='Revenue',
+            name='Actual Revenue',
             line=dict(color='blue')
         ))
-        fig_revenue.add_trace(go.Scatter(
-            x=data['Date'], 
-            y=data['Profit_Margin'], 
+        forecast_fig.add_trace(go.Scatter(
+            x=filtered_data['Date'], 
+            y=filtered_data['Predicted_Revenue'], 
             mode='lines', 
-            name='Profit Margin',
-            line=dict(color='green')
+            name='Predicted Revenue',
+            line=dict(color='red', dash='dot')
         ))
-        fig_revenue.update_layout(height=350)
-        st.plotly_chart(fig_revenue, use_container_width=True)
+        st.plotly_chart(forecast_fig, use_container_width=True)
     
     with col2:
-        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'
+        st.subheader("Category Performance")
+        category_performance = filtered_data.groupby('Category').agg({
+            'Revenue': ['sum', 'mean'],
+            'Users': 'sum',
+            'Engagement': 'mean'
+        }).reset_index()
+        category_performance.columns = ['Category', 'Total_Revenue', 'Avg_Revenue', 'Total_Users', 'Avg_Engagement']
+        
+        perf_fig = px.bar(
+            category_performance, 
+            x='Category', 
+            y='Total_Revenue', 
+            color='Avg_Engagement',
+            hover_data=['Total_Users', 'Avg_Revenue']
         )
-        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)
+        st.plotly_chart(perf_fig, use_container_width=True)
     
-    # Bottom Summary
-    st.subheader("Quick Insights")
-    insights_col1, insights_col2 = st.columns(2)
+    # Predictive Insights
+    st.subheader("Predictive Insights")
+    col1, col2 = st.columns(2)
     
-    with insights_col1:
-        st.metric("Top Performing Category", 
-                  category_performance.loc[category_performance['Revenue'].idxmax(), 'Category'])
+    with col1:
+        top_category = category_performance.loc[category_performance['Total_Revenue'].idxmax()]
+        st.metric("Top Revenue Category", 
+                  top_category['Category'], 
+                  delta=f"${top_category['Total_Revenue']:,.2f}")
     
-    with insights_col2:
-        st.metric("Highest Engagement Category", 
-                  category_performance.loc[category_performance['Engagement'].idxmax(), 'Category'])
+    with col2:
+        growth_prediction = filtered_data['Predicted_Revenue'].mean() / filtered_data['Revenue'].mean() - 1
+        st.metric("Revenue Growth Prediction", 
+                  f"{growth_prediction:.2%}")
 
 def render_analytics():
     st.header("🔍 Data Analytics")