added daily revenue table

app.py

@@ -69,11 +69,32 @@ def analyze_sales_performance(period, api_token):
         data = get_sales_data(period, api_token)
         
         if data.empty:
-            return "No sales data available for the selected period.", None, None
+            return "No sales data available for the selected period.", None, None, pd.DataFrame()
         
         # Get enhanced KPIs for Wildberries data
         kpis = create_wb_kpi_cards(data)
         
+        # Create daily revenue breakdown table
+        daily_revenue_table = pd.DataFrame()
+        if 'sale_date' in data.columns and 'total_price' in data.columns:
+            daily_revenue = data.groupby(data['sale_date'].dt.date).agg({
+                'total_price': 'sum',
+                'quantity': 'sum',
+                'sales_commission': 'sum' if 'sales_commission' in data.columns else lambda x: 0,
+                'amount_for_pay': 'sum' if 'amount_for_pay' in data.columns else lambda x: 0
+            }).reset_index()
+            
+            daily_revenue_table = pd.DataFrame({
+                'Date': daily_revenue['sale_date'],
+                'Revenue (₽)': daily_revenue['total_price'].round(2),
+                'Orders': daily_revenue['quantity'],
+                'Commission (₽)': daily_revenue.get('sales_commission', 0).round(2) if 'sales_commission' in daily_revenue.columns else 0,
+                'Net Revenue (₽)': daily_revenue.get('amount_for_pay', daily_revenue['total_price']).round(2)
+            })
+            
+            # Sort by date descending (most recent first)
+            daily_revenue_table = daily_revenue_table.sort_values('Date', ascending=False)
+        
         # Create sales summary with Wildberries-specific metrics
         mode_indicator = "Demo Mode" if not api_token or api_token.strip() == "" else "Live Data"
         
@@ -107,12 +128,12 @@ def analyze_sales_performance(period, api_token):
         if 'sales_commission' in data.columns:
             commission_chart = create_commission_analysis_chart(data)
         
-        return summary, main_chart, commission_chart
+        return summary, main_chart, commission_chart, daily_revenue_table
         
     except Exception as e:
         error_msg = f"Error analyzing sales: {str(e)}"
         gr.Error(error_msg)
-        return error_msg, None, None
+        return error_msg, None, None, pd.DataFrame()
 
 def calculate_stockout_forecast(method, api_token):
     """Calculate days until stockout for products"""
@@ -301,6 +322,7 @@ def create_interface():
         
         with gr.Tabs():
             # Sales Analytics Tab
+                        # Sales Analytics Tab
             with gr.TabItem("📊 Sales Analytics"):
                 with gr.Row():
                     with gr.Column(scale=1):
@@ -321,11 +343,20 @@ def create_interface():
                 with gr.Row():
                     commission_chart = gr.Plot(label="Commission Analysis Dashboard", visible=False)
                 
+                # Daily Revenue Table
+                with gr.Row():
+                    daily_revenue_table = gr.DataFrame(
+                        label="📅 Daily Revenue Breakdown",
+                        headers=["Date", "Revenue (₽)", "Orders", "Commission (₽)", "Net Revenue (₽)"],
+                        datatype=["str", "number", "number", "number", "number"],
+                        interactive=False
+                    )
+                
                 # Event handlers
                 analyze_btn.click(
                     fn=analyze_sales_performance,
                     inputs=[period_selector, api_token_input],
-                    outputs=[sales_summary, sales_chart, commission_chart]
+                    outputs=[sales_summary, sales_chart, commission_chart, daily_revenue_table]
                 )
             
             # Inventory Forecasting Tab