feat: update active agent count logic

app.py

@@ -1593,9 +1593,21 @@ def create_combined_time_series_graph(df):
             formatted_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S')
             
             # Calculate number of active agents in the last 24 hours
+            # Use ROI data after April 25th, 2025, and APR data before that date
             time_24h_ago = timestamp - pd.Timedelta(hours=24)
-            active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
-                                        (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+            april_25_2025 = datetime(2025, 4, 25)
+            
+            if timestamp >= april_25_2025 and global_roi_df is not None and not global_roi_df.empty:
+                # After April 25th, 2025: Use ROI data
+                roi_window_data = global_roi_df[(global_roi_df['timestamp'] >= time_24h_ago) & 
+                                              (global_roi_df['timestamp'] <= timestamp)]
+                active_agents = len(roi_window_data['agent_id'].unique())
+                logger.debug(f"Using ROI data for active agent count at {timestamp} (after Apr 25): {active_agents} agents")
+            else:
+                # Before April 25th, 2025 or if ROI data is not available: Use APR data
+                active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
+                                           (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+                logger.debug(f"Using APR data for active agent count at {timestamp} (before Apr 25): {active_agents} agents")
             
             hover_data_apr.append(
                 f"Time: {formatted_timestamp}<br>Avg APR (3d window): {row['moving_avg']:.2f}<br>Active agents (24h): {active_agents}"
@@ -1634,9 +1646,21 @@ def create_combined_time_series_graph(df):
                 formatted_timestamp = timestamp.strftime('%Y-%m-%d %H:%M:%S')
                 
                 # Calculate number of active agents in the last 24 hours
+                # Use ROI data after April 25th, 2025, and APR data before that date
                 time_24h_ago = timestamp - pd.Timedelta(hours=24)
-                active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
-                                           (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+                april_25_2025 = datetime(2025, 4, 25)
+                
+                if timestamp >= april_25_2025 and global_roi_df is not None and not global_roi_df.empty:
+                    # After April 25th, 2025: Use ROI data
+                    roi_window_data = global_roi_df[(global_roi_df['timestamp'] >= time_24h_ago) & 
+                                                  (global_roi_df['timestamp'] <= timestamp)]
+                    active_agents = len(roi_window_data['agent_id'].unique())
+                    logger.debug(f"Using ROI data for adjusted APR active agent count at {timestamp} (after Apr 25)")
+                else:
+                    # Before April 25th, 2025 or if ROI data is not available: Use APR data
+                    active_agents = len(apr_data[(apr_data['timestamp'] >= time_24h_ago) & 
+                                               (apr_data['timestamp'] <= timestamp)]['agent_id'].unique())
+                    logger.debug(f"Using APR data for adjusted APR active agent count at {timestamp} (before Apr 25)")
                 
                 if pd.notna(row['adjusted_moving_avg']):
                     hover_data_adj.append(