feat: update APR data filtering to exclude zero and -100 values, enhance graph handling for negative APR

app.py

@@ -251,15 +251,15 @@ def fetch_apr_data_from_db():
                 # Add is_dummy flag (all real data)
                 apr_data["is_dummy"] = False
                 
-                # CHANGE: Only include positive APR values (greater than 0)
-                if apr_data["apr"] > 0:
+                # Include all APR values (including negative ones) EXCEPT zero and -100
+                if apr_data["apr"] != 0 and apr_data["apr"] != -100:
                     apr_data["metric_type"] = "APR"
                     logger.debug(f"Agent {agent_name} ({attr['agent_id']}): APR value: {apr_data['apr']}")
-                    # Add to the data list only if value is positive
+                    # Add to the data list
                     apr_data_list.append(apr_data)
                 else:
-                    # Log that we're skipping non-positive values
-                    logger.debug(f"Skipping non-positive value for agent {agent_name} ({attr['agent_id']}): {apr_data['apr']}")
+                    # Log that we're skipping zero or -100 values
+                    logger.debug(f"Skipping value for agent {agent_name} ({attr['agent_id']}): {apr_data['apr']} (zero or -100)")
         
         # Convert list of dictionaries to DataFrame
         if not apr_data_list:
@@ -273,8 +273,8 @@ def fetch_apr_data_from_db():
         logger.info(f"Created DataFrame with {len(global_df)} rows")
         logger.info(f"DataFrame columns: {global_df.columns.tolist()}")
         logger.info(f"APR statistics: min={global_df['apr'].min()}, max={global_df['apr'].max()}, mean={global_df['apr'].mean()}")
-        # After filtering, all values are APR type
-        logger.info("All values are APR type (positive values only)")
+        # All values are APR type (excluding zero and -100 values)
+        logger.info("All values are APR type (excluding zero and -100 values)")
         logger.info(f"Agents count: {global_df['agent_name'].value_counts().to_dict()}")
         
         # Log the entire dataframe for debugging
@@ -534,16 +534,15 @@ def create_combined_time_series_graph(df):
     unique_agents = df['agent_id'].unique()
     colors = px.colors.qualitative.Plotly[:len(unique_agents)]
     
-    # IMPORTANT: Fixed y-axis range that always includes -100
-    # Since we're only showing positive values, adjust the range
-    min_apr = 0  # Start at 0
+    # Update y-axis range to include negative values
+    min_apr = min(df['apr'].min() * 1.1, -10)  # Add 10% padding, minimum of -10
     max_apr = max(df['apr'].max() * 1.1, 10)  # Add 10% padding, minimum of 10
     
     # Add background shapes for APR and Performance regions
     min_time = df['timestamp'].min()
     max_time = df['timestamp'].max()
     
-    # Add shape for APR region (above zero)
+    # Add shape for positive APR region (above zero)
     fig.add_shape(
         type="rect",
         fillcolor="rgba(230, 243, 255, 0.3)",
@@ -553,6 +552,16 @@ def create_combined_time_series_graph(df):
         layer="below"
     )
     
+    # Add shape for negative APR region (below zero)
+    fig.add_shape(
+        type="rect",
+        fillcolor="rgba(255, 230, 230, 0.3)",
+        line=dict(width=0),
+        y0=min_apr, y1=0,
+        x0=min_time, x1=max_time,
+        layer="below"
+    )
+    
     # Add zero line
     fig.add_shape(
         type="line",
@@ -621,7 +630,7 @@ def create_combined_time_series_graph(df):
     
     # Update layout - use simple boolean values everywhere
     fig.update_layout(
-        title="APR Values for All Agents (Positive Values Only)",
+        title="APR Values for All Agents",
         xaxis_title="Time",
         yaxis_title="Value",
         template="plotly_white",
@@ -644,10 +653,10 @@ def create_combined_time_series_graph(df):
         showgrid=True, 
         gridwidth=1, 
         gridcolor='rgba(0,0,0,0.1)',
-        range=[min_apr, max_apr],  # Fixed range
+        range=[min_apr, max_apr],  # Updated range including negative values
         tickmode='linear',
         tick0=0,
-        dtick=10  # Adjusted for positive values
+        dtick=10
     )
     
     # Update x-axis
@@ -714,10 +723,10 @@ def create_combined_time_series_graph(df):
         
         # Simplified layout
         simple_fig.update_layout(
-            title="APR Values (Simplified View - Positive Values Only)",
+            title="APR Values for All Agents",
             xaxis_title="Time",
             yaxis_title="Value",
-            yaxis=dict(range=[0, max_apr]),
+            yaxis=dict(range=[min_apr, max_apr]),
             height=600,
             width=1000
         )
@@ -1168,23 +1177,13 @@ def dashboard():
                 
                 # Set up the button click event with error handling
                 try:
-                    # Try newer Gradio API first
-                    refresh_btn.click(
-                        fn=update_apr_graph,
-                        inputs=None,
-                        outputs=combined_graph
-                    )
-                except TypeError:
-                    # Fall back to original method
-                    refresh_btn.click(
-                        fn=update_apr_graph,
-                        inputs=[],
-                        outputs=[combined_graph]
-                    )
-                
+                    # Use Gradio's button click properly
+                    refresh_btn.click(fn=update_apr_graph, outputs=combined_graph)
+                except Exception as e:
+                    logger.error(f"Error setting up button handler: {e}")
+                    
                 # Initialize the graph on load
                 # We'll use placeholder figure initially
-                import plotly.graph_objects as go
                 placeholder_fig = go.Figure()
                 placeholder_fig.add_annotation(
                     text="Click 'Refresh APR Data' to load APR graph",