feat: add detailed debug logging for time series graph creation and simplify APR data plotting

app.py

@@ -470,6 +470,69 @@ def create_time_series_graph_per_agent(df):
     # Return the figure object for direct use in Gradio
     return fig
 
+def write_debug_info(df, fig):
+    """Write detailed debug information to logs for troubleshooting"""
+    try:
+        logger.info("==== GRAPH DEBUG INFORMATION ====")
+        logger.info(f"Total data points: {len(df)}")
+        logger.info(f"DataFrame columns: {df.columns.tolist()}")
+        
+        logger.info("Data types:")
+        for col in df.columns:
+            logger.info(f"  {col}: {df[col].dtype}")
+        
+        # Output sample data points
+        logger.info("Sample data (up to 5 rows):")
+        sample_df = df.head(5)
+        for idx, row in sample_df.iterrows():
+            logger.info(f"  Row {idx}: {row.to_dict()}")
+        
+        # Output Plotly figure structure
+        logger.info("Plotly Figure Structure:")
+        logger.info(f"  Number of traces: {len(fig.data)}")
+        for i, trace in enumerate(fig.data):
+            logger.info(f"  Trace {i}:")
+            logger.info(f"    Type: {trace.type}")
+            logger.info(f"    Mode: {trace.mode if hasattr(trace, 'mode') else 'N/A'}")
+            logger.info(f"    Name: {trace.name}")
+            
+            # Only log first few values to avoid overwhelming logs
+            if hasattr(trace, 'x') and trace.x is not None and len(trace.x) > 0:
+                x_sample = str(trace.x[:2])
+                logger.info(f"    X data sample (first 2): {x_sample}")
+            
+            if hasattr(trace, 'y') and trace.y is not None and len(trace.y) > 0:
+                y_sample = str(trace.y[:2])
+                logger.info(f"    Y data sample (first 2): {y_sample}")
+            
+            if hasattr(trace, 'line') and hasattr(trace.line, 'color'):
+                logger.info(f"    Line color: {trace.line.color}")
+            
+            if hasattr(trace, 'line') and hasattr(trace.line, 'width'):
+                logger.info(f"    Line width: {trace.line.width}")
+        
+        # Check environment
+        import os
+        import sys
+        import platform
+        
+        logger.info("Environment Information:")
+        logger.info(f"  Platform: {platform.platform()}")
+        logger.info(f"  Python version: {sys.version}")
+        logger.info(f"  Running in Docker: {'DOCKER_CONTAINER' in os.environ}")
+        logger.info(f"  Running in HF Space: {'SPACE_ID' in os.environ}")
+        
+        # Plotly version
+        import plotly
+        logger.info(f"  Plotly version: {plotly.__version__}")
+        
+        logger.info("End of debug info")
+        
+        return True
+    except Exception as e:
+        logger.error(f"Error writing debug info: {e}")
+        return False
+
 def create_combined_time_series_graph(df):
     """Create a combined time series graph for all agents using Plotly"""
     if len(df) == 0:
@@ -585,51 +648,42 @@ def create_combined_time_series_graph(df):
         for idx, row in agent_data.iterrows():
             logger.info(f"  Point {idx}: timestamp={row['timestamp']}, apr={row['apr']}, type={row['metric_type']}")
         
-        # Now add scatter points for APR values
+        # Get the APR data - this is what we'll plot
         apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        if not apr_data.empty:
-            logger.info(f"  Adding {len(apr_data)} APR markers for {agent_name}")
-            for idx, row in apr_data.iterrows():
-                logger.info(f"    APR marker: timestamp={row['timestamp']}, apr={row['apr']}")
-            
-                # Use explicit Python boolean for showlegend
-                is_first_point = bool(idx == apr_data.index[0])
-                fig.add_trace(
-                    go.Scatter(
-                        x=[row['timestamp']],
-                        y=[row['apr']],
-                        mode='markers',
-                        marker=dict(
-                            color='blue',  # Force consistent color
-                            symbol='circle',
-                            size=14,       # Make markers larger
-                            line=dict(
-                                width=2,
-                                color='black'
-                            )
-                        ),
-                        name=f'{agent_name} APR',
-                        legendgroup=agent_name,
-                        showlegend=is_first_point,  # Use native Python boolean
-                        hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
-                    )
-                )
         
-        # Add the line connecting APR data points
-        # MODIFIED: Make sure to use same color for line and marker and ensure line mode works
+        # SIMPLIFIED APPROACH: Use a single trace with lines+markers mode
+        # This is much more reliable across different platforms
         if not apr_data.empty:
+            logger.info(f"  Adding combined line+markers for {agent_name}")
+            
+            # Explicitly convert to Python lists
+            x_values = apr_data['timestamp'].tolist()
+            y_values = apr_data['apr'].tolist()
+            
+            # Log what we're about to plot
+            for i, (x, y) in enumerate(zip(x_values, y_values)):
+                logger.info(f"    Point {i+1}: x={x}, y={y}")
+            
+            # Use a single trace for both markers and lines
             fig.add_trace(
                 go.Scatter(
-                    x=apr_data['timestamp'],
-                    y=apr_data['apr'],
-                    mode='lines',
+                    x=x_values,
+                    y=y_values,
+                    mode='lines+markers',  # Important: use both lines and markers
+                    marker=dict(
+                        color='blue',
+                        symbol='circle',
+                        size=12,
+                        line=dict(width=2, color='black')
+                    ),
                     line=dict(color='blue', width=2),
-                    name=f'{agent_name} Line',
+                    name=agent_name,
                     legendgroup=agent_name,
-                    showlegend=False,  # Don't show duplicate legend entries
-                    hovertemplate='Time: %{x}<br>Value: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
+                    showlegend=True,
+                    hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
                 )
             )
+            logger.info(f"  Added combined line+markers trace for {agent_name}")
     
     # Update layout - use simple boolean values everywhere
     fig.update_layout(
@@ -672,6 +726,9 @@ def create_combined_time_series_graph(df):
     # SIMPLIFIED APPROACH: Do a direct plot without markers for comparison
     # This creates a simple, reliable fallback plot if the advanced one fails
     try:
+        # Write detailed debug information before saving the figure
+        write_debug_info(df, fig)
+        
         # Save the figure (still useful for reference)
         graph_file = "modius_apr_combined_graph.html"
         fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)