feat: implement logging for error handling in APR data processing

apr_visualization.py

@@ -11,6 +11,10 @@ import requests
 import sys
 import json
 from typing import List, Dict, Any
+import logging
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
 
 # Global variable to store the data for reuse
 global_df = None
@@ -22,7 +26,7 @@ def get_agent_type_by_name(type_name: str) -> Dict[str, Any]:
     """Get agent type by name"""
     response = requests.get(f"{API_BASE_URL}/api/agent-types/name/{type_name}")
     if response.status_code == 404:
-        print(f"Error: Agent type '{type_name}' not found")
+        logger.error(f"Agent type '{type_name}' not found")
         return None
     response.raise_for_status()
     return response.json()
@@ -31,7 +35,7 @@ def get_attribute_definition_by_name(attr_name: str) -> Dict[str, Any]:
     """Get attribute definition by name"""
     response = requests.get(f"{API_BASE_URL}/api/attributes/name/{attr_name}")
     if response.status_code == 404:
-        print(f"Error: Attribute definition '{attr_name}' not found")
+        logger.error(f"Attribute definition '{attr_name}' not found")
         return None
     response.raise_for_status()
     return response.json()
@@ -40,7 +44,7 @@ def get_agents_by_type(type_id: int) -> List[Dict[str, Any]]:
     """Get all agents of a specific type"""
     response = requests.get(f"{API_BASE_URL}/api/agent-types/{type_id}/agents/")
     if response.status_code == 404:
-        print(f"No agents found for type ID {type_id}")
+        logger.error(f"No agents found for type ID {type_id}")
         return []
     response.raise_for_status()
     return response.json()
@@ -56,7 +60,7 @@ def get_attribute_values_by_type_and_attr(agents: List[Dict[str, Any]], attr_def
         # Call the /api/agents/{agent_id}/attributes/ endpoint
         response = requests.get(f"{API_BASE_URL}/api/agents/{agent_id}/attributes/", params={"limit": 1000})
         if response.status_code == 404:
-            print(f"No attributes found for agent ID {agent_id}")
+            logger.error(f"No attributes found for agent ID {agent_id}")
             continue
         
         try:
@@ -67,7 +71,7 @@ def get_attribute_values_by_type_and_attr(agents: List[Dict[str, Any]], attr_def
             filtered_attrs = [attr for attr in agent_attrs if attr.get("attr_def_id") == attr_def_id]
             all_attributes.extend(filtered_attrs)
         except requests.exceptions.RequestException as e:
-            print(f"Error fetching attributes for agent ID {agent_id}: {e}")
+            logger.error(f"Error fetching attributes for agent ID {agent_id}: {e}")
     
     return all_attributes
 
@@ -101,7 +105,7 @@ def extract_apr_value(attr: Dict[str, Any]) -> Dict[str, Any]:
             
         return {"apr": apr, "timestamp": timestamp_dt, "agent_id": attr["agent_id"], "is_dummy": False}
     except (json.JSONDecodeError, KeyError, TypeError) as e:
-        print(f"Error parsing JSON value: {e}")
+        logger.error(f"Error parsing JSON value: {e} for agent_id: {attr.get('agent_id')}")
         return {"apr": None, "timestamp": None, "agent_id": attr["agent_id"], "is_dummy": False}
 
 def fetch_apr_data_from_db():
@@ -114,7 +118,7 @@ def fetch_apr_data_from_db():
         # Step 1: Find the Modius agent type
         modius_type = get_agent_type_by_name("Modius")
         if not modius_type:
-            print("Modius agent type not found, using placeholder data")
+            logger.error("Modius agent type not found, using placeholder data")
             global_df = pd.DataFrame([])
             return global_df
         
@@ -123,7 +127,7 @@ def fetch_apr_data_from_db():
         # Step 2: Find the APR attribute definition
         apr_attr_def = get_attribute_definition_by_name("APR")
         if not apr_attr_def:
-            print("APR attribute definition not found, using placeholder data")
+            logger.error("APR attribute definition not found, using placeholder data")
             global_df = pd.DataFrame([])
             return global_df
             
@@ -132,14 +136,14 @@ def fetch_apr_data_from_db():
         # Step 3: Get all agents of type Modius
         modius_agents = get_agents_by_type(type_id)
         if not modius_agents:
-            print("No agents of type 'Modius' found")
+            logger.error("No agents of type 'Modius' found")
             global_df = pd.DataFrame([])
             return global_df
         
         # Step 4: Fetch all APR values for Modius agents
         apr_attributes = get_attribute_values_by_type_and_attr(modius_agents, attr_def_id)
         if not apr_attributes:
-            print("No APR values found for 'Modius' agents")
+            logger.error("No APR values found for 'Modius' agents")
             global_df = pd.DataFrame([])
             return global_df
         
@@ -165,7 +169,7 @@ def fetch_apr_data_from_db():
         
         # Convert list of dictionaries to DataFrame
         if not apr_data_list:
-            print("No valid APR data extracted")
+            logger.error("No valid APR data extracted")
             global_df = pd.DataFrame([])
             return global_df
         
@@ -173,11 +177,11 @@ def fetch_apr_data_from_db():
         return global_df
         
     except requests.exceptions.RequestException as e:
-        print(f"API request error: {e}")
+        logger.error(f"API request error: {e}")
         global_df = pd.DataFrame([])
         return global_df
     except Exception as e:
-        print(f"Error fetching APR data: {e}")
+        logger.error(f"Error fetching APR data: {e}")
         global_df = pd.DataFrame([])
         return global_df
 
@@ -190,7 +194,7 @@ def generate_apr_visualizations():
     
     # If we got no data at all, return placeholder figures
     if df.empty:
-        print("No APR data available. Using fallback visualization.")
+        logger.info("No APR data available. Using fallback visualization.")
         # Create empty visualizations with a message using Plotly
         fig = go.Figure()
         fig.add_annotation(
@@ -234,7 +238,7 @@ def create_time_series_graph_per_agent(df):
     unique_agents = df['agent_id'].unique()
     
     if len(unique_agents) == 0:
-        print("No agent data to plot")
+        logger.error("No agent data to plot")
         fig = go.Figure()
         fig.add_annotation(
             text="No agent data available",
@@ -357,7 +361,7 @@ def create_time_series_graph_per_agent(df):
     img_file = "modius_apr_per_agent_graph.png"
     fig.write_image(img_file)
     
-    print(f"Per-agent graph saved to {graph_file} and {img_file}")
+    logger.info(f"Per-agent graph saved to {graph_file} and {img_file}")
     
     # Return the figure object for direct use in Gradio
     return fig
@@ -365,7 +369,7 @@ def create_time_series_graph_per_agent(df):
 def create_combined_time_series_graph(df):
     """Create a combined time series graph for all agents using Plotly"""
     if len(df) == 0:
-        print("No data to plot combined graph")
+        logger.error("No data to plot combined graph")
         fig = go.Figure()
         fig.add_annotation(
             text="No data available",
@@ -501,7 +505,7 @@ def create_combined_time_series_graph(df):
     img_file = "modius_apr_combined_graph.png"
     fig.write_image(img_file)
     
-    print(f"Combined graph saved to {graph_file} and {img_file}")
+    logger.info(f"Combined graph saved to {graph_file} and {img_file}")
     
     # Return the figure object for direct use in Gradio
     return fig
@@ -509,7 +513,7 @@ def create_combined_time_series_graph(df):
 def save_to_csv(df):
     """Save the APR data DataFrame to a CSV file and return the file path"""
     if df.empty:
-        print("No APR data to save to CSV")
+        logger.error("No APR data to save to CSV")
         return None
     
     # Define the CSV file path
@@ -517,13 +521,13 @@ def save_to_csv(df):
     
     # Save to CSV
     df.to_csv(csv_file, index=False)
-    print(f"APR data saved to {csv_file}")
+    logger.info(f"APR data saved to {csv_file}")
     
     # Also generate a statistics CSV file
     stats_df = generate_statistics_from_data(df)
     stats_csv = "modius_apr_statistics.csv"
     stats_df.to_csv(stats_csv, index=False)
-    print(f"Statistics saved to {stats_csv}")
+    logger.info(f"Statistics saved to {stats_csv}")
     
     return csv_file
 
@@ -585,4 +589,4 @@ def generate_statistics_from_data(df):
     }
     stats_list.append(overall_stats)
     
-    return pd.DataFrame(stats_list) 
+    return pd.DataFrame(stats_list)