timestamp issue

python_code_executor_service.py

@@ -217,31 +217,65 @@ class PythonExecutor:
             raise Exception(f"Failed to save plot: {e}")
     
     def _format_result(self, result: Any) -> str:
-        """Format the result for display"""
-        if isinstance(result, pd.DataFrame):
-            # Limit DataFrame display to avoid huge outputs
-            if len(result) > 100:
-                limited_result = result.head(100)
-                return f"DataFrame (first 100 of {len(result)} rows):\n{limited_result.to_string()}"
-            return result.to_string()
-        elif isinstance(result, pd.Series):
-            if len(result) > 100:
-                limited_result = result.head(100)
-                return f"Series (first 100 of {len(result)} values):\n{limited_result.to_string()}"
-            return result.to_string()
-        elif isinstance(result, (dict, list)):
+     """Format the result for display"""
+     if isinstance(result, pd.DataFrame):
+         # Limit DataFrame display to avoid huge outputs
+         if len(result) > 100:
+             limited_result = result.head(100)
+             return f"DataFrame (first 100 of {len(result)} rows):\n{limited_result.to_string()}"
+         return result.to_string()
+     elif isinstance(result, pd.Series):
+        if len(result) > 100:
+            limited_result = result.head(100)
+            return f"Series (first 100 of {len(result)} values):\n{limited_result.to_string()}"
+        return result.to_string()
+     elif isinstance(result, (dict, list)):
+        # Custom JSON encoder to handle special types
+        def json_serializer(obj):
+            """Handle special types that aren't JSON serializable"""
+            if isinstance(obj, (pd.Timestamp, datetime)):
+                return obj.isoformat()
+            elif isinstance(obj, (np.integer, np.int64, np.int32)):
+                return int(obj)
+            elif isinstance(obj, (np.floating, np.float64, np.float32)):
+                return float(obj)
+            elif isinstance(obj, np.ndarray):
+                return obj.tolist()
+            elif isinstance(obj, pd.Series):
+                return obj.to_dict()
+            elif isinstance(obj, pd.DataFrame):
+                return obj.to_dict('records')
+            elif hasattr(obj, '__dict__'):
+                return str(obj)
+            else:
+                return str(obj)
+        
+        try:
             # Limit large collections
             if isinstance(result, dict) and len(result) > 50:
                 limited = {k: result[k] for k in list(result.keys())[:50]}
-                return json.dumps(limited, indent=2) + f"\n... (showing first 50 of {len(result)} items)"
+                return json.dumps(limited, indent=2, default=json_serializer) + f"\n... (showing first 50 of {len(result)} items)"
             elif isinstance(result, list) and len(result) > 50:
                 limited = result[:50]
-                return json.dumps(limited, indent=2) + f"\n... (showing first 50 of {len(result)} items)"
-            return json.dumps(result, indent=2, default=str)
-        elif hasattr(result, '__str__'):
-            return str(result)
-        else:
-            return repr(result)
+                return json.dumps(limited, indent=2, default=json_serializer) + f"\n... (showing first 50 of {len(result)} items)"
+            return json.dumps(result, indent=2, default=json_serializer)
+        except Exception as e:
+            # Fallback to string representation if JSON serialization fails
+            return f"Result (JSON serialization failed: {str(e)}):\n{str(result)}"
+     elif isinstance(result, (pd.Timestamp, datetime)):
+        return result.isoformat()
+     elif isinstance(result, (np.integer, np.int64, np.int32)):
+        return str(int(result))
+     elif isinstance(result, (np.floating, np.float64, np.float32)):
+        return str(float(result))
+     elif isinstance(result, np.ndarray):
+        if result.size > 100:
+            return f"Array (first 100 of {result.size} elements):\n{result.flatten()[:100]}"
+        return str(result)
+     elif hasattr(result, '__str__'):
+        return str(result)
+     else:
+        return repr(result)
     
     def _get_result_variables(self, result_var: Union[str, List[str]]) -> Dict[str, Any]:
         """Get result variables from execution locals"""