used f strings and fixed some bugs

src/sempy_labs/_generate_semantic_model.py

@@ -126,7 +126,7 @@ def create_semantic_model_from_bim(
         lro_wait=True,
     )
 
-    if response.status_code != 200:
+    if response.status_code not in [200, 201]:
         raise FabricHTTPException(response)
     print(
         f"{icons.green_dot} The '{dataset}' semantic model has been created within the '{workspace}' workspace."

src/sempy_labs/_helper_functions.py

@@ -286,26 +286,34 @@ def get_direct_lake_sql_endpoint(dataset: str, workspace: Optional[str] = None)
         The ID of SQL Endpoint.
     """
 
+    from sempy_labs.tom import connect_semantic_model
+
     if workspace is None:
         workspace_id = fabric.get_workspace_id()
         workspace = fabric.resolve_workspace_name(workspace_id)
 
-    dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
-    dfP_filt = dfP[dfP["Mode"] == "DirectLake"]
+    # dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
+    # dfP_filt = dfP[dfP["Mode"] == "DirectLake"]
 
-    if len(dfP_filt) == 0:
-        raise ValueError(
-            f"The '{dataset}' semantic model in the '{workspace}' workspace is not in Direct Lake mode."
-        )
+    # if len(dfP_filt) == 0:
+    #    raise ValueError(
+    #        f"The '{dataset}' semantic model in the '{workspace}' workspace is not in Direct Lake mode."
+    #    )
 
-    dfE = fabric.list_expressions(dataset=dataset, workspace=workspace)
-    dfE_filt = dfE[dfE["Name"] == "DatabaseQuery"]
-    expr = dfE_filt["Expression"].iloc[0]
+    with connect_semantic_model(
+        dataset=dataset, readonly=True, workspace=workspace
+    ) as tom:
+        sqlEndpointId = None
+        for e in tom.model.Expressions:
+            if e.Name == "DatabaseQuery":
+                expr = e.Expression
+                matches = re.findall(r'"([^"]*)"', expr)
+                sqlEndpointId = matches[1]
 
-    matches = re.findall(r'"([^"]*)"', expr)
-    sqlEndpointId = matches[1]
+        if sqlEndpointId is None:
+            raise ValueError("SQL Endpoint not found.")
 
-    return sqlEndpointId
+        return sqlEndpointId
 
 
 def generate_embedded_filter(filter: str):

src/sempy_labs/_model_dependencies.py

@@ -321,9 +321,7 @@ def measure_dependency_tree(
         child_node_name = ref_obj_name
         child_node = Node(child_node_name, parent=parent_node)
         if ref_obj_type == "Column":
-            child_node.custom_property = (
-                f"{icons.column_icon} '{ref_obj_table_name}'"
-            )
+            child_node.custom_property = f"{icons.column_icon} '{ref_obj_table_name}'"
         elif ref_obj_type == "Table":
             child_node.custom_property = f"{icons.table_icon} "
         elif ref_obj_type == "Measure":

src/sempy_labs/directlake/_get_directlake_lakehouse.py

@@ -6,7 +6,6 @@
 )
 from typing import Optional, Tuple
 from uuid import UUID
-import sempy_labs._icons as icons
 
 
 def get_direct_lake_lakehouse(
@@ -49,13 +48,13 @@ def get_direct_lake_lakehouse(
         lakehouse_id = fabric.get_lakehouse_id()
         lakehouse = resolve_lakehouse_name(lakehouse_id, lakehouse_workspace)
 
-    dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
-    dfP_filt = dfP[dfP["Mode"] == "DirectLake"]
+    # dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
+    # dfP_filt = dfP[dfP["Mode"] == "DirectLake"]
 
-    if len(dfP_filt) == 0:
-        raise ValueError(
-            f"{icons.red_dot} The '{dataset}' semantic model within the '{workspace}' workspace is not in Direct Lake mode."
-        )
+    # if len(dfP_filt) == 0:
+    #    raise ValueError(
+    #        f"{icons.red_dot} The '{dataset}' semantic model within the '{workspace}' workspace is not in Direct Lake mode."
+    #    )
 
     sqlEndpointId = get_direct_lake_sql_endpoint(dataset, workspace)
 

src/sempy_labs/directlake/_update_directlake_partition_entity.py

@@ -93,7 +93,8 @@ def add_table_to_direct_lake_semantic_model(
     dataset: str,
     table_name: str,
     lakehouse_table_name: str,
-    workspace: Optional[str | None] = None,
+    refresh: Optional[bool] = True,
+    workspace: Optional[str] = None,
 ):
     """
     Adds a table and all of its columns to a Direct Lake semantic model, based on a Fabric lakehouse table.
@@ -106,6 +107,8 @@ def add_table_to_direct_lake_semantic_model(
         Name of the table in the semantic model.
     lakehouse_table_name : str
         The name of the Fabric lakehouse table.
+    refresh : bool, default=True
+        Refreshes the table after it is added to the semantic model.
     workspace : str, default=None
         The name of the Fabric workspace in which the semantic model resides.
         Defaults to None which resolves to the workspace of the attached lakehouse
@@ -128,9 +131,11 @@ def add_table_to_direct_lake_semantic_model(
         dataset=dataset, readonly=False, workspace=workspace
     ) as tom:
 
-        if tom.is_direct_lake() is False:
+        table_count = tom.model.Tables.Count
+
+        if tom.is_direct_lake() is False and table_count > 0:
             raise ValueError(
-                "This function is only valid for Direct Lake semantic models."
+                "This function is only valid for Direct Lake semantic models or semantic models with no tables."
             )
 
         if any(
@@ -194,4 +199,7 @@ def add_table_to_direct_lake_semantic_model(
                 f"{icons.green_dot} The '{lakeCName}' column has been added to the '{table_name}' table as a '{dt}' data type in the '{dataset}' semantic model within the '{workspace}' workspace."
             )
 
-        refresh_semantic_model(dataset=dataset, tables=table_name, workspace=workspace)
+        if refresh:
+            refresh_semantic_model(
+                dataset=dataset, tables=table_name, workspace=workspace
+            )

src/sempy_labs/report/_generate_report.py

@@ -106,7 +106,7 @@ def create_report_from_reportjson(
         f"/v1/workspaces/{workspace_id}/reports", json=request_body, lro_wait=True
     )
 
-    if response.status_code != 200:
+    if response.status_code not in [200, 201]:
         raise FabricHTTPException(response)
     print(
         f"{icons.green_dot} Succesfully created the '{report}' report within the '{workspace}' workspace."

src/sempy_labs/report/_report_functions.py

@@ -39,7 +39,7 @@ def get_report_json(
     report : str
         Name of the Power BI report.
     workspace : str, default=None
-        The Fabric workspace name.
+        The Fabric workspace name in which the report exists.
         Defaults to None which resolves to the workspace of the attached lakehouse
         or if no lakehouse attached, resolves to the workspace of the notebook.
     save_to_file_name : str, default=None
@@ -51,6 +51,8 @@ def get_report_json(
         The report.json file for a given Power BI report.
     """
 
+    from notebookutils import mssparkutils
+
     (workspace, workspace_id) = resolve_workspace_name_and_id(workspace)
 
     client = fabric.FabricRestClient()
@@ -82,7 +84,14 @@ def get_report_json(
             )
 
         lakehouse_id = fabric.get_lakehouse_id()
-        lakehouse = resolve_lakehouse_name(lakehouse_id, workspace)
+        for mp in mssparkutils.fs.mounts():
+            if mp.mountPoint == "/default" and mp.storageType == "Lakehouse":
+                ind = mp.source.index("@")
+                lakehouse_workspace_id = mp.source[8:ind]
+                lakehouse_workspace = fabric.resolve_workspace_name(
+                    lakehouse_workspace_id
+                )
+        lakehouse = resolve_lakehouse_name(lakehouse_id, lakehouse_workspace)
         folderPath = "/lakehouse/default/Files"
         fileExt = ".json"
         if not save_to_file_name.endswith(fileExt):