use labelbox export_v2 instead of export

deeplake/integrations/labelbox/labelbox_utils.py

@@ -167,7 +167,6 @@ def validate_project_creation_data_(proj, project_id, type):
 
 
 def labelbox_get_project_json_with_id_(client, project_id, fail_on_error=False):
-    print("requesting project info from labelbox with id", project_id)
     # Set the export params to include/exclude certain fields.
     export_params = {
         "attachments": False,
@@ -180,49 +179,25 @@ def labelbox_get_project_json_with_id_(client, project_id, fail_on_error=False):
         "embeddings": False,
     }
 
-    # Note: Filters follow AND logic, so typically using one filter is sufficient.
-    filters = {
-        "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
-        "label_created_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
-    }
-
     project = client.get_project(project_id)
-    export_task = project.export(params=export_params, filters=filters)
-
+    export_task = project.export_v2(params=export_params)
+
+    print(
+        "requesting project info from labelbox with id",
+        project_id,
+        "export task id",
+        export_task.uid,
+    )
     export_task.wait_till_done()
 
-    # Provide results with JSON converter
-    # Returns streamed JSON output strings from export task results/errors, one by one
-
-    projects = []
-
-    # Callback used for JSON Converter
-    def json_stream_handler(output: lb.BufferedJsonConverterOutput):
-        projects.append(output.json)
-
-    def error_stream_handler(error):
+    if export_task.errors:
         if fail_on_error:
-            raise Exception(f"Error during export: {error}")
-        print(f"Error during export: {error}")
-
-    try:
-        if export_task.has_errors():
-            export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(
-                stream_handler=error_stream_handler
-            )
-    except Exception as e:
-        if fail_on_error:
-            raise e
-        print(f"Error during export: {e}")
-
-    if export_task.has_result():
-        export_json = export_task.get_buffered_stream(
-            stream_type=lb.StreamType.RESULT
-        ).start(stream_handler=json_stream_handler)
+            raise ValueError("Labelbox export task failed with errors")
+        print("Labelbox export task failed with errors:", export_task.errors)
 
     print("project info is ready for project with id", project_id)
 
-    return projects
+    return export_task.result
 
 
 def create_tensors_default_(ds):