do not make providing both flows mandatory

src/depiction_targeted_preproc/app_interface/dispatch_individual_resources.py

@@ -1,11 +1,11 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Any
+from typing import Any, Self
 
 import yaml
 from loguru import logger
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, model_validator
 
 from bfabric import Bfabric
 from bfabric.entities import Resource, Dataset
@@ -24,11 +24,17 @@ class ConfigDatasetFlow(BaseModel):
 
 
 class ConfigDispatchIndividualResources(BaseModel):
-    resource_flow: ConfigResourceFlow
-    dataset_flow: ConfigDatasetFlow
+    resource_flow: ConfigResourceFlow | None
+    dataset_flow: ConfigDatasetFlow | None
 
+    @model_validator(mode="after")
+    def check_at_least_one_flow(self) -> Self:
+        if self.resource_flow is None and self.dataset_flow is None:
+            raise ValueError("either resource_flow or dataset_flow must be provided")
+        return self
 
-def config_msi_imzml():
+
+def config_msi_imzml() -> ConfigDispatchIndividualResources:
     return ConfigDispatchIndividualResources(
         resource_flow=ConfigResourceFlow(filter_suffix=".imzML"),
         dataset_flow=ConfigDatasetFlow(resource_column="Imzml", param_columns=[("PanelDataset", "mass_list_id")]),
@@ -69,6 +75,8 @@ def _write_chunks(self, chunks: list[Path]) -> None:
             yaml.safe_dump(data, f)
 
     def _dispatch_jobs_resource_flow(self, definition: WorkunitDefinition, params: dict[str, Any]) -> list[Path]:
+        if self._config.resource_flow is None:
+            raise ValueError("resource_flow is not configured")
         resources = Resource.find_all(ids=definition.execution.resources, client=self._client)
         paths = []
         for resource in sorted(resources.values()):
@@ -81,6 +89,8 @@ def _dispatch_jobs_resource_flow(self, definition: WorkunitDefinition, params: d
         return paths
 
     def _dispatch_jobs_dataset_flow(self, definition: WorkunitDefinition, params: dict[str, Any]) -> list[Path]:
+        if self._config.dataset_flow is None:
+            raise ValueError("dataset_flow is not configured")
         dataset = Dataset.find(id=definition.execution.dataset, client=self._client)
         dataset_df = dataset.to_polars()
         resources = Resource.find_all(