Peyton DeNiro
9e7e474b05
Split the previous monolithic implementation into focused modules for CLI handling, workflow loading, runtime bootstrap, load ordering, planning, rendering, and file I/O. Keep the public compatibility surface intact by preserving the legacy top-level wrapper while adding as the documented entrypoint. This reduces cross-cutting coupling inside the exporter and makes the runtime/bootstrap behavior easier to evolve without carrying unrelated concerns in one file.
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
import copy
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from typing import TextIO
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from .generator.planner import WorkflowPlanner
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from .generator.render import WorkflowRenderer
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from .io import write_python_output
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from .load_order import LoadOrderDeterminer
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from .workflow_loader import load_frontend_workflow_data, load_workflow_data
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class ExportApplication:
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"""High-level exporter orchestration."""
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def __init__(
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self,
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workflow: str = "",
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frontend_workflow: str | dict | None = None,
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input_file: str = "",
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output_file: str | TextIO = "",
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queue_size: int = 1,
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node_class_mappings: dict | None = None,
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needs_init_custom_nodes: bool = False,
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node_mapping_loader=None,
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custom_node_importer=None,
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):
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if input_file and workflow:
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raise ValueError("Can't provide both input_file and workflow")
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if not input_file and not workflow:
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raise ValueError("Needs input_file or workflow")
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if not output_file:
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raise ValueError("Needs output_file")
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self.workflow = workflow
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self.frontend_workflow = frontend_workflow
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self.input_file = input_file
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self.output_file = output_file
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self.queue_size = queue_size
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self.node_mapping_loader = node_mapping_loader
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self.custom_node_importer = custom_node_importer
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self.node_class_mappings = (
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node_class_mappings
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if node_class_mappings is not None
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else self.node_mapping_loader()
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)
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self.needs_init_custom_nodes = needs_init_custom_nodes
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self.base_node_class_mappings = copy.deepcopy(self.node_class_mappings)
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def execute(self) -> None:
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data = load_workflow_data(self.workflow, self.input_file)
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metadata_workflow_data = load_frontend_workflow_data(self.frontend_workflow)
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missing_node_types = {
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node_data["class_type"]
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for node_data in data.values()
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if node_data["class_type"] not in self.node_class_mappings
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}
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if self.needs_init_custom_nodes or missing_node_types:
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self.custom_node_importer()
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self.base_node_class_mappings = copy.deepcopy(self.node_class_mappings)
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load_order = LoadOrderDeterminer(
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data, self.node_class_mappings
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).determine_load_order()
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plan = WorkflowPlanner(
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self.node_class_mappings, self.base_node_class_mappings
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).build_plan(
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load_order,
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data,
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metadata_workflow_data,
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queue_size=self.queue_size,
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)
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generated_code = WorkflowRenderer().render(plan)
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write_python_output(self.output_file, generated_code)
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print(f"Code successfully generated and written to {self.output_file}")
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