89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
"""
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Runtime config endpoint for the detection pipeline.
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GET /detect/config — read current config
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PUT /detect/config — update config (takes effect on next run)
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GET /detect/config/stages — list stage palette with config fields
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"""
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from __future__ import annotations
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import logging
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from fastapi import APIRouter
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from pydantic import BaseModel
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/detect", tags=["detect"])
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# In-memory config — persists until server restart.
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# Phase 12+ moves this to DB.
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_runtime_config: dict = {}
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class ConfigUpdate(BaseModel):
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detection: dict | None = None
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ocr: dict | None = None
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resolver: dict | None = None
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escalation: dict | None = None
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preprocessing: dict | None = None
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class StageConfigInfo(BaseModel):
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name: str
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label: str
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description: str
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category: str
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config_fields: list[dict]
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reads: list[str]
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writes: list[str]
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@router.get("/config")
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def read_config():
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return _runtime_config
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@router.put("/config")
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def write_config(update: ConfigUpdate):
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changes = update.model_dump(exclude_none=True)
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for section, values in changes.items():
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if section not in _runtime_config:
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_runtime_config[section] = {}
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_runtime_config[section].update(values)
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logger.info("Config updated: %s", list(changes.keys()))
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return _runtime_config
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@router.get("/config/stages", response_model=list[StageConfigInfo])
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def list_stage_configs():
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"""Return the stage palette with config field metadata for the editor."""
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from detect.stages import list_stages
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result = []
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for stage in list_stages():
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info = StageConfigInfo(
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name=stage.name,
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label=stage.label,
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description=stage.description,
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category=stage.category,
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config_fields=[
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{
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"name": f.name,
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"type": f.type,
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"default": f.default,
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"description": f.description,
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"min": f.min,
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"max": f.max,
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"options": f.options,
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}
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for f in stage.config_fields
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],
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reads=stage.io.reads,
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writes=stage.io.writes,
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)
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result.append(info)
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return result
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