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128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
from fastapi import APIRouter, BackgroundTasks, HTTPException, Query, Depends
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from pydantic import BaseModel
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from typing import Optional
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from app.internal import firestore as fstore
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from app.internal.auth import require_admin_token
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class TranscriptUpdate(BaseModel):
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transcript: str
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router = APIRouter(prefix="/calls", tags=["calls"])
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@router.get("")
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async def list_calls(
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node_id: Optional[str] = Query(None),
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status: Optional[str] = Query(None),
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system_id: Optional[str] = Query(None),
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):
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filters = {}
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if node_id:
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filters["node_id"] = node_id
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if status:
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filters["status"] = status
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if system_id:
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filters["system_id"] = system_id
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return await fstore.collection_list("calls", **filters)
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@router.get("/{call_id}")
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async def get_call(call_id: str):
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call = await fstore.doc_get("calls", call_id)
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if not call:
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raise HTTPException(404, f"Call '{call_id}' not found.")
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return call
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@router.post("/{call_id}/reprocess")
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async def reprocess_call(call_id: str, background_tasks: BackgroundTasks):
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"""Re-run the full intelligence pipeline (transcription → extraction → correlation) for a call."""
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call = await fstore.doc_get("calls", call_id)
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if not call:
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raise HTTPException(404, f"Call '{call_id}' not found.")
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from app.routers.upload import _run_intelligence_pipeline, _public_url_to_gcs_uri
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audio_url = call.get("audio_url")
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gcs_uri = _public_url_to_gcs_uri(audio_url) if audio_url else None
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background_tasks.add_task(
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_run_intelligence_pipeline,
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call_id=call_id,
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node_id=call.get("node_id"),
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system_id=call.get("system_id"),
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talkgroup_id=call.get("talkgroup_id"),
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talkgroup_name=call.get("talkgroup_name"),
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gcs_uri=gcs_uri,
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)
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return {"ok": True, "call_id": call_id}
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@router.patch("/{call_id}/transcript")
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async def patch_transcript(
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call_id: str,
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body: TranscriptUpdate,
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background_tasks: BackgroundTasks,
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_: dict = Depends(require_admin_token),
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):
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"""Overwrite a call's transcript and re-run intelligence extraction."""
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call = await fstore.doc_get("calls", call_id)
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if not call:
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raise HTTPException(404, f"Call '{call_id}' not found.")
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# Save user correction as transcript_corrected; leave original transcript intact.
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# Clear stale intelligence fields so re-extraction runs fresh.
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await fstore.doc_set("calls", call_id, {
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"transcript_corrected": body.transcript,
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"tags": [],
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"severity": "unknown",
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"location": None,
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"units": [],
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"vehicles": [],
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"embedding": None,
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})
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# Unlink from ALL current incidents so re-correlation starts clean.
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# Handles both old single incident_id and new incident_ids list.
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old_ids: list[str] = call.get("incident_ids") or (
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[call["incident_id"]] if call.get("incident_id") else []
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)
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for old_incident_id in old_ids:
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old_incident = await fstore.doc_get("incidents", old_incident_id)
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if old_incident:
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remaining = [c for c in (old_incident.get("call_ids") or []) if c != call_id]
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if remaining:
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await fstore.doc_set("incidents", old_incident_id, {
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"call_ids": remaining,
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"summary_stale": True,
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})
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else:
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await fstore.doc_set("incidents", old_incident_id, {
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"call_ids": [],
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"status": "resolved",
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"summary_stale": True,
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})
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await fstore.doc_set("calls", call_id, {"incident_ids": [], "incident_id": None})
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# Learn from the correction: diff original → corrected and add new tokens to vocabulary
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system_id = call.get("system_id")
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original_text = call.get("transcript_corrected") or call.get("transcript") or ""
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if system_id and original_text:
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from app.internal.vocabulary_learner import learn_from_correction
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await learn_from_correction(system_id, original_text, body.transcript)
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from app.routers.upload import _run_extraction_pipeline
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background_tasks.add_task(
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_run_extraction_pipeline,
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call_id=call_id,
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node_id=call.get("node_id"),
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system_id=call.get("system_id"),
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talkgroup_id=call.get("talkgroup_id"),
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talkgroup_name=call.get("talkgroup_name"),
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transcript=body.transcript,
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segments=call.get("segments"),
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preserve_transcript_correction=True,
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)
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return {"ok": True, "call_id": call_id}
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