server-26/drb-c2-core/app/internal/summarizer.py

"""
Background incident summary loop.

Runs every SUMMARY_INTERVAL_MINUTES. Two passes per tick:
  1. Summary pass  — find stale incidents (summary_stale=True) and regenerate summaries.
  2. Stale sweep   — auto-resolve incidents with no new calls for incident_auto_resolve_minutes.
     This is effectively "time since last call" because updated_at is stamped on every
     new linked call.
"""
import asyncio
from datetime import datetime, timezone, timedelta
from typing import Optional
from app.internal.logger import logger
from app.internal import firestore as fstore
from app.config import settings


def _scene_sort_key(scene_index: str):
    """Numeric-first sort so a >=10-scene call's entries still read in order."""
    return (0, int(scene_index)) if scene_index.isdigit() else (1, scene_index)


def _scene_text_for_incident(doc: dict, incident_id: str) -> Optional[str]:
    """
    The text of `doc` (a call doc) that actually belongs to `incident_id`.

    server-26#96 records, per scene, which incident_id that scene's
    correlation decision resolved to (incident_correlator._apply_and_log's
    `scenes.<index>.incident_id`). Use that to pick only the scene(s) of this
    call that are genuinely part of this incident, joining more than one if
    several scenes happened to link into the same incident.

    Falls back to transcript_corrected-or-transcript when the call doc has no
    `scenes` field (predates server-26#96) or — defensively — when it has one
    but nothing in it names this incident_id (should not happen for a call_id
    that's actually in this incident's call_ids, but silently dropping a
    call's contribution to its own summary would be a worse failure mode than
    falling back to the whole-call text).
    """
    scenes = doc.get("scenes") or {}
    matched = [
        scene.get("transcript")
        for _, scene in sorted(scenes.items(), key=lambda kv: _scene_sort_key(kv[0]))
        if scene.get("incident_id") == incident_id and scene.get("transcript")
    ]
    if matched:
        return "\n".join(matched)
    return doc.get("transcript_corrected") or doc.get("transcript")


async def summarizer_loop() -> None:
    from app.internal.feature_flags import get_flags
    interval = settings.summary_interval_minutes * 60
    logger.info(f"Summarizer started — interval: {settings.summary_interval_minutes}m")
    while True:
        await asyncio.sleep(interval)
        try:
            flags = await get_flags()
            if flags["summaries_enabled"]:
                await _run_summary_pass()
            else:
                logger.info("Summaries disabled — skipping summary pass")
            # Deliberately outside the flag. Auto-resolving a quiet incident is
            # pure Firestore with no model call in it, and gating it behind the
            # AI kill switch meant nothing ever auto-resolved in the standing
            # flags-off configuration — leaving every incident "active" forever
            # and growing the candidate set every correlation reads.
            await _resolve_stale_incidents()
        except Exception as e:
            logger.error(f"Summarizer pass failed: {e}")


async def _run_summary_pass() -> None:
    stale = await fstore.collection_list("incidents", status="active", summary_stale=True)
    if not stale:
        return

    logger.info(f"Summarizer: processing {len(stale)} stale incident(s)")
    for inc in stale:
        await _summarize_incident(inc)


async def _summarize_incident(inc: dict) -> None:
    from app.internal.feature_flags import get_flags

    incident_id = inc.get("incident_id")
    if not incident_id:
        return

    flags = await get_flags()
    if not flags["summaries_enabled"]:
        logger.info(f"Summaries disabled — skipping summary for incident {incident_id}")
        return

    call_ids: list[str] = inc.get("call_ids", [])
    if not call_ids:
        return

    # Fetch transcripts for all calls in this incident.
    #
    # server-26#114: a call links into an incident one SCENE at a time (see
    # incident_correlator._apply_decision / server-26#96's `scenes` map on the
    # call doc), and the same call_id can appear in more than one incident's
    # call_ids — once per scene, each scene possibly landing in a different
    # incident. Reading doc["transcript"] (the whole call, raw) meant an
    # incident's summary was built partly on text from a DIFFERENT scene of
    # that call that this incident has nothing to do with, and ignored
    # transcript_corrected entirely.
    #
    # _scene_text_for_incident reads the specific scene(s) whose corr_debug
    # recorded a link into THIS incident_id. For a call doc that predates
    # this fix (no `scenes` field) it falls back to
    # transcript_corrected-or-transcript — the one-liner half of #114, worth
    # doing even for old-schema docs since it stops raw-transcript summaries.
    transcripts: list[str] = []
    for cid in call_ids:
        doc = await fstore.doc_get("calls", cid)
        if not doc:
            continue
        text = _scene_text_for_incident(doc, incident_id)
        if text:
            transcripts.append(text)

    if not transcripts:
        # No transcripts yet — clear stale flag and wait for next pass
        await fstore.doc_set("incidents", incident_id, {"summary_stale": False})
        return

    summary = await asyncio.to_thread(_sync_summarize, inc, transcripts)

    now = datetime.now(timezone.utc).isoformat()
    updates: dict = {
        "summary_stale": False,
        "summary_last_run": now,
    }
    if summary:
        updates["summary"] = summary
        logger.info(f"Summarizer: updated summary for incident {incident_id}")
    else:
        logger.warning(f"Summarizer: Gemini returned nothing for incident {incident_id}")

    await fstore.doc_set("incidents", incident_id, updates)


async def _resolve_stale_incidents() -> None:
    """Auto-resolve active incidents that have had no new calls for incident_auto_resolve_minutes."""
    all_active = await fstore.collection_list("incidents", status="active")
    if not all_active:
        return

    now     = datetime.now(timezone.utc)
    cutoff  = timedelta(minutes=settings.incident_auto_resolve_minutes)
    count   = 0

    for inc in all_active:
        incident_id = inc.get("incident_id")
        if not incident_id:
            continue
        try:
            updated_dt = datetime.fromisoformat(
                str(inc.get("updated_at", "")).replace("Z", "+00:00")
            )
            if updated_dt.tzinfo is None:
                updated_dt = updated_dt.replace(tzinfo=timezone.utc)
            idle_minutes = (now - updated_dt).total_seconds() / 60
            if idle_minutes > settings.incident_auto_resolve_minutes:
                await fstore.doc_set("incidents", incident_id, {
                    "status": "resolved",
                    "resolved_at": now.isoformat(),
                })
                from app.internal.incident_correlator import maybe_resolve_parent
                await maybe_resolve_parent(incident_id)
                logger.info(
                    f"Auto-resolved stale incident {incident_id} "
                    f"(idle {idle_minutes:.0f}m)"
                )
                count += 1
        except Exception as e:
            logger.warning(f"Stale sweep error for {incident_id}: {e}")

    if count:
        logger.info(f"Stale sweep: resolved {count} incident(s)")


def _sync_summarize(inc: dict, transcripts: list[str]) -> Optional[str]:
    from app.config import settings
    from openai import OpenAI

    if not settings.openai_api_key:
        return None

    inc_type = inc.get("type", "unknown")
    location = inc.get("location") or "unknown location"
    tg_ids = ", ".join(inc.get("talkgroup_ids", [])) or "unknown"
    numbered = "\n".join(f"{i+1}. {t}" for i, t in enumerate(transcripts))

    prompt = f"""You are analyzing P25 public safety radio communications for a single active incident.

Incident type: {inc_type}
Location: {location}
Talkgroup(s): {tg_ids}

Transcripts ({len(transcripts)} calls, chronological):
{numbered}

Write a concise factual summary of this incident in 2-4 sentences. Include:
- What happened
- Location (most specific mentioned)
- Units or resources involved if mentioned
- Current status if determinable

Be factual. Do not speculate beyond what the transcripts say. Do not use bullet points."""

    try:
        client = OpenAI(api_key=settings.openai_api_key)
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
        )
        return response.choices[0].message.content.strip() or None
    except Exception as e:
        logger.warning(f"GPT-4o mini summary failed: {e}")
        return None