feat(lotto): grade_weekly_review 통합 잡 — 큐레이터 자기평가 + 패턴 갭

lotto/app/jobs/grade_weekly_review.py

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+"""주간 회고 채점 통합 잡 — 일요일 03:00 KST 실행.
+
+1) 기존 purchase_manager.check_purchases_for_draw() 로 사용자 구매 자동 채점
+2) 큐레이터 4계층 picks vs 추첨 결과 비교
+3) 패턴 요약·갭 계산
+4) weekly_review UPSERT
+5) 4등 이상 발견 시 agent-office webhook 호출
+"""
+import json
+import logging
+import os
+from typing import Optional
+
+import httpx
+
+from .. import db
+from ..purchase_manager import check_purchases_for_draw
+from .grading_helpers import (
+    score_picks_against_draw,
+    summarize_pattern,
+    aggregate_pattern_summaries,
+    compute_pattern_delta,
+)
+
+logger = logging.getLogger("lotto-backend")
+
+AGENT_OFFICE_URL = os.environ.get("AGENT_OFFICE_URL", "http://agent-office:8000")
+
+
+def _flatten_curator_picks(briefing: dict) -> list:
+    """4계층 picks 를 모두 합쳐 단일 리스트(score 계산용)."""
+    picks = briefing.get("picks") or {}
+    if isinstance(picks, list):
+        return picks
+    out = []
+    for tier in ("core", "bonus", "extended", "pool"):
+        out.extend(picks.get(tier) or [])
+    return out
+
+
+def _curator_score(briefing: dict, win_nums: list, bonus: int) -> dict:
+    if not briefing:
+        return {}
+    flat = _flatten_curator_picks(briefing)
+    if not flat:
+        return {}
+    return score_picks_against_draw(flat, win_nums, bonus)
+
+
+def _user_score(drw_no: int, win_nums: list) -> dict:
+    purchases = db.get_purchases(draw_no=drw_no)
+    if not purchases:
+        return {}
+    matches = []
+    win_set = set(win_nums)
+    pattern_summaries = []
+    for p in purchases:
+        for nums in (p.get("numbers") or []):
+            if not nums:
+                continue
+            m = len(set(nums) & win_set)
+            matches.append(m)
+            pattern_summaries.append(summarize_pattern(nums))
+    if not matches:
+        return {}
+    return {
+        "avg_match": round(sum(matches) / len(matches), 2),
+        "best_match": max(matches),
+        "five_plus_prizes": sum(1 for m in matches if m >= 3),
+        "pattern_avg": aggregate_pattern_summaries(pattern_summaries),
+    }
+
+
+def _trigger_prize_alert(drw_no: int, match_count: int, numbers: list, purchase_id: int) -> None:
+    try:
+        with httpx.Client(timeout=10) as client:
+            client.post(
+                f"{AGENT_OFFICE_URL}/api/agent-office/notify/lotto-prize",
+                json={
+                    "draw_no": drw_no,
+                    "match_count": match_count,
+                    "numbers": numbers,
+                    "purchase_id": purchase_id,
+                },
+            )
+    except Exception as e:
+        logger.warning(f"[grade_weekly_review] prize alert webhook failed: {e}")
+
+
+def run_weekly_grading(drw_no: int) -> dict:
+    """주어진 회차에 대해 채점 잡 1회 실행. 멱등."""
+    draw = db.get_draw(drw_no)
+    if not draw:
+        logger.warning(f"[grade_weekly_review] draw {drw_no} not found, skip")
+        return {"ok": False, "reason": "no draw"}
+
+    win_nums = [draw["n1"], draw["n2"], draw["n3"], draw["n4"], draw["n5"], draw["n6"]]
+    bonus = draw["bonus"]
+
+    # 1) 사용자 구매 자동 채점 (기존 인프라)
+    try:
+        check_purchases_for_draw(drw_no)
+    except Exception as e:
+        logger.warning(f"[grade_weekly_review] check_purchases_for_draw failed: {e}")
+
+    # 2) 4등 이상 발견 시 webhook
+    purchases = db.get_purchases(draw_no=drw_no, checked=True)
+    for p in purchases:
+        for r in (p.get("results") or []):
+            if r.get("correct", 0) >= 4:
+                _trigger_prize_alert(drw_no, r["correct"], r["numbers"], p["id"])
+
+    # 3) 큐레이터 자기 평가
+    briefing = db.get_briefing(drw_no)
+    cur = _curator_score(briefing, win_nums, bonus)
+
+    # 4) 사용자 평가 (재로드, 구매가 다 채점된 후 패턴 계산)
+    usr = _user_score(drw_no, win_nums)
+
+    # 5) 추첨 패턴 요약 + 델타
+    draw_summary = summarize_pattern(win_nums)
+    draw_pattern = {
+        "low_avg": draw_summary["low_count"],
+        "odd_avg": draw_summary["odd_count"],
+        "sum_avg": draw_summary["sum"],
+    }
+    user_pattern = usr.get("pattern_avg", {})
+    delta = compute_pattern_delta(user_pattern, draw_pattern) if user_pattern else ""
+
+    # 6) UPSERT
+    payload = {
+        "draw_no": drw_no,
+        "curator_avg_match": cur.get("avg_match"),
+        "curator_best_tier": cur.get("best_tier"),
+        "curator_best_match": cur.get("best_match"),
+        "curator_5plus_prizes": cur.get("five_plus_prizes"),
+        "user_avg_match": usr.get("avg_match"),
+        "user_best_match": usr.get("best_match"),
+        "user_5plus_prizes": usr.get("five_plus_prizes"),
+        "user_pattern_summary": json.dumps(user_pattern, ensure_ascii=False) if user_pattern else None,
+        "draw_pattern_summary": json.dumps(draw_pattern, ensure_ascii=False),
+        "pattern_delta": delta,
+    }
+    rid = db.save_review(payload)
+    logger.info(f"[grade_weekly_review] saved review id={rid} for draw {drw_no}")
+    return {"ok": True, "review_id": rid}
+
+
+def run_for_latest() -> dict:
+    """가장 최근 sync된 추첨 회차로 채점 — cron 진입점."""
+    latest = db.get_latest_draw()
+    if not latest:
+        return {"ok": False, "reason": "no draws"}
+    return run_weekly_grading(latest["drw_no"])