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- backend/ → lotto/ 디렉토리 이동 - docker-compose: lotto-backend→lotto, lotto-frontend→frontend - deploy scripts, nginx, agent-office config 네이밍 일괄 반영 - lotto/app/db.py에서 todos·blog_posts CREATE TABLE 제거 (personal로 이관 완료) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
import json
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from .db import (
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_conn, get_draw, update_recommendation_result
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)
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def _calc_rank(my_nums: list[int], win_nums: list[int], bonus: int) -> tuple[int, int, bool]:
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"""
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(rank, correct_cnt, has_bonus) 반환
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rank: 1~5 (1등~5등), 0 (낙첨)
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"""
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matched = set(my_nums) & set(win_nums)
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cnt = len(matched)
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has_bonus = bonus in my_nums
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if cnt == 6:
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return 1, cnt, has_bonus
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if cnt == 5 and has_bonus:
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return 2, cnt, has_bonus
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if cnt == 5:
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return 3, cnt, has_bonus
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if cnt == 4:
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return 4, cnt, has_bonus
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if cnt == 3:
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return 5, cnt, has_bonus
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return 0, cnt, has_bonus
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def check_results_for_draw(drw_no: int) -> int:
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"""
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특정 회차(drw_no) 결과가 나왔을 때,
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해당 회차를 타겟으로 했던(based_on_draw == drw_no - 1) 추천들을 채점한다.
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반환값: 채점한 개수
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"""
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win_row = get_draw(drw_no)
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if not win_row:
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return 0
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win_nums = [
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win_row["n1"], win_row["n2"], win_row["n3"],
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win_row["n4"], win_row["n5"], win_row["n6"]
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]
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bonus = win_row["bonus"]
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# based_on_draw가 (이번회차 - 1)인 것들 조회
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# 즉, 1000회차 추첨 결과로는, 999회차 데이터를 바탕으로 1000회차를 예측한 것들을 채점
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target_based_on = drw_no - 1
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with _conn() as conn:
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rows = conn.execute(
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"""
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SELECT id, numbers
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FROM recommendations
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WHERE based_on_draw = ? AND checked = 0
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""",
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(target_based_on,)
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).fetchall()
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count = 0
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for r in rows:
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my_nums = json.loads(r["numbers"])
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rank, correct, has_bonus = _calc_rank(my_nums, win_nums, bonus)
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update_recommendation_result(r["id"], rank, correct, has_bonus)
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count += 1
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# ── 구매 이력 체크 연동 ──────────────────────────────────────
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try:
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from .purchase_manager import check_purchases_for_draw as _check_purchases
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_check_purchases(drw_no) # 내부에서 evolve_after_check → recalculate_weights 호출
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except ImportError:
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pass # purchase_manager 미설치 시 무시 (하위호환)
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return count
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