gahusb/web-page-backend
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

refactor: backend→lotto 서비스 리네이밍 + lotto.db 레거시 테이블 스키마 제거

Browse Source 
- 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>
This commit is contained in:
gahusb
2026-04-27 17:29:13 +09:00
parent 6c46759848
commit 2a8635e9ed
26 changed files with 18 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

837
lotto/app/main.py Normal file
View File

@@ -0,0 +1,837 @@
import os
import time
import logging
from typing import Optional, List, Dict, Any, Tuple
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from apscheduler.schedulers.background import BackgroundScheduler
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(levelname)s %(message)s")
logger = logging.getLogger("lotto-backend")
from .db import (
init_db, get_draw, get_latest_draw, get_all_draw_numbers,
save_recommendation_dedup, list_recommendations_ex, delete_recommendation,
update_recommendation,
# 시뮬레이션 관련
get_best_picks, get_simulation_runs, get_simulation_candidates,
# 성과 통계
get_recommendation_performance,
# Phase 2: 구매 이력
add_purchase, get_purchases, update_purchase, delete_purchase, get_purchase_stats,
# Phase 2: 주간 리포트 캐시
save_weekly_report, get_weekly_report_list, get_weekly_report,
# Phase 2: 개인 패턴 분석
get_all_recommendation_numbers,
# Phase 3: 전략 관련
get_strategy_performance as db_get_strategy_performance,
)
from .recommender import recommend_numbers, recommend_with_heatmap
from .collector import sync_latest, sync_ensure_all
from .generator import run_simulation, generate_smart_recommendations
from .checker import check_results_for_draw
from .utils import calc_metrics, calc_recent_overlap
from .analyzer import get_statistical_report, generate_weekly_report, analyze_personal_patterns, generate_combined_recommendation
from .purchase_manager import check_purchases_for_draw
from .strategy_evolver import (
get_weights_with_trend, recalculate_weights,
generate_smart_recommendation,
)
from .routers import curator as curator_router
from .routers import briefing as briefing_router
app = FastAPI()
app.include_router(curator_router.router)
app.include_router(briefing_router.router)
scheduler = BackgroundScheduler(timezone=os.getenv("TZ", "Asia/Seoul"))
ALL_URL = os.getenv("LOTTO_ALL_URL", "https://smok95.github.io/lotto/results/all.json")
LATEST_URL = os.getenv("LOTTO_LATEST_URL", "https://smok95.github.io/lotto/results/latest.json")
# ── 성과 통계 인메모리 캐시 ───────────────────────────────────────────────────
# 채점 데이터는 하루 2번 스케줄러 실행 시에만 갱신되므로 인메모리 캐시로 충분
_PERF_CACHE: Dict[str, Any] = {"data": None, "at": 0.0}
_PERF_CACHE_TTL = 3600 # 1시간 (스케줄러 미실행 상황 대비 폴백)
def _refresh_perf_cache() -> None:
_PERF_CACHE["data"] = get_recommendation_performance()
_PERF_CACHE["at"] = time.time()
logger.info("성과 통계 캐시 갱신")
@app.on_event("startup")
def on_startup():
init_db()
# 1. 로또 당첨번호 동기화 (매일 9시, 21시 10분)
# 동기화 후 새로운 회차가 있으면 채점(check)까지 수행
def _sync_and_check():
res = sync_latest(LATEST_URL)
if res["was_new"]:
check_results_for_draw(res["drawNo"])
_refresh_perf_cache() # 새 채점 결과 반영 → 즉시 갱신
scheduler.add_job(_sync_and_check, "cron", hour="9,21", minute=10)
# 2. 몬테카를로 시뮬레이션 (하루 6회: 0, 4, 8, 12, 16, 20시)
# 20,000개 후보 생성 → 스코어링 → 상위 100개 저장 → best_picks 교체
def _run_simulation_job():
run_simulation(n_candidates=20000, top_k=100, best_n=20)
scheduler.add_job(_run_simulation_job, "cron", hour="0,4,8,12,16,20", minute=5)
# 3. 토요일 오전 9시 — 다음 회차 공략 리포트 자동 캐싱
def _save_weekly_report_job():
import json as _json
draws = get_all_draw_numbers()
latest = get_latest_draw()
if not draws or not latest:
return
target = latest["drw_no"] + 1
report = generate_weekly_report(draws, target)
save_weekly_report(target, _json.dumps(report, ensure_ascii=False))
logger.info(f"{target}회차 리포트 저장 완료")
scheduler.add_job(_save_weekly_report_job, "cron", day_of_week="sat", hour=9, minute=0)
scheduler.start()
@app.get("/health")
def health():
return {"ok": True}
@app.get("/api/lotto/latest")
def api_latest():
row = get_latest_draw()
if not row:
raise HTTPException(status_code=404, detail="No data yet")
return {
"drawNo": row["drw_no"],
"date": row["drw_date"],
"numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
"bonus": row["bonus"],
"metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
}
@app.get("/api/lotto/{drw_no:int}")
def api_draw(drw_no: int):
row = get_draw(drw_no)
if not row:
raise HTTPException(status_code=404, detail="Not found")
return {
"drwNo": row["drw_no"],
"date": row["drw_date"],
"numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
"bonus": row["bonus"],
"metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
}
@app.post("/api/admin/sync_latest")
def admin_sync_latest():
res = sync_latest(LATEST_URL)
if res["was_new"]:
check_results_for_draw(res["drawNo"])
return res
@app.post("/api/admin/auto_gen")
def admin_auto_gen(count: int = 10):
"""기존 호환 유지: 소규모 시뮬레이션 수동 트리거"""
n = generate_smart_recommendations(count)
return {"generated": n}
@app.post("/api/admin/simulate")
def admin_simulate(n_candidates: int = 20000, top_k: int = 100, best_n: int = 20):
"""
몬테카를로 시뮬레이션 수동 트리거.
백그라운드 스케줄과 동일한 동작을 즉시 실행.
"""
result = run_simulation(
n_candidates=max(1000, min(n_candidates, 50000)),
top_k=max(10, min(top_k, 500)),
best_n=max(10, min(best_n, 50)),
)
if "error" in result:
raise HTTPException(status_code=500, detail=result["error"])
return result
@app.get("/api/lotto/stats")
def api_stats():
sync_ensure_all(LATEST_URL, ALL_URL)
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
frequency = {n: 0 for n in range(1, 46)}
total_draws = len(draws)
for _, nums in draws:
for n in nums:
frequency[n] += 1
stats = [
{"number": n, "count": frequency[n]}
for n in range(1, 46)
]
return {
"total_draws": total_draws,
"frequency": stats,
}
# ── 추천 성과 통계 (Phase 1, 인메모리 캐시) ──────────────────────────────────
@app.get("/api/lotto/stats/performance")
def api_performance_stats():
"""
채점된 추천 이력 기반 성과 통계 (캐시 반환).
캐시 갱신 시점: 새 회차 채점 직후 | TTL 1시간 만료 시
"""
if _PERF_CACHE["data"] is None or time.time() - _PERF_CACHE["at"] > _PERF_CACHE_TTL:
_refresh_perf_cache()
return _PERF_CACHE["data"]
# ── 회차 공략 리포트 (Phase 1) ────────────────────────────────────────────────
@app.get("/api/lotto/report/latest")
def api_report_latest():
"""
다음 회차 공략 리포트 (최신 회차 기준으로 자동 계산).
- 과출현/냉각/오버듀 번호 분석
- 최근 3회 패턴
- 3가지 전략별 추천 번호
- AI 신뢰도 점수
"""
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
latest = get_latest_draw()
target = latest["drw_no"] + 1
return generate_weekly_report(draws, target)
@app.get("/api/lotto/report/history")
def api_report_history(limit: int = 10):
"""저장된 주간 리포트 목록 (자동 저장된 것만)"""
return {"reports": get_weekly_report_list(limit=min(limit, 52))}
@app.get("/api/lotto/report/{drw_no}")
def api_report_by_draw(drw_no: int):
"""
특정 회차 공략 리포트 (캐시 우선, 없으면 실시간 생성).
"""
cached = get_weekly_report(drw_no)
if cached:
return {**cached, "cached": True}
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
base_draws = [(no, nums) for no, nums in draws if no < drw_no]
if not base_draws:
raise HTTPException(status_code=400, detail=f"{drw_no}회차 이전 데이터가 없습니다")
return {**generate_weekly_report(base_draws, drw_no), "cached": False}
# ── 개인 패턴 분석 (Phase 2) ─────────────────────────────────────────────────
@app.get("/api/lotto/analysis/personal")
def api_personal_analysis():
"""
저장된 추천 이력 기반 개인 패턴 분석.
- 자주 선택한 번호 TOP 10 / 한 번도 선택 안 한 번호
- 홀짝 비율, 합계, 범위, 연속번호 포함률
- 구간별 분포, 역대 당첨번호 평균과 비교
"""
all_numbers = get_all_recommendation_numbers()
draws = get_all_draw_numbers()
return analyze_personal_patterns(all_numbers, draws)
# ── 구매 이력 API (Phase 2) ───────────────────────────────────────────────────
class PurchaseCreate(BaseModel):
draw_no: int
amount: int
sets: int = 1
prize: int = 0
note: str = ""
numbers: List[List[int]] = []
is_real: bool = True
source_strategy: str = "manual"
source_detail: dict = {}
class PurchaseUpdate(BaseModel):
draw_no: Optional[int] = None
amount: Optional[int] = None
sets: Optional[int] = None
prize: Optional[int] = None
note: Optional[str] = None
numbers: Optional[List[List[int]]] = None
is_real: Optional[bool] = None
source_strategy: Optional[str] = None
@app.get("/api/lotto/purchase/stats")
def api_purchase_stats():
"""투자 수익률 통계 (총 투자금, 총 당첨금, 수익률 등)"""
return get_purchase_stats()
@app.get("/api/lotto/purchase")
def api_purchase_list(draw_no: Optional[int] = None, days: Optional[int] = None,
is_real: Optional[bool] = None, strategy: Optional[str] = None):
"""구매 이력 조회 (필터: draw_no, days, is_real, strategy)"""
return {"records": get_purchases(draw_no=draw_no, days=days, is_real=is_real, strategy=strategy)}
@app.post("/api/lotto/purchase", status_code=201)
def api_purchase_create(body: PurchaseCreate):
"""구매 이력 추가 (실제/가상)"""
sets = body.sets if body.sets > 0 else max(len(body.numbers), 1)
amount = body.amount if body.amount > 0 else sets * 1000
return add_purchase(
draw_no=body.draw_no,
amount=amount,
sets=sets,
prize=body.prize,
note=body.note,
numbers=body.numbers,
is_real=body.is_real,
source_strategy=body.source_strategy,
source_detail=body.source_detail,
)
@app.put("/api/lotto/purchase/{purchase_id}")
def api_purchase_update(purchase_id: int, body: PurchaseUpdate):
"""구매 이력 수정 (당첨금 업데이트 등)"""
updated = update_purchase(purchase_id, body.model_dump(exclude_none=True))
if updated is None:
raise HTTPException(status_code=404, detail="Purchase not found")
return updated
@app.delete("/api/lotto/purchase/{purchase_id}")
def api_purchase_delete(purchase_id: int):
"""구매 이력 삭제"""
if not delete_purchase(purchase_id):
raise HTTPException(status_code=404, detail="Purchase not found")
return {"ok": True}
# ── 전략 진화 API ──────────────────────────────────────────────────────────
@app.get("/api/lotto/strategy/weights")
def api_strategy_weights():
"""현재 전략별 가중치 + 성과 요약 + trend"""
return get_weights_with_trend()
@app.get("/api/lotto/strategy/performance")
def api_strategy_performance(strategy: Optional[str] = None, days: Optional[int] = None):
"""전략별 회차 성과 이력 (차트용)"""
rows = db_get_strategy_performance(strategy=strategy, days=days)
return {"records": rows}
@app.post("/api/lotto/strategy/evolve")
def api_strategy_evolve():
"""수동 가중치 재계산 트리거"""
new_weights = recalculate_weights()
return {"ok": True, "weights": new_weights}
# ── 스마트 추천 API ────────────────────────────────────────────────────────
@app.get("/api/lotto/recommend/smart")
def api_recommend_smart(sets: int = 5):
"""전략 가중치 기반 메타 전략 추천"""
sets = max(1, min(sets, 10))
result = generate_smart_recommendation(sets=sets)
if "error" in result:
raise HTTPException(status_code=500, detail=result["error"])
return result
# ── 통계 분석 리포트 ────────────────────────────────────────────────────────
@app.get("/api/lotto/analysis")
def api_analysis():
"""
5가지 통계 기법 기반 분석 리포트.
- 번호별 빈도, Z-score, 갭
- 핫/콜드/오버듀 번호
- 역대 합계 분포, 홀짝 분포
"""
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
return get_statistical_report(draws)
# ── 시뮬레이션 best_picks (메인 추천 엔드포인트) ────────────────────────────
@app.get("/api/lotto/best")
def api_best_picks(limit: int = 20):
"""
시뮬레이션을 통해 선별된 최적 번호 조합 반환 (기본 20쌍).
하루 6회 시뮬레이션 후 자동 갱신됨.
각 조합에 점수 및 메트릭 포함.
"""
limit = max(1, min(limit, 50))
picks = get_best_picks(limit=limit)
if not picks:
raise HTTPException(
status_code=404,
detail="시뮬레이션 결과가 없습니다. /api/admin/simulate로 먼저 실행하세요.",
)
draws = get_all_draw_numbers()
result = []
for p in picks:
nums = p["numbers"]
result.append({
"rank": p["rank_in_run"],
"numbers": nums,
"score_total": p["score_total"],
"based_on_draw": p["based_on_draw"],
"simulation_run_id": p["source_run_id"],
"created_at": p["created_at"],
"metrics": calc_metrics(nums),
})
latest = get_latest_draw()
return {
"based_on_draw": latest["drw_no"] if latest else None,
"count": len(result),
"items": result,
}
# ── 시뮬레이션 전체 결과 조회 (상세 API) ────────────────────────────────────
@app.get("/api/lotto/simulation")
def api_simulation(run_id: Optional[int] = None, runs_limit: int = 5):
"""
시뮬레이션 실행 기록 및 상위 후보 상세 조회.
run_id 미지정 시: 최근 runs_limit개 실행 기록 + 가장 최근 run의 후보 반환.
run_id 지정 시: 해당 run의 후보만 반환.
"""
runs = get_simulation_runs(limit=runs_limit)
if not runs:
raise HTTPException(status_code=404, detail="시뮬레이션 기록이 없습니다.")
target_run_id = run_id if run_id is not None else runs[0]["id"]
candidates = get_simulation_candidates(target_run_id, limit=100)
# 후보에 메트릭 추가
enriched = []
for c in candidates:
enriched.append({
**c,
"metrics": calc_metrics(c["numbers"]),
})
return {
"runs": runs,
"selected_run_id": target_run_id,
"candidates_count": len(enriched),
"candidates": enriched,
}
# ── 종합 추론 추천 ───────────────────────────────────────────────────────────
@app.get("/api/lotto/recommend/combined")
def api_recommend_combined():
"""5가지 통계 기법 종합 추론 추천 — 결과를 이력에 저장한다."""
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data")
latest = get_latest_draw()
result = generate_combined_recommendation(draws)
if "error" in result:
raise HTTPException(status_code=500, detail=result["error"])
# 추천 이력 저장 (태그: 종합추론)
params = {"method": "combined"}
saved = save_recommendation_dedup(
latest["drw_no"] if latest else None,
result["final_numbers"],
params,
)
if saved["saved"]:
update_recommendation(saved["id"], tags=["종합추론"])
return {
**result,
"id": saved["id"],
"saved": saved["saved"],
"deduped": saved["deduped"],
"based_on_latest_draw": latest["drw_no"] if latest else None,
}
@app.get("/api/lotto/recommend/combined/history")
def api_combined_history(limit: int = 30):
"""종합추론 추천 이력 조회."""
items = list_recommendations_ex(limit=limit, tag="종합추론", sort="id_desc")
return {"items": items, "total": len(items)}
# ── 기존 수동 추천 API (하위 호환 유지) ─────────────────────────────────────
@app.get("/api/lotto/recommend")
def api_recommend(
recent_window: int = 200,
recent_weight: float = 2.0,
avoid_recent_k: int = 5,
sum_min: Optional[int] = None,
sum_max: Optional[int] = None,
odd_min: Optional[int] = None,
odd_max: Optional[int] = None,
range_min: Optional[int] = None,
range_max: Optional[int] = None,
max_overlap_latest: Optional[int] = None,
max_try: int = 200,
):
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
latest = get_latest_draw()
params = {
"recent_window": recent_window,
"recent_weight": float(recent_weight),
"avoid_recent_k": avoid_recent_k,
"sum_min": sum_min,
"sum_max": sum_max,
"odd_min": odd_min,
"odd_max": odd_max,
"range_min": range_min,
"range_max": range_max,
"max_overlap_latest": max_overlap_latest,
"max_try": int(max_try),
}
def _accept(nums: List[int]) -> bool:
m = calc_metrics(nums)
if sum_min is not None and m["sum"] < sum_min:
return False
if sum_max is not None and m["sum"] > sum_max:
return False
if odd_min is not None and m["odd"] < odd_min:
return False
if odd_max is not None and m["odd"] > odd_max:
return False
if range_min is not None and m["range"] < range_min:
return False
if range_max is not None and m["range"] > range_max:
return False
if max_overlap_latest is not None:
ov = calc_recent_overlap(nums, draws, last_k=avoid_recent_k)
if ov["repeats"] > max_overlap_latest:
return False
return True
chosen = None
explain = None
tries = 0
while tries < max_try:
tries += 1
result = recommend_numbers(
draws,
recent_window=recent_window,
recent_weight=recent_weight,
avoid_recent_k=avoid_recent_k,
)
nums = result["numbers"]
if _accept(nums):
chosen = nums
explain = result["explain"]
break
if chosen is None:
raise HTTPException(
status_code=400,
detail=f"Constraints too strict. No valid set found in max_try={max_try}.",
)
saved = save_recommendation_dedup(
latest["drw_no"] if latest else None,
chosen,
params,
)
metrics = calc_metrics(chosen)
overlap = calc_recent_overlap(chosen, draws, last_k=avoid_recent_k)
return {
"id": saved["id"],
"saved": saved["saved"],
"deduped": saved["deduped"],
"based_on_latest_draw": latest["drw_no"] if latest else None,
"numbers": chosen,
"explain": explain,
"params": params,
"metrics": metrics,
"recent_overlap": overlap,
"tries": tries,
}
# ── 히트맵 기반 추천 (하위 호환 유지) ────────────────────────────────────────
@app.get("/api/lotto/recommend/heatmap")
def api_recommend_heatmap(
heatmap_window: int = 20,
heatmap_weight: float = 1.5,
recent_window: int = 200,
recent_weight: float = 2.0,
avoid_recent_k: int = 5,
sum_min: Optional[int] = None,
sum_max: Optional[int] = None,
odd_min: Optional[int] = None,
odd_max: Optional[int] = None,
range_min: Optional[int] = None,
range_max: Optional[int] = None,
max_overlap_latest: Optional[int] = None,
max_try: int = 200,
):
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
past_recs = list_recommendations_ex(limit=100, sort="id_desc")
latest = get_latest_draw()
params = {
"heatmap_window": heatmap_window,
"heatmap_weight": float(heatmap_weight),
"recent_window": recent_window,
"recent_weight": float(recent_weight),
"avoid_recent_k": avoid_recent_k,
"sum_min": sum_min,
"sum_max": sum_max,
"odd_min": odd_min,
"odd_max": odd_max,
"range_min": range_min,
"range_max": range_max,
"max_overlap_latest": max_overlap_latest,
"max_try": int(max_try),
}
def _accept(nums: List[int]) -> bool:
m = calc_metrics(nums)
if sum_min is not None and m["sum"] < sum_min:
return False
if sum_max is not None and m["sum"] > sum_max:
return False
if odd_min is not None and m["odd"] < odd_min:
return False
if odd_max is not None and m["odd"] > odd_max:
return False
if range_min is not None and m["range"] < range_min:
return False
if range_max is not None and m["range"] > range_max:
return False
if max_overlap_latest is not None:
ov = calc_recent_overlap(nums, draws, last_k=avoid_recent_k)
if ov["repeats"] > max_overlap_latest:
return False
return True
chosen = None
explain = None
tries = 0
while tries < max_try:
tries += 1
result = recommend_with_heatmap(
draws,
past_recs,
heatmap_window=heatmap_window,
heatmap_weight=heatmap_weight,
recent_window=recent_window,
recent_weight=recent_weight,
avoid_recent_k=avoid_recent_k,
)
nums = result["numbers"]
if _accept(nums):
chosen = nums
explain = result["explain"]
break
if chosen is None:
raise HTTPException(
status_code=400,
detail=f"Constraints too strict. No valid set found in max_try={max_try}.",
)
saved = save_recommendation_dedup(
latest["drw_no"] if latest else None,
chosen,
params,
)
metrics = calc_metrics(chosen)
overlap = calc_recent_overlap(chosen, draws, last_k=avoid_recent_k)
return {
"id": saved["id"],
"saved": saved["saved"],
"deduped": saved["deduped"],
"based_on_latest_draw": latest["drw_no"] if latest else None,
"numbers": chosen,
"explain": explain,
"params": params,
"metrics": metrics,
"recent_overlap": overlap,
"tries": tries,
}
# ── 추천 이력 ────────────────────────────────────────────────────────────────
@app.get("/api/history")
def api_history(
limit: int = 30,
offset: int = 0,
favorite: Optional[bool] = None,
tag: Optional[str] = None,
q: Optional[str] = None,
sort: str = "id_desc",
):
items = list_recommendations_ex(
limit=limit,
offset=offset,
favorite=favorite,
tag=tag,
q=q,
sort=sort,
)
draws = get_all_draw_numbers()
out = []
for it in items:
nums = it["numbers"]
out.append({
**it,
"metrics": calc_metrics(nums),
"recent_overlap": calc_recent_overlap(
nums, draws, last_k=int(it["params"].get("avoid_recent_k", 0) or 0)
),
})
return {
"items": out,
"limit": limit,
"offset": offset,
"filters": {"favorite": favorite, "tag": tag, "q": q, "sort": sort},
}
@app.delete("/api/history/{rec_id:int}")
def api_history_delete(rec_id: int):
ok = delete_recommendation(rec_id)
if not ok:
raise HTTPException(status_code=404, detail="Not found")
return {"deleted": True, "id": rec_id}
class HistoryUpdate(BaseModel):
favorite: Optional[bool] = None
note: Optional[str] = None
tags: Optional[List[str]] = None
@app.patch("/api/history/{rec_id:int}")
def api_history_patch(rec_id: int, body: HistoryUpdate):
ok = update_recommendation(rec_id, favorite=body.favorite, note=body.note, tags=body.tags)
if not ok:
raise HTTPException(status_code=404, detail="Not found or no changes")
return {"updated": True, "id": rec_id}
# ── 배치 추천 (하위 호환 유지) ───────────────────────────────────────────────
def _batch_unique(draws, count: int, recent_window: int, recent_weight: float, avoid_recent_k: int, max_try: int = 200):
items = []
seen = set()
tries = 0
while len(items) < count and tries < max_try:
tries += 1
r = recommend_numbers(draws, recent_window=recent_window, recent_weight=recent_weight, avoid_recent_k=avoid_recent_k)
key = tuple(sorted(r["numbers"]))
if key in seen:
continue
seen.add(key)
items.append(r)
return items
@app.get("/api/lotto/recommend/batch")
def api_recommend_batch(
count: int = 5,
recent_window: int = 200,
recent_weight: float = 2.0,
avoid_recent_k: int = 5,
):
count = max(1, min(count, 20))
draws = get_all_draw_numbers()
if not draws:
raise HTTPException(status_code=404, detail="No data yet")
latest = get_latest_draw()
params = {
"recent_window": recent_window,
"recent_weight": float(recent_weight),
"avoid_recent_k": avoid_recent_k,
"count": count,
}
items = _batch_unique(draws, count, recent_window, float(recent_weight), avoid_recent_k)
return {
"based_on_latest_draw": latest["drw_no"] if latest else None,
"count": count,
"items": [{
"numbers": it["numbers"],
"explain": it["explain"],
"metrics": calc_metrics(it["numbers"]),
} for it in items],
"params": params,
}
class BatchSave(BaseModel):
items: List[List[int]]
params: dict
@app.post("/api/lotto/recommend/batch")
def api_recommend_batch_save(body: BatchSave):
latest = get_latest_draw()
based = latest["drw_no"] if latest else None
created, deduped = [], []
for nums in body.items:
saved = save_recommendation_dedup(based, nums, body.params)
(created if saved["saved"] else deduped).append(saved["id"])
return {"saved": True, "created_ids": created, "deduped_ids": deduped}
@app.get("/api/version")
def version():
return {"version": os.getenv("APP_VERSION", "dev")}