411 lines
12 KiB
Python
411 lines
12 KiB
Python
import os
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from typing import Optional, List, Dict, Any, Tuple
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from apscheduler.schedulers.background import BackgroundScheduler
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from .db import (
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init_db, get_draw, get_latest_draw, get_all_draw_numbers,
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save_recommendation_dedup, list_recommendations_ex, delete_recommendation,
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update_recommendation,
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)
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from .recommender import recommend_numbers
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from .collector import sync_latest, sync_ensure_all
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from .generator import generate_smart_recommendations
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from .checker import check_results_for_draw
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app = FastAPI()
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scheduler = BackgroundScheduler(timezone=os.getenv("TZ", "Asia/Seoul"))
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ALL_URL = os.getenv("LOTTO_ALL_URL", "https://smok95.github.io/lotto/results/all.json")
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LATEST_URL = os.getenv("LOTTO_LATEST_URL", "https://smok95.github.io/lotto/results/latest.json")
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def calc_metrics(numbers: List[int]) -> Dict[str, Any]:
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nums = sorted(numbers)
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s = sum(nums)
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odd = sum(1 for x in nums if x % 2 == 1)
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even = len(nums) - odd
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mn, mx = nums[0], nums[-1]
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rng = mx - mn
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# 1-10, 11-20, 21-30, 31-40, 41-45
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buckets = {
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"1-10": 0,
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"11-20": 0,
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"21-30": 0,
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"31-40": 0,
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"41-45": 0,
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}
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for x in nums:
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if 1 <= x <= 10:
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buckets["1-10"] += 1
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elif 11 <= x <= 20:
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buckets["11-20"] += 1
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elif 21 <= x <= 30:
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buckets["21-30"] += 1
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elif 31 <= x <= 40:
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buckets["31-40"] += 1
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else:
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buckets["41-45"] += 1
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return {
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"sum": s,
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"odd": odd,
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"even": even,
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"min": mn,
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"max": mx,
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"range": rng,
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"buckets": buckets,
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}
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def calc_recent_overlap(numbers: List[int], draws: List[Tuple[int, List[int]]], last_k: int) -> Dict[str, Any]:
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"""
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draws: [(drw_no, [n1..n6]), ...] 오름차순
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last_k: 최근 k회 기준 중복
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"""
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if last_k <= 0:
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return {"last_k": 0, "repeats": 0, "repeated_numbers": []}
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recent = draws[-last_k:] if len(draws) >= last_k else draws
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recent_set = set()
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for _, nums in recent:
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recent_set.update(nums)
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repeated = sorted(set(numbers) & recent_set)
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return {
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"last_k": len(recent),
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"repeats": len(repeated),
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"repeated_numbers": repeated,
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}
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@app.on_event("startup")
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def on_startup():
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init_db()
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# 1. 로또 당첨번호 동기화 (매일 9시, 21시 10분)
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# 동기화 후 새로운 회차가 있으면 채점(check)까지 수행
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def _sync_and_check():
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res = sync_latest(LATEST_URL)
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if res["was_new"]:
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# 새로운 회차(예: 1000회)가 나오면, 999회차 기반 추천들을 채점
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check_results_for_draw(res["drawNo"])
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scheduler.add_job(_sync_and_check, "cron", hour="9,21", minute=10)
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# 2. 매일 아침 8시: 지능형 자동 추천 (10개씩)
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scheduler.add_job(lambda: generate_smart_recommendations(10), "cron", hour="8", minute=0)
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scheduler.start()
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@app.get("/health")
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def health():
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return {"ok": True}
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@app.get("/api/lotto/latest")
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def api_latest():
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row = get_latest_draw()
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if not row:
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raise HTTPException(status_code=404, detail="No data yet")
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return {
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"drawNo": row["drw_no"],
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"date": row["drw_date"],
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"numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
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"bonus": row["bonus"],
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"metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
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}
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@app.get("/api/lotto/{drw_no:int}")
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def api_draw(drw_no: int):
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row = get_draw(drw_no)
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if not row:
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raise HTTPException(status_code=404, detail="Not found")
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return {
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"drwNo": row["drw_no"],
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"date": row["drw_date"],
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"numbers": [row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]],
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"bonus": row["bonus"],
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"metrics": calc_metrics([row["n1"], row["n2"], row["n3"], row["n4"], row["n5"], row["n6"]]),
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}
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@app.post("/api/admin/sync_latest")
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def admin_sync_latest():
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res = sync_latest(LATEST_URL)
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# 수동 동기화 시에도 신규 회차면 채점
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if res["was_new"]:
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check_results_for_draw(res["drawNo"])
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return res
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@app.post("/api/admin/auto_gen")
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def admin_auto_gen(count: int = 10):
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"""지능형 자동 생성 수동 트리거"""
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n = generate_smart_recommendations(count)
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return {"generated": n}
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@app.get("/api/lotto/stats")
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def api_stats():
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# 1. 데이터 완전성 보장 (없으면 가져옴)
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sync_ensure_all(LATEST_URL, ALL_URL)
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# 2. 전체 데이터 조회
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draws = get_all_draw_numbers()
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if not draws:
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raise HTTPException(status_code=404, detail="No data yet")
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# 1~45번 빈도 초기화
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frequency = {n: 0 for n in range(1, 46)}
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total_draws = len(draws)
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for _, nums in draws:
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for n in nums:
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frequency[n] += 1
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# 리스트 형태로 변환 (프론트엔드 차트용)
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# x: 번호, y: 횟수
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stats = [
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{"number": n, "count": frequency[n]}
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for n in range(1, 46)
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]
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return {
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"total_draws": total_draws,
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"frequency": stats
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}
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# ---------- ✅ recommend (dedup save) ----------
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@app.get("/api/lotto/recommend")
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def api_recommend(
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recent_window: int = 200,
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recent_weight: float = 2.0,
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avoid_recent_k: int = 5,
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# ---- optional constraints (Lotto Lab) ----
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sum_min: Optional[int] = None,
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sum_max: Optional[int] = None,
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odd_min: Optional[int] = None,
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odd_max: Optional[int] = None,
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range_min: Optional[int] = None,
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range_max: Optional[int] = None,
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max_overlap_latest: Optional[int] = None, # 최근 avoid_recent_k 회차와 중복 허용 개수
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max_try: int = 200, # 조건 맞는 조합 찾기 재시도
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):
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draws = get_all_draw_numbers()
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if not draws:
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raise HTTPException(status_code=404, detail="No data yet")
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latest = get_latest_draw()
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params = {
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"recent_window": recent_window,
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"recent_weight": float(recent_weight),
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"avoid_recent_k": avoid_recent_k,
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"sum_min": sum_min,
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"sum_max": sum_max,
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"odd_min": odd_min,
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"odd_max": odd_max,
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"range_min": range_min,
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"range_max": range_max,
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"max_overlap_latest": max_overlap_latest,
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"max_try": int(max_try),
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}
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def _accept(nums: List[int]) -> bool:
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m = calc_metrics(nums)
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if sum_min is not None and m["sum"] < sum_min:
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return False
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if sum_max is not None and m["sum"] > sum_max:
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return False
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if odd_min is not None and m["odd"] < odd_min:
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return False
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if odd_max is not None and m["odd"] > odd_max:
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return False
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if range_min is not None and m["range"] < range_min:
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return False
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if range_max is not None and m["range"] > range_max:
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return False
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if max_overlap_latest is not None:
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ov = calc_recent_overlap(nums, draws, last_k=avoid_recent_k)
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if ov["repeats"] > max_overlap_latest:
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return False
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return True
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chosen = None
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explain = None
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tries = 0
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while tries < max_try:
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tries += 1
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result = recommend_numbers(
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draws,
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recent_window=recent_window,
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recent_weight=recent_weight,
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avoid_recent_k=avoid_recent_k,
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)
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nums = result["numbers"]
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if _accept(nums):
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chosen = nums
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explain = result["explain"]
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break
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if chosen is None:
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raise HTTPException(
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status_code=400,
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detail=f"Constraints too strict. No valid set found in max_try={max_try}. "
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f"Try relaxing sum/odd/range/overlap constraints.",
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)
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# ✅ dedup save
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saved = save_recommendation_dedup(
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latest["drw_no"] if latest else None,
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chosen,
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params,
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)
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metrics = calc_metrics(chosen)
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overlap = calc_recent_overlap(chosen, draws, last_k=avoid_recent_k)
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return {
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"id": saved["id"],
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"saved": saved["saved"],
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"deduped": saved["deduped"],
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"based_on_latest_draw": latest["drw_no"] if latest else None,
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"numbers": chosen,
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"explain": explain,
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"params": params,
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"metrics": metrics,
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"recent_overlap": overlap,
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"tries": tries,
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}
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# ---------- ✅ history list (filter/paging) ----------
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@app.get("/api/history")
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def api_history(
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limit: int = 30,
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offset: int = 0,
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favorite: Optional[bool] = None,
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tag: Optional[str] = None,
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q: Optional[str] = None,
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sort: str = "id_desc",
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):
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items = list_recommendations_ex(
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limit=limit,
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offset=offset,
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favorite=favorite,
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tag=tag,
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q=q,
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sort=sort,
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)
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draws = get_all_draw_numbers()
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out = []
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for it in items:
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nums = it["numbers"]
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out.append({
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**it,
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"metrics": calc_metrics(nums),
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"recent_overlap": calc_recent_overlap(
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nums, draws, last_k=int(it["params"].get("avoid_recent_k", 0) or 0)
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),
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})
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return {
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"items": out,
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"limit": limit,
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"offset": offset,
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"filters": {"favorite": favorite, "tag": tag, "q": q, "sort": sort},
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}
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@app.delete("/api/history/{rec_id:int}")
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def api_history_delete(rec_id: int):
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ok = delete_recommendation(rec_id)
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if not ok:
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raise HTTPException(status_code=404, detail="Not found")
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return {"deleted": True, "id": rec_id}
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# ---------- ✅ history update (favorite/note/tags) ----------
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class HistoryUpdate(BaseModel):
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favorite: Optional[bool] = None
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note: Optional[str] = None
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tags: Optional[List[str]] = None
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@app.patch("/api/history/{rec_id:int}")
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def api_history_patch(rec_id: int, body: HistoryUpdate):
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ok = update_recommendation(rec_id, favorite=body.favorite, note=body.note, tags=body.tags)
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if not ok:
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raise HTTPException(status_code=404, detail="Not found or no changes")
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return {"updated": True, "id": rec_id}
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# ---------- ✅ batch recommend ----------
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def _batch_unique(draws, count: int, recent_window: int, recent_weight: float, avoid_recent_k: int, max_try: int = 200):
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items = []
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seen = set()
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tries = 0
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while len(items) < count and tries < max_try:
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tries += 1
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r = recommend_numbers(draws, recent_window=recent_window, recent_weight=recent_weight, avoid_recent_k=avoid_recent_k)
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key = tuple(sorted(r["numbers"]))
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if key in seen:
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continue
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seen.add(key)
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items.append(r)
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return items
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@app.get("/api/lotto/recommend/batch")
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def api_recommend_batch(
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count: int = 5,
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recent_window: int = 200,
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recent_weight: float = 2.0,
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avoid_recent_k: int = 5,
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):
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count = max(1, min(count, 20))
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draws = get_all_draw_numbers()
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if not draws:
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raise HTTPException(status_code=404, detail="No data yet")
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latest = get_latest_draw()
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params = {
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"recent_window": recent_window,
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"recent_weight": float(recent_weight),
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"avoid_recent_k": avoid_recent_k,
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"count": count,
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}
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items = _batch_unique(draws, count, recent_window, float(recent_weight), avoid_recent_k)
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return {
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"based_on_latest_draw": latest["drw_no"] if latest else None,
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"count": count,
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"items": [{
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"numbers": it["numbers"],
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"explain": it["explain"],
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"metrics": calc_metrics(it["numbers"]),
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} for it in items],
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"params": params,
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}
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class BatchSave(BaseModel):
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items: List[List[int]]
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params: dict
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@app.post("/api/lotto/recommend/batch")
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def api_recommend_batch_save(body: BatchSave):
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latest = get_latest_draw()
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based = latest["drw_no"] if latest else None
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created, deduped = [], []
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for nums in body.items:
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saved = save_recommendation_dedup(based, nums, body.params)
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(created if saved["saved"] else deduped).append(saved["id"])
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return {"saved": True, "created_ids": created, "deduped_ids": deduped}
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@app.get("/api/version")
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def version():
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import os
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return {"version": os.getenv("APP_VERSION", "dev")}
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