chore: initial backend + travel-proxy dockerized setup for NAS deployment

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2026-01-25 11:27:18 +09:00
parent 0fa163c00a
commit 9c9968b9a7
15 changed files with 1219 additions and 0 deletions

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backend/app/main.py Normal file
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import os
from typing import Optional, List, Dict, Any, Tuple
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from apscheduler.schedulers.background import BackgroundScheduler
from .db import (
init_db, get_draw, get_latest_draw, get_all_draw_numbers,
save_recommendation_dedup, list_recommendations_ex, delete_recommendation,
update_recommendation,
)
from .recommender import recommend_numbers
from .collector import sync_latest
app = FastAPI()
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")
def calc_metrics(numbers: List[int]) -> Dict[str, Any]:
nums = sorted(numbers)
s = sum(nums)
odd = sum(1 for x in nums if x % 2 == 1)
even = len(nums) - odd
mn, mx = nums[0], nums[-1]
rng = mx - mn
# 1-10, 11-20, 21-30, 31-40, 41-45
buckets = {
"1-10": 0,
"11-20": 0,
"21-30": 0,
"31-40": 0,
"41-45": 0,
}
for x in nums:
if 1 <= x <= 10:
buckets["1-10"] += 1
elif 11 <= x <= 20:
buckets["11-20"] += 1
elif 21 <= x <= 30:
buckets["21-30"] += 1
elif 31 <= x <= 40:
buckets["31-40"] += 1
else:
buckets["41-45"] += 1
return {
"sum": s,
"odd": odd,
"even": even,
"min": mn,
"max": mx,
"range": rng,
"buckets": buckets,
}
def calc_recent_overlap(numbers: List[int], draws: List[Tuple[int, List[int]]], last_k: int) -> Dict[str, Any]:
"""
draws: [(drw_no, [n1..n6]), ...] 오름차순
last_k: 최근 k회 기준 중복
"""
if last_k <= 0:
return {"last_k": 0, "repeats": 0, "repeated_numbers": []}
recent = draws[-last_k:] if len(draws) >= last_k else draws
recent_set = set()
for _, nums in recent:
recent_set.update(nums)
repeated = sorted(set(numbers) & recent_set)
return {
"last_k": len(recent),
"repeats": len(repeated),
"repeated_numbers": repeated,
}
@app.on_event("startup")
def on_startup():
init_db()
scheduler.add_job(lambda: sync_latest(LATEST_URL), "cron", hour="9,21", minute=10)
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"],
}
@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"],
}
@app.post("/api/admin/sync_latest")
def admin_sync_latest():
return sync_latest(LATEST_URL)
# ---------- ✅ recommend (dedup save) ----------
@app.get("/api/lotto/recommend")
def api_recommend(
recent_window: int = 200,
recent_weight: float = 2.0,
avoid_recent_k: int = 5,
# ---- optional constraints (Lotto Lab) ----
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, # 최근 avoid_recent_k 회차와 중복 허용 개수
max_try: int = 200, # 조건 맞는 조합 찾기 재시도
):
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}. "
f"Try relaxing sum/odd/range/overlap constraints.",
)
# ✅ dedup save
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,
}
# ---------- ✅ history list (filter/paging) ----------
@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}
# ---------- ✅ history update (favorite/note/tags) ----------
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}
# ---------- ✅ batch recommend ----------
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"]} 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}