"""Rule-based nomad assistant (no LLM)."""

from __future__ import annotations

KEYWORD_CITIES = {
  "便宜": ["chiangmai", "mexico", "bali"],
  "budget": ["chiangmai", "mexico", "bali"],
  "快": ["tokyo", "barcelona", "lisbon"],
  "网速": ["tokyo", "barcelona", "lisbon"],
  "internet": ["tokyo", "barcelona", "lisbon"],
  "暖": ["bali", "chiangmai", "mexico"],
  "warm": ["bali", "chiangmai", "mexico"],
  "冷": ["lisbon", "barcelona"],
  "cool": ["lisbon", "barcelona"],
  "签证": ["lisbon", "mexico"],
  "visa": ["lisbon", "mexico"],
  "中文": ["chiangmai", "bali"],
  "chinese": ["chiangmai", "bali"],
}


def assistant_reply(message: str, destinations: list[dict]) -> dict:
  msg = message.lower()
  picks: list[str] = []
  for kw, slugs in KEYWORD_CITIES.items():
    if kw in msg:
      picks.extend(slugs)
  if not picks:
    picks = ["chiangmai", "lisbon", "bali"]
  seen: set[str] = set()
  items = []
  for slug in picks:
    if slug in seen:
      continue
    seen.add(slug)
    dest = next((d for d in destinations if d.get("slug") == slug), None)
    if dest:
      items.append({
        "slug": slug,
        "name": dest.get("name", slug),
        "emoji": dest.get("emoji", "🌍"),
        "reason": f"匹配你的需求：{message[:40]}",
      })
    if len(items) >= 3:
      break
  reply = (
    f"根据你的描述，我推荐看看 {', '.join(i['name'] for i in items)}。"
    " 可以用「下一站决策」做更精细的筛选，或在社区问当地细节。"
  )
  return {"reply": reply, "cities": items}