import datetime as dt
import re
from collections import Counter, defaultdict

from sqlalchemy import select
from sqlalchemy.orm import Session

from .config import settings
from .models import Article

_STOPWORDS = {
    "the", "a", "an", "in", "on", "of", "to", "for", "and", "or", "is", "as",
    "at", "by", "with", "from", "after", "over", "amid", "amid", "into",
    "says", "say", "will", "has", "have", "had", "its", "it", "his", "her",
    "new", "up", "out", "how", "why", "what", "who", "be", "are", "was",
    "were", "this", "that", "than", "not", "no", "us", "u.s.",
}
_WORD_RE = re.compile(r"[A-Za-z][A-Za-z'-]{2,}")


def _topic_label(titles: list[str]) -> str:
    words = Counter()
    for title in titles:
        for word in _WORD_RE.findall(title.lower()):
            if word not in _STOPWORDS:
                words[word] += 1
    top = [w for w, _ in words.most_common(3)]
    return ", ".join(top) if top else ""


def build_clusters(session: Session) -> list[dict]:
    cutoff = dt.datetime.utcnow() - dt.timedelta(hours=settings.article_window_hours)
    rows = session.execute(
        select(Article)
        .where(Article.cluster_key.is_not(None))
        .where(Article.published_at >= cutoff)
        .order_by(Article.published_at.desc())
    ).scalars().all()

    groups: dict[str, list[Article]] = defaultdict(list)
    for article in rows:
        groups[article.cluster_key].append(article)

    clusters = []
    for key, articles in groups.items():
        lat = sum(a.lat for a in articles) / len(articles)
        lon = sum(a.lon for a in articles) / len(articles)
        sources = sorted({a.source for a in articles})
        clusters.append(
            {
                "cluster_key": key,
                "lat": lat,
                "lon": lon,
                "location_name": articles[0].location_name,
                "country": articles[0].country,
                "article_count": len(articles),
                "source_count": len(sources),
                "sources": sources,
                "topic": _topic_label([a.title for a in articles]),
                "latest_published_at": max(a.published_at for a in articles).isoformat(),
                "headline": articles[0].title,
                "article_ids": [a.id for a in articles],
            }
        )

    clusters.sort(key=lambda c: c["article_count"], reverse=True)
    return clusters