"""Finnhub news collector with VADER sentiment analysis."""

import logging
from datetime import datetime, timezone

import aiohttp
from nltk.sentiment.vader import SentimentIntensityAnalyzer

from shared.models import NewsCategory, NewsItem

from .base import BaseCollector

logger = logging.getLogger(__name__)

_CATEGORY_KEYWORDS: dict[NewsCategory, list[str]] = {
    NewsCategory.FED: ["fed", "fomc", "rate", "federal reserve"],
    NewsCategory.POLICY: ["tariff", "trump", "regulation", "policy", "trade war"],
    NewsCategory.EARNINGS: ["earnings", "revenue", "profit", "eps", "guidance", "quarter"],
}


def _categorize(text: str) -> NewsCategory:
    lower = text.lower()
    for category, keywords in _CATEGORY_KEYWORDS.items():
        if any(kw in lower for kw in keywords):
            return category
    return NewsCategory.MACRO


class FinnhubCollector(BaseCollector):
    name: str = "finnhub"
    poll_interval: int = 300

    _BASE_URL = "https://finnhub.io/api/v1/news"

    def __init__(self, api_key: str) -> None:
        self._api_key = api_key
        self._vader = SentimentIntensityAnalyzer()

    async def is_available(self) -> bool:
        return bool(self._api_key)

    async def _fetch_news(self) -> list[dict]:
        url = f"{self._BASE_URL}?category=general&token={self._api_key}"
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as resp:
                resp.raise_for_status()
                return await resp.json()

    async def collect(self) -> list[NewsItem]:
        try:
            raw_items = await self._fetch_news()
        except Exception as exc:
            logger.error("Finnhub fetch failed: %s", exc)
            return []

        items: list[NewsItem] = []
        for article in raw_items:
            headline = article.get("headline", "")
            summary = article.get("summary", "")
            combined = f"{headline} {summary}"

            sentiment_scores = self._vader.polarity_scores(combined)
            sentiment = sentiment_scores["compound"]

            ts = article.get("datetime", 0)
            published_at = datetime.fromtimestamp(ts, tz=timezone.utc)

            related = article.get("related", "")
            symbols = [t.strip() for t in related.split(",") if t.strip()] if related else []

            category = _categorize(combined)

            items.append(
                NewsItem(
                    source=self.name,
                    headline=headline,
                    summary=summary or None,
                    url=article.get("url") or None,
                    published_at=published_at,
                    symbols=symbols,
                    sentiment=sentiment,
                    category=category,
                    raw_data=article,
                )
            )

        return items