import datetime as dt

from sqlalchemy import DateTime, Float, Integer, String, Text, UniqueConstraint
from sqlalchemy.orm import Mapped, mapped_column

from .db import Base


class Article(Base):
    __tablename__ = "articles"
    __table_args__ = (UniqueConstraint("url", name="uq_article_url"),)

    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    source: Mapped[str] = mapped_column(String(128), index=True)
    source_bias: Mapped[str] = mapped_column(String(64), default="")
    title: Mapped[str] = mapped_column(Text)
    url: Mapped[str] = mapped_column(Text, index=True)
    summary: Mapped[str] = mapped_column(Text, default="")

    published_at: Mapped[dt.datetime] = mapped_column(DateTime, index=True)
    fetched_at: Mapped[dt.datetime] = mapped_column(DateTime, default=dt.datetime.utcnow)

    location_name: Mapped[str | None] = mapped_column(String(128), nullable=True, index=True)
    country: Mapped[str | None] = mapped_column(String(128), nullable=True)
    lat: Mapped[float | None] = mapped_column(Float, nullable=True)
    lon: Mapped[float | None] = mapped_column(Float, nullable=True)

    # Rounded lat/lon grid key articles are clustered on, e.g. "31.5:34.5"
    cluster_key: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)


class MarketPrice(Base):
    __tablename__ = "market_prices"

    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    symbol: Mapped[str] = mapped_column(String(32), index=True)
    label: Mapped[str] = mapped_column(String(128))
    category: Mapped[str] = mapped_column(String(32))  # "index" | "commodity"
    price: Mapped[float] = mapped_column(Float)
    currency: Mapped[str] = mapped_column(String(8), default="USD")
    change_pct: Mapped[float | None] = mapped_column(Float, nullable=True)
    recorded_at: Mapped[dt.datetime] = mapped_column(DateTime, index=True, default=dt.datetime.utcnow)


class MarketSpike(Base):
    __tablename__ = "market_spikes"

    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    symbol: Mapped[str] = mapped_column(String(32), index=True)
    label: Mapped[str] = mapped_column(String(128))
    from_price: Mapped[float] = mapped_column(Float)
    to_price: Mapped[float] = mapped_column(Float)
    pct_change: Mapped[float] = mapped_column(Float)
    # This instrument's recent typical (mean absolute) poll-to-poll move,
    # for comparison — null if there wasn't enough price history yet to
    # compute one (see market_spike_min_samples). Lets the UI show "Nx this
    # instrument's normal move" instead of just the raw percentage.
    baseline_volatility_pct: Mapped[float | None] = mapped_column(Float, nullable=True)

    window_start: Mapped[dt.datetime] = mapped_column(DateTime)
    window_end: Mapped[dt.datetime] = mapped_column(DateTime)
    detected_at: Mapped[dt.datetime] = mapped_column(DateTime, index=True, default=dt.datetime.utcnow)

    # JSON-encoded list of Article.id, ranked most-to-least likely relevant.
    article_ids_json: Mapped[str] = mapped_column(Text, default="[]")
    # JSON-encoded [[word, count], ...] — plain word-frequency across every
    # article in the window's titles, stopwords dropped. A heuristic hint at
    # "likely factors," not an explanation; see textutil.py.
    top_keywords_json: Mapped[str] = mapped_column(Text, default="[]")


class ConflictEvent(Base):
    __tablename__ = "conflict_events"
    __table_args__ = (UniqueConstraint("source", "external_id", name="uq_conflict_event"),)

    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    source: Mapped[str] = mapped_column(String(32), default="acled")
    external_id: Mapped[str] = mapped_column(String(64))

    event_type: Mapped[str] = mapped_column(String(128))
    actor1: Mapped[str] = mapped_column(String(256), default="")
    actor2: Mapped[str] = mapped_column(String(256), default="")
    fatalities: Mapped[int | None] = mapped_column(Integer, nullable=True)
    notes: Mapped[str] = mapped_column(Text, default="")

    location_name: Mapped[str] = mapped_column(String(128), default="")
    country: Mapped[str] = mapped_column(String(128), default="")
    lat: Mapped[float] = mapped_column(Float)
    lon: Mapped[float] = mapped_column(Float)

    event_date: Mapped[dt.datetime] = mapped_column(DateTime, index=True)