from datetime import datetime

from sqlalchemy import DateTime, ForeignKey, Integer, Text
from sqlalchemy.dialects.postgresql import ARRAY, JSONB
from sqlalchemy.orm import Mapped, mapped_column

from fabledassistant.models import Base
from fabledassistant.models.base import TimestampMixin


class UserProfile(Base, TimestampMixin):
    __tablename__ = "user_profiles"

    id: Mapped[int] = mapped_column(primary_key=True)
    user_id: Mapped[int] = mapped_column(
        Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False, unique=True
    )
    display_name: Mapped[str | None] = mapped_column(Text, nullable=True)
    job_title: Mapped[str | None] = mapped_column(Text, nullable=True)
    industry: Mapped[str | None] = mapped_column(Text, nullable=True)
    # novice / intermediate / expert — calibrates explanation depth
    expertise_level: Mapped[str | None] = mapped_column(Text, nullable=True)
    # concise / balanced / detailed
    response_style: Mapped[str | None] = mapped_column(Text, nullable=True)
    # casual / professional / technical
    tone: Mapped[str | None] = mapped_column(Text, nullable=True)
    interests: Mapped[list[str] | None] = mapped_column(ARRAY(Text), nullable=True)
    # {days: ["Mon","Tue",...], start: "09:00", end: "17:00"}
    work_schedule: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
    # LLM-consolidated summary of learned preferences
    learned_summary: Mapped[str | None] = mapped_column(Text, nullable=True)
    # [{date: "YYYY-MM-DD", bullets: "..."}, ...]
    observations_raw: Mapped[list | None] = mapped_column(JSONB, nullable=True)
    observations_updated_at: Mapped[datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True
    )

    def to_dict(self) -> dict:
        return {
            "display_name": self.display_name or "",
            "job_title": self.job_title or "",
            "industry": self.industry or "",
            "expertise_level": self.expertise_level or "intermediate",
            "response_style": self.response_style or "balanced",
            "tone": self.tone or "casual",
            "interests": self.interests or [],
            "work_schedule": self.work_schedule or {},
            "learned_summary": self.learned_summary or "",
            "observations_count": len(self.observations_raw or []),
            "observations_updated_at": (
                self.observations_updated_at.isoformat()
                if self.observations_updated_at
                else None
            ),
        }