"""Pydantic schemas for the Reddit examples pipeline.

`RawPost`         — what PRAW gives us (title + body + metadata).
`ExtractedExample`— what the LLM returns (all nullable; confidence-gated).
`Summary`         — per-country headline stats served from the API.
"""
from __future__ import annotations

from datetime import date
from decimal import Decimal
from enum import StrEnum

from pydantic import BaseModel, ConfigDict, Field


class FiStatus(StrEnum):
    ACCUMULATING = "accumulating"
    COAST_FIRE = "coastFIRE"
    BARISTA_FIRE = "baristaFIRE"
    LEAN_FIRE = "leanFIRE"
    FIRE = "FIRE"
    FAT_FIRE = "fatFIRE"
    UNKNOWN = "unknown"


class RawPost(BaseModel):
    """A single Reddit post fetched from PRAW (no LLM processing yet)."""

    model_config = ConfigDict(frozen=True)

    reddit_id: str
    source_sub: str
    url: str
    title: str
    body: str
    created_at: date


class ExtractedExample(BaseModel):
    """LLM output — all extracted fields nullable except confidence + model."""

    country: str | None = None
    city: str | None = None
    portfolio_native: Decimal | None = None
    annual_exp_native: Decimal | None = None
    raw_currency: str | None = None
    age: int | None = Field(default=None, ge=0, le=120)
    family_size: int | None = Field(default=None, ge=1, le=20)
    fi_status: FiStatus | None = None
    is_retired: bool | None = None
    confidence: Decimal = Field(ge=Decimal("0"), le=Decimal("1"))
    llm_model: str


class SummaryStats(BaseModel):
    median: Decimal | None
    p25: Decimal | None
    p75: Decimal | None


class Summary(BaseModel):
    country: str
    count: int
    portfolio_gbp: SummaryStats
    annual_exp_gbp: SummaryStats
    sample_links: list[str]