fashion_ai/schemas.py

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any, Literal

import numpy as np

SlotName = Literal["top", "bottom", "shoes", "accessory", "unknown"]


@dataclass(slots=True)
class WeatherContext:
    season: str = "all-season"
    temperature_c: float | None = None
    is_rainy: bool | None = None


@dataclass(slots=True)
class RecommendationContext:
    occasion: str = "casual"
    weather: WeatherContext = field(default_factory=WeatherContext)
    region: str = "global"
    user_profile: dict[str, Any] = field(default_factory=dict)


@dataclass(slots=True)
class EncodedWardrobeItem:
    item: dict[str, Any]
    vector: np.ndarray
    slot: SlotName
    metadata_text: str


@dataclass(slots=True)
class OutfitCandidate:
    top: EncodedWardrobeItem
    bottom: EncodedWardrobeItem
    shoes: EncodedWardrobeItem | None = None
    accessory: EncodedWardrobeItem | None = None
    score: float = 0.0
    breakdown: dict[str, float] = field(default_factory=dict)
    reason: str = ""
    tip: str = ""

    def slot_items(self) -> list[EncodedWardrobeItem]:
        return [
            slot_item
            for slot_item in [self.top, self.bottom, self.shoes, self.accessory]
            if slot_item is not None
        ]