from __future__ import annotations

from datetime import datetime
from typing import Literal
from uuid import UUID

from pydantic import BaseModel, Field

from app.schemas.operations import VectorSelectionBBox


class AssistantChatMessage(BaseModel):
    role: Literal["user", "assistant"]
    content: str = Field(min_length=1, max_length=4_000)


class AssistantQueryRequest(BaseModel):
    question: str = Field(min_length=2, max_length=2_000)
    model: str | None = Field(default=None, max_length=255)
    bbox: VectorSelectionBBox | None = None
    area_id: UUID | None = None
    history: list[AssistantChatMessage] = Field(default_factory=list, max_length=8)


class AssistantModelRead(BaseModel):
    name: str
    size_bytes: int | None = None
    parameter_size: str | None = None
    quantization_level: str | None = None
    capabilities: list[str] = Field(default_factory=list)


class AssistantModelList(BaseModel):
    items: list[AssistantModelRead]
    total: int
    default_model: str | None = None


class AssistantStatus(BaseModel):
    enabled: bool
    reachable: bool
    status: str
    base_url: str
    default_model: str | None = None
    model_count: int = 0
    limitation_message: str


class AssistantContextMetric(BaseModel):
    theme: str
    label: str
    value: float
    unit: str
    source: str
    dataset_id: UUID
    observed_at: datetime | None = None
    is_estimate: bool = False


class AssistantTemporalSeries(BaseModel):
    temporal_series_key: str
    label: str
    source: str
    first_year: int
    last_year: int
    observation_count: int


class AssistantEstimateDisclosure(BaseModel):
    """A value in the answer that the source itself calls an estimate.

    Derived from metric metadata rather than from the generated sentences, so
    the disclosure is present whatever wording the model chose.
    """

    theme: str
    label: str
    unit: str
    source: str
    dataset_id: UUID
    reason: str


class AssistantQueryResponse(BaseModel):
    answer: str
    model: str
    scope_label: str
    context_metrics: list[AssistantContextMetric]
    temporal_series: list[AssistantTemporalSeries]
    estimate_disclosures: list[AssistantEstimateDisclosure] = Field(default_factory=list)
    source_dataset_ids: list[UUID]
    warnings: list[str]
    generated_at: datetime