from pydantic import BaseModel
from datetime import datetime


class TranscriptOut(BaseModel):
    id: str
    filename: str
    file_path: str
    line_count: int
    file_size: int
    status: str
    error_message: str
    uploaded_at: datetime

    class Config:
        from_attributes = True


class PersonOut(BaseModel):
    id: str
    name: str
    nickname: str
    filename: str
    file_path: str
    photo_path: str
    info: str
    uploaded_at: datetime

    class Config:
        from_attributes = True


class StoryOut(BaseModel):
    id: str
    title: str
    summary: str
    content: str
    raw_material: str
    speaker_nickname: str
    source_transcript_id: str
    source_lines: str
    duration_minutes: float
    confidence: float
    confidence_level: str
    person_id: str | None
    match_status: str
    created_at: datetime

    class Config:
        from_attributes = True


class StoryEdit(BaseModel):
    title: str | None = None
    summary: str | None = None
    content: str | None = None


class MatchCreate(BaseModel):
    story_id: str
    person_id: str


class MatchOut(BaseModel):
    story_id: str
    person_id: str
    person_name: str
    story_title: str

    class Config:
        from_attributes = True


class ExportItemOut(BaseModel):
    person_id: str
    person_name: str
    story_count: int
    has_photo: bool


class SettingsOut(BaseModel):
    llm_provider: str
    llm_model: str
    llm_base_url: str
    llm_api_key: str
    segment_max_lines: int
    story_min_lines: int
    confidence_threshold: float
    temperature: float


class SettingsUpdate(BaseModel):
    llm_provider: str | None = None
    llm_model: str | None = None
    llm_base_url: str | None = None
    llm_api_key: str | None = None
    segment_max_lines: int | None = None
    story_min_lines: int | None = None
    confidence_threshold: float | None = None
    temperature: float | None = None