""" Dataset-related Pydantic models for API requests and responses. """ from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field, model_validator class CheckFormatRequest(BaseModel): """Request for dataset format check""" dataset_name: str # HuggingFace dataset name or local path is_vlm: bool = False hf_token: Optional[str] = None subset: Optional[str] = None train_split: Optional[str] = "train" @model_validator(mode="before") @classmethod def _compat_split(cls, values: Any) -> Any: """Accept legacy 'split' field as alias for 'train_split'.""" if isinstance(values, dict) and "split" in values: values.setdefault("train_split", values.pop("split")) return values class CheckFormatResponse(BaseModel): """Response for dataset format check""" requires_manual_mapping: bool detected_format: str columns: List[str] is_image: bool = False is_audio: bool = False multimodal_columns: Optional[List[str]] = None suggested_mapping: Optional[Dict[str, str]] = None detected_image_column: Optional[str] = None detected_audio_column: Optional[str] = None detected_text_column: Optional[str] = None detected_speaker_column: Optional[str] = None preview_samples: Optional[List[Dict]] = None total_rows: Optional[int] = None warning: Optional[str] = None class UploadDatasetResponse(BaseModel): """Response with stored dataset path for training.""" filename: str = Field(..., description="Original filename") stored_path: str = Field(..., description="Absolute path stored on backend") class LocalDatasetItem(BaseModel): class Metadata(BaseModel): actual_num_records: Optional[int] = None target_num_records: Optional[int] = None total_num_batches: Optional[int] = None num_completed_batches: Optional[int] = None columns: Optional[List[str]] = None id: str label: str path: str rows: Optional[int] = None updated_at: Optional[float] = None metadata: Optional[Metadata] = None class LocalDatasetsResponse(BaseModel): datasets: List[LocalDatasetItem] = Field(default_factory=list)