"""Pydantic schemas for the live datacenter verification inference API."""

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, ConfigDict, Field


class PredictRequest(BaseModel):
    """One-row prediction request.

    The feature dictionary intentionally accepts arbitrary keys because the
    public demo exposes only a subset of the model columns and future UI
    controls may add new fields before the API schema changes.
    """

    model_config = ConfigDict(extra="allow")

    feature_row_id: str | None = None
    features: dict[str, Any] = Field(default_factory=dict)
    context: dict[str, Any] = Field(default_factory=dict)
    derive: bool = True
    return_completed_features: bool = False


class BatchPredictRequest(BaseModel):
    requests: list[PredictRequest]


class HealthResponse(BaseModel):
    status: str
    model_loaded: bool
    api_version: str
    build_sha: str | None = None
    build_source: str | None = None
    model_run_id: str | None = None
    dataset_id: str | None = None
    feature_count: int = 0
    base_row_lookup_enabled: bool = False
    error: str | None = None


class MetadataResponse(BaseModel):
    api_version: str
    build_sha: str | None = None
    build_source: str | None = None
    model_run_id: str
    model_run_dir: str
    feature_table: str | None = None
    dataset_id: str | None = None
    dataset_scale: str | None = None
    model_type: str | None = None
    metrics_summary: dict[str, Any] = Field(default_factory=dict)
    feature_count: int
    feature_columns: list[str]
    supported_labels: list[int]
    base_row_lookup_enabled: bool


class PredictResponse(BaseModel):
    mode: str = "live_model_inference"
    model_run_id: str
    feature_row_id: str | None = None
    predicted_label: int
    p_large_training: float
    severity_score: float
    negative_certification_confidence: float
    integrity_warning: bool
    capacity_possible: bool
    min_critical_coverage: float
    probabilities: list[float]
    probability_by_label: dict[str, float]
    raw_probability_by_label: dict[str, float] = Field(default_factory=dict)
    top_evidence: list[str]
    critical_missing_layers: list[str]
    input_warnings: list[str] = Field(default_factory=list)
    debug_warnings: list[str] = Field(default_factory=list)
    completed_features: dict[str, Any] = Field(default_factory=dict)


class BatchPredictResponse(BaseModel):
    mode: str = "live_model_inference_batch"
    model_run_id: str
    predictions: list[PredictResponse]