"""
application/dto/prediction_dto.py
───────────────────────────────────
Pydantic DTO for BP prediction responses.
"""
from __future__ import annotations

from datetime import datetime
from typing import Optional

from pydantic import BaseModel, Field


class PredictionResponse(BaseModel):
    """
    Outgoing payload returned to the frontend for a blood pressure prediction.

    Derived from the BPPrediction domain entity, but formatted for API consumers.
    """

    id: str = Field(description="UUID of the prediction record.")
    ppg_signal_id: str = Field(description="UUID of the source PPG signal.")
    predicted_sbp: float = Field(description="Predicted Systolic Blood Pressure (mmHg).")
    predicted_dbp: float = Field(description="Predicted Diastolic Blood Pressure (mmHg).")
    predicted_ecg: Optional[list] = Field(
        default=None,
        description=(
            "Synthetic ECG signal windows produced by CardioGAN. "
            "Format: list of segments, each segment is a list of float samples (224 samples @ 125 Hz)."
        ),
    )
    mean_arterial_pressure: float = Field(description="Computed MAP: DBP + (SBP-DBP)/3 (mmHg).")
    pulse_pressure: float = Field(description="SBP - DBP (mmHg).")
    hypertension_stage: str = Field(
        description="Classification: Normal | Elevated | Stage 1 | Stage 2 | Crisis"
    )
    model_version: str = Field(description="Model version that produced this prediction.")
    inference_time_ms: float = Field(description="Wall-clock inference duration (ms).")
    sa_log: Optional[dict] = Field(
        default=None,
        description="Logs of the Simulated Annealing optimization process.",
    )
    created_at: datetime = Field(description="UTC timestamp of when the prediction was made.")

    model_config = {
        "json_schema_extra": {
            "example": {
                "id": "7ba85f64-5717-4562-b3fc-2c963f66afa9",
                "ppg_signal_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
                "predicted_sbp": 118.5,
                "predicted_dbp": 76.2,
                "predicted_ecg": [[0.12, -0.05, 0.33], [0.09, -0.11, 0.28]],
                "mean_arterial_pressure": 90.3,
                "pulse_pressure": 42.3,
                "hypertension_stage": "Normal",
                "model_version": "gan-vgtlnet-v1.0",
                "inference_time_ms": 142.7,
                "created_at": "2026-05-30T12:00:00Z",
            }
        }
    }


class PredictionHistoryResponse(BaseModel):
    """Paginated list of predictions for a user."""

    user_id: str
    total: int
    predictions: list[PredictionResponse]

    @classmethod
    def from_predictions(
        cls,
        user_id: str,
        predictions: list[PredictionResponse],
    ) -> "PredictionHistoryResponse":
        return cls(user_id=user_id, total=len(predictions), predictions=predictions)


class DateRangeRequest(BaseModel):
    """Optional query parameters for date-range filtering."""

    start: Optional[datetime] = Field(default=None, description="Start of range (UTC).")
    end: Optional[datetime] = Field(default=None, description="End of range (UTC).")
    limit: int = Field(default=50, ge=1, le=500, description="Max records to return.")
    offset: int = Field(default=0, ge=0, description="Records to skip.")