app/models/schemas.py

from datetime import datetime
from typing import List, Optional

try:
    from pydantic import BaseModel, Field, ConfigDict
    _CONFIG_DICT_SUPPORTED = True
except ImportError:  # pragma: no cover - Pydantic v1 fallback
    from pydantic import BaseModel, Field  # type: ignore
    _CONFIG_DICT_SUPPORTED = False


class ECGInferenceRequest(BaseModel):
    patient_id: str = Field(..., example="patient-123")
    signal: List[float] = Field(..., min_items=1, example=[0.1, 0.2, 0.3])
    device_id: Optional[str] = Field(None, example="device-abc")
    sampling_rate: Optional[float] = Field(None, gt=0, example=250.0)
    age: Optional[int] = Field(None, ge=0, example=70)
    has_prior_stroke: Optional[bool] = Field(None, example=False)


class ECGInferenceResponse(BaseModel):
    patient_id: str
    label: str
    score: float
    alert_level: str
    hr: Optional[int] = None
    sample_id: int
    created_at: datetime
    explanations: List[str] = Field(default_factory=list)

    if _CONFIG_DICT_SUPPORTED:  # type: ignore
        model_config = ConfigDict(from_attributes=True)  # type: ignore
    else:
        class Config:
            orm_mode = True


class DashboardStats(BaseModel):
    time_window_hours: int
    total_samples: int
    unique_patients: int
    alert_distribution: dict
    label_distribution: dict
    avg_score: float
    estimated_energy_savings_pct: float
    timestamp: str


class PatientSummary(BaseModel):
    patient_id: str
    latest_label: Optional[str]
    latest_score: float
    latest_alert_level: Optional[str]
    latest_hr: Optional[int]
    last_updated: str
    total_samples: int
    alert_count: int


class AlertSummary(BaseModel):
    sample_id: int
    patient_id: str
    alert_level: str
    label: Optional[str]
    score: float
    hr: Optional[int]
    timestamp: str