package models

import "time"

// DemandForecast is one zone-day the engine expects.
//
// ─── Why this is stored rather than computed on read ───────────────────────
//
// The forecast is produced in Python (AI_engine/prediction), because the model
// is: Prophet where it beats a seasonal baseline, the baseline otherwise. The
// console and any staffing decision need it in Go. So the engine writes here
// and the backend serves it — the same split the agent registry and the
// decision log already use, and the reason the /internal/* surface exists.
//
// It is also the honest shape: a forecast is a thing produced at a moment by a
// model, not a function of the current table. Recomputing it on every read
// would make yesterday's number unrecoverable, which is exactly what you want
// when asking "was the forecast any good".
type DemandForecast struct {
	Forecastid int `json:"forecastid" gorm:"primaryKey;column:forecastid;autoIncrement"`

	// Zone is the first three digits of a pickup pincode — the same grain the
	// hub console scopes on (pickuppincode LIKE '641%') and the same grain
	// internal/prediction calibrates ETA at. Keeping one definition of "zone"
	// across both is deliberate.
	Zone string `json:"zone" gorm:"column:zone;size:8;not null;uniqueIndex:uq_demandforecast_zone_day,priority:1"`

	// Forday is the day being predicted, not the day it was predicted on.
	Forday time.Time `json:"forday" gorm:"column:forday;not null;uniqueIndex:uq_demandforecast_zone_day,priority:2;index"`

	Expectedbookings int `json:"expectedbookings" gorm:"column:expectedbookings;not null"`

	// Model and Reason record WHICH model produced this and why it was chosen —
	// "prophet beat the weekly baseline over 12 folds (18% lower MAE)", or
	// "prophet is not installed in this image". Stored because the choice is
	// made per zone from that zone's own history, so without it nobody can tell
	// whether a bad forecast came from a bad model or from a thin series.
	Model  string `json:"model" gorm:"column:model;size:32;not null"`
	Reason string `json:"reason" gorm:"column:reason"`

	// Observations is how many days of history the forecast was fitted on, and
	// Baselinemae/Modelmae are the backtest scores. A forecast with 31
	// observations and a model barely beating the baseline deserves less trust
	// than one with 400, and this is what lets a reader see that rather than
	// taking the number at face value.
	Observations int      `json:"observations" gorm:"column:observations;default:0"`
	Baselinemae  *float64 `json:"baselinemae" gorm:"column:baselinemae"`
	Modelmae     *float64 `json:"modelmae" gorm:"column:modelmae"`

	Generatedat time.Time `json:"generatedat" gorm:"column:generatedat;not null"`
}

func (DemandForecast) TableName() string { return "demandforecast" }