// Command seedsample inserts synthetic activities, laps, and workout kinds
// directly into the SQLite database, then runs them through the real
// classification engine -- so the frontend (Dashboard/ReviewQueue/
// WorkoutKinds) can be visually verified with realistic data without a live
// Garmin account.
package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"time"

	"smartrun/backend/internal/classify"
	"smartrun/backend/internal/garmin/mock"
	"smartrun/backend/internal/store"
	appsync "smartrun/backend/internal/sync"
)

func main() {
	dbPath := flag.String("db", "smartrun_sample.db", "path to the SQLite database to seed")
	flag.Parse()

	ctx := context.Background()
	db, err := store.Open(*dbPath)
	if err != nil {
		log.Fatalf("open db: %v", err)
	}
	defer db.Close()

	// Set a max heart rate on the profile so avg_hr_pct_max is computed
	// during classification below (it's nil/unset by default).
	profile, err := db.GetProfile(ctx)
	must(err)
	maxHR := 190.0
	profile.MaxHeartRate = &maxHR
	must(db.UpdateProfile(ctx, profile))

	// Easy Run and Tempo's pace/HR ranges deliberately overlap a little
	// (330-340 sec/km, 0.70-0.85 HR%max) so a run that lands in that overlap
	// zone demonstrates the ambiguous-multi-match review path, not just a
	// gap between disjoint ranges. The taxonomy migration already seeded
	// these rows (by fixed name) -- update their rules in place rather than
	// creating new ones, since names are unique.
	easyID := mustFindKindID(ctx, db, "Easy")
	must(db.UpdateWorkoutKind(ctx, store.WorkoutKind{
		ID: easyID, Name: "Easy", Description: "Easy conversational runs", Color: "#22c55e",
		RuleJSON: `{"match":"all","conditions":[
			{"metric":"avg_pace_sec_per_km","op":"between","value":[330,420]},
			{"metric":"avg_hr_pct_max","op":"<=","value":0.85}
		]}`,
		IsActive: true,
	}))

	tempoID := mustFindKindID(ctx, db, "Tempo")
	must(db.UpdateWorkoutKind(ctx, store.WorkoutKind{
		ID: tempoID, Name: "Tempo", Description: "Comfortably hard sustained effort", Color: "#f59e0b",
		RuleJSON: `{"match":"all","conditions":[
			{"metric":"avg_pace_sec_per_km","op":"between","value":[300,340]},
			{"metric":"avg_hr_pct_max","op":">=","value":0.70}
		]}`,
		IsActive: true,
	}))

	intervalID := mustFindKindID(ctx, db, "Intervals")
	must(db.UpdateWorkoutKind(ctx, store.WorkoutKind{
		ID: intervalID, Name: "Intervals", Description: "Structured work/rest intervals", Color: "#ef4444",
		RuleJSON: `{"match":"all","conditions":[{"metric":"lap_interval_pattern","op":"==","value":true}]}`,
		IsActive: true,
	}))

	m := &mock.Client{}
	svc := appsync.NewService(m, db, appsync.Config{MinConfidence: 0.6}, nil)

	today := time.Now()
	activityIDs := []int64{}

	// 5 Easy runs, pace/HR trending slightly faster over time (progression).
	for i := 0; i < 5; i++ {
		start := today.AddDate(0, 0, -60+i*10)
		speed := 1000.0 / (390 - float64(i)*5) // pace improving from 390 -> 370 sec/km
		hr := 125.0 + float64(i)               // pct-of-max stays comfortably under the 0.75 ceiling
		id := seedActivity(ctx, db, seedParams{
			garminID: 1000 + int64(i), name: "Easy morning run", start: start,
			distance: 8000, duration: 8000 / (speed) * 1, speedMps: speed, avgHR: hr,
			aerobicTE: 2.5, anaerobicTE: 0.3,
		})
		activityIDs = append(activityIDs, id)
	}

	// 3 Tempo runs.
	for i := 0; i < 3; i++ {
		start := today.AddDate(0, 0, -45+i*15)
		speed := 1000.0 / 320.0 // centered in Tempo's [300,340] range
		id := seedActivity(ctx, db, seedParams{
			garminID: 2000 + int64(i), name: "Tempo run", start: start,
			distance: 6000, duration: 1800, speedMps: speed, avgHR: 168,
			aerobicTE: 3.8, anaerobicTE: 1.2,
		})
		activityIDs = append(activityIDs, id)
	}

	// 1 Interval workout, with alternating ACTIVE/REST laps + HR samples
	// showing drift on the work intervals and recovery on the rest ones.
	{
		id := seedActivity(ctx, db, seedParams{
			garminID: 3000, name: "Track intervals", start: today.AddDate(0, 0, -5),
			distance: 8000, duration: 2400, speedMps: 1000.0 / 240.0, avgHR: 165,
			aerobicTE: 3.0, anaerobicTE: 3.5,
		})
		seedIntervalLapsAndSamples(ctx, db, id)
		activityIDs = append(activityIDs, id)
	}

	// 1 ambiguous run: pace 335 sec/km sits inside both Easy's [330,420] and
	// Tempo's [300,340] ranges, and HR% (0.78) satisfies both Easy's <=0.85
	// and Tempo's >=0.70 -- so it should land in the review queue with two
	// candidates, not a clean single match.
	{
		id := seedActivity(ctx, db, seedParams{
			garminID: 4000, name: "Ambiguous run", start: today.AddDate(0, 0, -2),
			distance: 7000, duration: 2200, speedMps: 1000.0 / 335.0, avgHR: 148,
			aerobicTE: 3.0, anaerobicTE: 0.8,
		})
		activityIDs = append(activityIDs, id)
	}

	for _, id := range activityIDs {
		must(svc.ClassifyActivity(ctx, id))
	}

	fmt.Printf("Seeded %d activities (kinds: Easy=%d, Tempo=%d) into %s\n", len(activityIDs), easyID, tempoID, *dbPath)
}

type seedParams struct {
	garminID    int64
	name        string
	start       time.Time
	distance    float64
	duration    float64
	speedMps    float64
	avgHR       float64
	aerobicTE   float64
	anaerobicTE float64
}

func seedActivity(ctx context.Context, db *store.DB, p seedParams) int64 {
	speed := p.speedMps
	hr := p.avgHR
	aerobic := p.aerobicTE
	anaerobic := p.anaerobicTE
	id, err := db.UpsertActivity(ctx, store.Activity{
		GarminActivityID:        p.garminID,
		StartTimeUTC:            p.start.Format("2006-01-02 15:04:05"),
		DurationSeconds:         p.duration,
		DistanceMeters:          p.distance,
		AvgSpeedMps:             &speed,
		AvgHR:                   &hr,
		AerobicTrainingEffect:   &aerobic,
		AnaerobicTrainingEffect: &anaerobic,
		// ActivityName/ActivityType aren't stored columns anymore -- the API
		// decodes them from RawJSON, so seeded activities need them here to
		// still show a sensible name/type in the dev UI.
		RawJSON: fmt.Sprintf(`{"activityName":%q,"activityType":{"typeKey":"running"}}`, p.name),
	})
	must(err)
	return id
}

// seedIntervalLapsAndSamples gives one activity 6 alternating ACTIVE/REST
// laps plus per-second HR samples: rising HR within each ACTIVE lap (drift)
// and falling HR within each REST lap (recovery).
func seedIntervalLapsAndSamples(ctx context.Context, db *store.DB, activityID int64) {
	var laps []store.Lap
	var samples []store.Sample
	elapsed := 0.0
	baseHR := 140.0

	for i := 0; i < 6; i++ {
		isActive := i%2 == 0
		lapDuration := 180.0
		intensity := "REST"
		if isActive {
			intensity = "ACTIVE"
		}

		var lapSamples []classify.SampleInfo
		for s := 0.0; s < lapDuration; s += 5 {
			var hr float64
			if isActive {
				hr = baseHR + s/10 // rising through the work interval
			} else {
				hr = baseHR + 20 - s/8 // falling through the rest interval
			}
			samples = append(samples, store.Sample{
				ElapsedSeconds: elapsed + s,
				TimestampMs:    int64((elapsed + s) * 1000),
				HeartRate:      &hr,
			})
			lapSamples = append(lapSamples, classify.SampleInfo{ElapsedSeconds: elapsed + s, HeartRate: &hr})
		}

		var drift, recovery *float64
		if isActive {
			if v, ok := classify.HRDrift(lapSamples); ok {
				drift = &v
			}
		} else {
			if v, ok := classify.HRRecovery(lapSamples); ok {
				recovery = &v
			}
		}

		laps = append(laps, store.Lap{
			LapIndex:      i + 1,
			IntensityType: intensity,
			// DurationSeconds/AvgHR aren't stored columns anymore -- the API
			// decodes them from RawJSON, so seeded laps need them here to
			// still show a sensible duration/HR on the dev chart.
			RawJSON:             fmt.Sprintf(`{"duration":%v,"distance":400,"averageHR":%v}`, lapDuration, baseHR),
			HRDriftBpmPerMin:    drift,
			HRRecoveryBpmPerMin: recovery,
		})
		elapsed += lapDuration
	}

	must(db.ReplaceActivitySamples(ctx, activityID, samples))
	must(db.ReplaceLaps(ctx, activityID, laps))
}

func must(err error) {
	if err != nil {
		log.Fatal(err)
	}
}

func mustFindKindID(ctx context.Context, db *store.DB, name string) int64 {
	kinds, err := db.ListWorkoutKinds(ctx, false)
	must(err)
	for _, k := range kinds {
		if k.Name == name {
			return k.ID
		}
	}
	log.Fatalf("seedsample: no workout kind named %q found (did migration 0004 run?)", name)
	return 0
}