package staffing

import (
	"context"
	"sort"
	"strings"
	"time"

	"github.com/google/uuid"
	"github.com/jackc/pgx/v5"

	"github.com/missionworkforce/missionmatch/internal/modules/matching"
	"github.com/missionworkforce/missionmatch/internal/modules/matching/engine"
	"github.com/missionworkforce/missionmatch/internal/platform/database/dbgen"
)

// RecomputeProactive refreshes the proactive bench watch. For each available candidate it scores the open
// requirements, keeps the best fits at or above the staffing floor, and writes a fresh proactive run for
// that candidate, superseding their prior pending proactive rows. It is the scheduled job behind the host
// timer (the same pattern as mm-opp-sync), idempotent, and it writes deterministic rationales (no model
// call in the batch, so the job is fast and never floods the inference plane). It returns the number of
// candidates that produced at least one fit.
//
// The freshness of the watch depends on the ingestion timers: a newly posted opportunity only unlocks a
// fit once the opportunity and requirement rows exist. The build report notes this dependency.
func (s *Service) RecomputeProactive(ctx context.Context) (int, error) {
	if s.db == nil {
		return 0, ErrNoDatabase
	}
	orgID, err := s.orgID(ctx)
	if err != nil {
		return 0, err
	}
	cfg := s.cfg
	asOf := s.clock()

	type job struct {
		candID    uuid.UUID
		recs      []Recommendation
		resByPair map[string]engine.MatchResult
	}
	var jobs []job
	var valueSource string

	err = s.db.RunInTenantTx(ctx, func(ctx context.Context, tx pgx.Tx) error {
		q := dbgen.New(tx)
		openRows, e := q.ListOpenRequirementsWithValue(ctx, dbgen.ListOpenRequirementsWithValueParams{Limit: openRequirementCap, Offset: 0})
		if e != nil {
			return e
		}
		_, valueSource = buildPriorities(cfg, openRows) // proactive does not weight by dollars, but record the source

		type reqE struct {
			id string
			er engine.Requirement
		}
		reqs := make([]reqE, 0, len(openRows))
		for _, rr := range openRows {
			er, e := matching.LoadEngineRequirement(ctx, q, rr.ID)
			if e != nil {
				return e
			}
			reqs = append(reqs, reqE{id: rr.ID.String(), er: er})
		}

		cands, e := q.ListCandidates(ctx, dbgen.ListCandidatesParams{Limit: benchScanLimit, Offset: 0})
		if e != nil {
			return e
		}
		for _, c := range cands {
			if !c.Available {
				continue // the bench watch is for candidates available to staff
			}
			ec, e := matching.LoadEngineCandidate(ctx, q, c, asOf)
			if e != nil {
				return e
			}
			if !ec.Available {
				continue
			}
			type fit struct {
				reqID string
				res   engine.MatchResult
			}
			var fits []fit
			for _, r := range reqs {
				res := engine.ScoreCandidate(ctx, ec, r.er, s.matcher)
				if res.Total.GreaterThanOrEqual(cfg.StaffingFloor) {
					fits = append(fits, fit{reqID: r.id, res: res})
				}
			}
			if len(fits) == 0 {
				continue
			}
			sort.SliceStable(fits, func(i, j int) bool {
				if d := fits[i].res.Total.Cmp(fits[j].res.Total); d != 0 {
					return d > 0
				}
				return fits[i].reqID < fits[j].reqID
			})
			if len(fits) > cfg.ProactiveBestFitLimit {
				fits = fits[:cfg.ProactiveBestFitLimit]
			}
			cid := c.ID.String()
			prov := c.ClearanceProvenance
			comp := completeness(ec)
			recs := make([]Recommendation, 0, len(fits))
			rbp := make(map[string]engine.MatchResult, len(fits))
			for i, f := range fits {
				conf := Confidence(cfg, ScoredCandidate{Total: f.res.Total, Provenance: prov, Completeness: comp})
				recs = append(recs, Recommendation{
					RequirementID: f.reqID,
					CandidateID:   cid,
					Action:        ActionAssign,
					Rank:          i + 1,
					Confidence:    conf,
					Evidence: Evidence{
						EngineTotal: f.res.Total.StringFixed(2),
						Band:        f.res.Band.Code(),
						Trigger:     &TriggerChange{Kind: "recompute", DetectedAt: asOf.Format(time.RFC3339)},
					},
				})
				rbp[pairKey(cid, f.reqID)] = f.res
			}
			jobs = append(jobs, job{candID: c.ID, recs: recs, resByPair: rbp})
		}
		return nil
	})
	if err != nil {
		return 0, err
	}

	processed := 0
	for _, j := range jobs {
		enriched := enrichDeterministic(j.recs)
		scope := j.candID
		candLock := j.candID
		e := s.db.RunInTenantTx(ctx, func(ctx context.Context, tx pgx.Tx) error {
			// Serialize the proactive recompute for THIS candidate so overlapping cron invocations cannot
			// leave two live pending sets for one candidate.
			if le := acquireAdvisoryLock(ctx, tx, advisoryKey(ViewProactive, orgID, candLock.String())); le != nil {
				return le
			}
			_, _, perr := s.persistRun(ctx, tx, orgID, ViewProactive, &scope, runParams(cfg, valueSource), "staffing.proactive", enriched, j.resByPair,
				func(ctx context.Context, q *dbgen.Queries) error {
					cp := candLock
					return q.SupersedePendingProactive(ctx, &cp)
				})
			return perr
		})
		if e != nil {
			return processed, e
		}
		processed++
	}
	return processed, nil
}

// enrichDeterministic narrates each recommendation with the deterministic template only, no model call.
func enrichDeterministic(recs []Recommendation) []enrichedRec {
	out := make([]enrichedRec, 0, len(recs))
	for _, r := range recs {
		rationale := strings.Join(evidenceStatements(r.Action, r.Evidence), " ")
		if rationale == "" {
			rationale = "Recommended by the deterministic engine."
		}
		r.Evidence.FaithFallbackReason = "proactive_batch"
		out = append(out, enrichedRec{rec: r, rationale: rationale, source: RationaleDeterministic})
	}
	return out
}