Keysat
2b0abad68e
Add a single-session AI flow alongside program generation: describe a
workout in plain words and get a ready-to-log workout back — exercises
with suggested weights, target reps, and set counts grounded in the
user's recent history. The suggestion can be inline-edited or refined
by sending a follow-up instruction back to the model, then "Use this
workout" pre-fills the normal New Workout form (nothing persists until
the user saves through the regular path).
Why reuse, not fork: the existing program-generation spine (detached
background runner, SSE streaming, lenient-JSON preview, 5 providers,
history context, library name->id mapping) already does the hard parts.
A new AIGeneration.kind discriminant ("program" | "workout", default
"program" via boot-time guarded ALTER) selects the parser and keeps the
ephemeral workout rows out of the program-shaped AI history. Refine is a
fresh generation seeded with the prior suggestion (validated through the
same schema before it re-enters the prompt).
Hand-off is sessionStorage -> /main/workouts/new?from=ai -> AiWorkoutPrefill,
which expands each suggestion into N sets and maps effort by cardio-ness
(Gear for cardio, RPE for strength). EditWorkoutData.id is now optional so
the prefill CREATEs rather than PATCHing a nonexistent id. The AI suggests
each weight in that exercise's effective logging unit (the library JSON
carries a per-exercise unit) so the stored number and unit never diverge.
Built + sideloaded to immense-voyage.local as 1.2.0:6; on-box ALTER and
non-root launch confirmed via start-cli. tsc clean (app + packaging),
251 tests pass, next build + s9pk build succeed.
79 lines
2.5 KiB
TypeScript
79 lines
2.5 KiB
TypeScript
import { isCardioExercise } from '@/lib/exerciseOptions';
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/**
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* The ephemeral draft the "today's workout" flow hands to the New Workout
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* form (via sessionStorage). One entry per exercise, with a working set
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* count plus a single target weight/reps that we expand into N identical
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* pre-filled sets. Shared by the producer (GenerateWorkoutClient) and the
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* consumer (AiWorkoutPrefill) so the shape stays in sync.
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*/
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export interface AiWorkoutDraftExercise {
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exerciseId: string;
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sets: number;
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reps?: number;
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suggestedWeight?: number;
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suggestedWeightUnit?: 'lbs' | 'kg';
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rpe?: number;
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gear?: number;
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durationSeconds?: number;
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notes?: string;
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}
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export interface AiWorkoutDraft {
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name: string;
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notes?: string;
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exercises: AiWorkoutDraftExercise[];
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}
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export interface PrefillSet {
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setNumber: number;
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reps?: number;
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weight?: number;
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rpe?: number;
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gear?: number;
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durationSeconds?: number;
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notes?: string;
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}
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export interface PrefillExercise<E> {
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exercise: E;
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sets: PrefillSet[];
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}
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/** Default working sets when the model omits a positive count. */
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const DEFAULT_SET_COUNT = 3;
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/**
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* Expand a draft into pre-filled exercises against the user's library.
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*
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* - Exercises whose `exerciseId` isn't in the library are dropped (the
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* preview forces the user to map them first, so this is just defensive).
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* - Each exercise becomes `sets` identical SetLogs seeded with the
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* suggested weight/reps.
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* - Effort follows the app convention: cardio logs `gear` (1-5), every
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* other exercise logs `rpe`. We keep only the matching one so a stray
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* value on the wrong kind never reaches the form.
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* - The coaching note rides only on the first set (avoids N copies).
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*/
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export function buildPrefillExercises<
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E extends { id: string; type?: string | null; muscleGroups?: string | null },
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>(draft: AiWorkoutDraft, exercises: E[]): PrefillExercise<E>[] {
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const byId = new Map(exercises.map((e) => [e.id, e]));
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const out: PrefillExercise<E>[] = [];
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for (const d of draft.exercises) {
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const exercise = byId.get(d.exerciseId);
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if (!exercise) continue;
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const cardio = isCardioExercise(exercise);
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const setCount = d.sets && d.sets > 0 ? d.sets : DEFAULT_SET_COUNT;
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const sets: PrefillSet[] = Array.from({ length: setCount }, (_, i) => ({
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setNumber: i + 1,
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reps: d.reps,
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weight: d.suggestedWeight,
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rpe: cardio ? undefined : d.rpe,
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gear: cardio ? d.gear : undefined,
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durationSeconds: d.durationSeconds,
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notes: i === 0 ? d.notes : undefined,
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}));
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out.push({ exercise, sets });
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}
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return out;
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}
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