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.
71 lines
2.3 KiB
TypeScript
71 lines
2.3 KiB
TypeScript
'use client';
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import { useEffect, useState } from 'react';
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import { Exercise } from '@prisma/client';
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import WorkoutForm, { EditWorkoutData } from './WorkoutForm';
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import { AI_WORKOUT_DRAFT_KEY } from '@/components/ai/GenerateWorkoutClient';
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import { buildPrefillExercises, type AiWorkoutDraft } from '@/lib/ai/workoutDraft';
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/**
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* Reads the ephemeral AI workout draft from sessionStorage (stashed by
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* GenerateWorkoutClient before navigating to /main/workouts/new?from=ai),
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* expands each suggested exercise into N pre-filled SetLogs, and renders
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* the normal WorkoutForm. Nothing is persisted until the user saves
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* through the regular workout path.
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*
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* The draft has no workout id, so WorkoutForm's first save CREATEs.
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* Effort follows the app convention: cardio → gear (1-5), else → rpe.
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*
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* If the draft is missing (e.g. a refresh cleared it), we fall back to a
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* blank form so the page is never broken.
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*/
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export default function AiWorkoutPrefill({
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exercises,
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}: {
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exercises: Exercise[];
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}) {
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// Read + build once on mount via a lazy initializer. This stays PURE
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// (no sessionStorage mutation) so React's StrictMode double-invoke is
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// safe — both passes read the same draft. The one-shot removal happens
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// in the effect below, after the value is captured.
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const [editWorkout] = useState<EditWorkoutData | undefined>(() => {
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if (typeof window === 'undefined') return undefined;
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const raw = sessionStorage.getItem(AI_WORKOUT_DRAFT_KEY);
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if (!raw) return undefined;
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let draft: AiWorkoutDraft;
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try {
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draft = JSON.parse(raw);
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} catch {
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return undefined;
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}
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const builtExercises: EditWorkoutData['exercises'] =
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buildPrefillExercises(draft, exercises);
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if (builtExercises.length === 0) return undefined;
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return {
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// No id → first save CREATEs a new workout.
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name: draft.name || '',
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date: new Date().toISOString(),
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notes: draft.notes,
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exercises: builtExercises,
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};
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});
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// Clear the one-shot draft after mount so a manual reload starts blank.
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// removeItem is idempotent, so StrictMode's double-run is harmless.
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useEffect(() => {
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sessionStorage.removeItem(AI_WORKOUT_DRAFT_KEY);
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}, []);
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return (
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<WorkoutForm
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exercises={exercises}
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recentlyUsedExercises={[]}
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editWorkout={editWorkout}
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/>
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);
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}
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