Backend:
- Prisma: add stripeSubscriptionId, subscriptionStatus, priceId,
currentPeriodEnd to User + migration SQL
- plugins/stripe.ts: getPlans catalog with env-based price IDs
- server.ts: raw body JSON parser for webhook signature verification,
skip rate limit on /stripe/webhook
- types/fastify.d.ts: declare rawBody on FastifyRequest
- routes/stripe.ts (new):
- GET /stripe/plans public
- GET /stripe/subscription user status
- POST /stripe/checkout hosted Checkout Session, lazy-creates
customer, dynamic payment methods, promo codes enabled
- POST /stripe/portal Billing Portal session
- POST /stripe/webhook signature verified, handles
checkout.session.completed, customer.subscription.*,
invoice.payment_failed. Resolves user by clientReferenceId,
metadata.userId, or stripeId fallback
- .env.example + README: Stripe setup, stripe CLI, test cards
Frontend:
- api/stripe.ts typed client (getPlans, getSubscription,
startCheckout, openPortal)
- pages/Pricing.tsx: 3-card grid (free/essentiel/premium) with
popular badge, current plan indicator, gradient popular card
- pages/CheckoutSuccess.tsx: animated confirmation with polling on
/stripe/subscription until webhook activates plan
- pages/Profile.tsx: SubscriptionCard above tabs — free users see an
upgrade banner, paid users see plan + status + next billing date
+ 'Gérer l'abonnement' button opening Customer Portal
- components/header.tsx: 'Tarifs' link in nav
- App.tsx: /pricing (public) and /checkout/success (protected) routes
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Backend:
- Prisma: add user preferences (dietaryPreference, allergies, maxCookingTime,
equipment, cuisinePreference, servingsDefault) + migration SQL.
Also make stripeId nullable so signup works without Stripe.
- prompts.ts: buildUserPrompt now takes a BuildPromptOptions with preferences.
Injects strong, explicit constraints in the user message (vegan rules,
allergy warnings, time limits, equipment availability, cuisine hints).
- recipe-generator.ts: new streamRecipe() async generator. Streams OpenAI
chat completion with json_schema strict mode, parses the growing buffer
to detect 'titre' and 'description' early, yields typed events:
{ type: 'delta' | 'title' | 'description' | 'complete' }
Final event includes the parsed StructuredRecipe + cost log.
- recipes.ts route: new POST /recipes/create-stream returning SSE:
event: progress { step }
event: transcription{ text }
event: title { title } <- triggers parallel image gen
event: description { description }
event: recipe { recipe }
event: image { url }
event: saved { id }
event: done
Heartbeat every 15s to prevent proxy timeouts. Image generation is
kicked off the moment the title is extracted, running in parallel with
the rest of the recipe stream. Legacy POST /recipes/create still works
and now also passes user preferences.
- users.ts route: GET /profile now returns preferences (equipment
deserialized from JSON). New PUT /users/preferences with validation
(diet enum, time 5-600, servings 1-20, equipment array -> JSON).
- ai.ts plugin: generateRecipe signature extended to accept preferences.
Frontend:
- api/recipe.ts: createRecipeStream() async generator that consumes SSE
via fetch + ReadableStream + TextDecoder (EventSource can't do POST).
Parses 'event:' and 'data:' lines, yields typed StreamEvent union.
- api/auth.ts: User interface extended with preferences; new
UserPreferences type exported.
- api/user.ts: updatePreferences() method.
- RecipeForm.tsx: handleSubmit now consumes the stream. Live UI displays:
1. Initial cooking loader with step label
2. Transcription appears as soon as it's ready
3. Title fades in the moment it's extracted (before the rest of the
recipe finishes generating)
4. Description appears right after
5. Image replaces the loader when ready
6. Small delay before navigating to the saved recipe detail page
- Profile.tsx: new 'Cuisine' tab with form for diet, allergies, max time,
servings, cuisine preference, and equipment checkboxes (8 options).
UX improvement: perceived latency is dramatically reduced. Instead of
waiting 40s staring at a spinner, the user sees the title ~3-4s in and
can start reading, while the image finishes generating in parallel.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
