SCRIPT pour générer le doc de contexte pour Gemini : node src/tools/generate-context.js
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IMAGES/INGREDIENTS VS /RECIPIENTS ET COMMENT JE M'Y RÉFÈRE COMPLEXIFIE LE CODE JSON
Me : Refining the AI prompt for initial generation is where I've invested a lot of time already, and this approach is a good first draft, but rarely gives me results above 80%. Actually, the Decap or Sanity approach sound nice, but I'd quickly be underwhelmed. You see, I don't need to be the recipe builder to be "part" of the website. yes it will call upon the ingredients/recipients in the same folders, but ultimately, it doesn't have to be web-based. It could run on Rust, Python or whatever makes more sense to create a Canva-like experience.
What I would really want it to do is in a first time to start from the natural recipe and extract keywords for potential images that would be used on this recipe this would be 80% of the ingredients in recipients or maybe even 95% because I think that would be easy to do after that I have an interface where I can choose what each step looks like there would be 5 or 6 cases that represent 95% of what I need.
Actually it could start with the output of the AI work (arount 80%), or any Json file That is perfectly structured as one of my recipes. then I would build from there with the interface allowing me to add ingredients add actions left or right add recipients text notes Badges and so on that would just have this empty field waiting with a little plus sign in there and then I could lock and unlock steps this would be such a great tool I need this tool.
so yeah tell me based on what I just said how I could build this what are our options as far as technology stack and approach goes.
The Data Structure Roadblock Part of the friction you are experiencing is indeed due to the current data structure. Grouping images into rigid semantic folders (/ingredients, /recipients, /actions) forces the system to classify items that are inherently fluid.
If a salmon acts as a recipient, the current logic breaks because it expects recipients to live in the /recipients folder.
The Structural Fix: Flatten the image directory for recipe components. Instead of semantic folders, pool everything into a single /images/items/ folder (or just keep /images/ingredients/ and treat it as a universal pool).
An image is just an asset. The JSON should dictate how it is used (as a tile, a recipient, or a HUD element), not the folder structure.
By removing the folder-routing logic, you eliminate the need to specify "folder": "recipients" as an exception in your JSON. A salmon is just salmon.png, whether it sits in the left panel or acts as the base in the right panel.
The Solution: Streamlining Authoring (Option B) If building an automated parser is too complex and manual JSON editing is too slow, the best middle ground is upgrading the authoring experience.
However, building a custom drag-and-drop React interface from scratch is a massive development detour. Instead, you can achieve a highly efficient workflow using a two-pronged approach:
By refining this prompt, you can paste a URL or text from a food blog and have the LLM output 95% accurate JSON, handling the state changes and step splits based on plain-English instructions.
These tools read your JSON schema and automatically generate a clean, user-friendly graphical interface.
You get form fields for steps, toggles for effort/diets, and image upload buttons. It provides the speed and visual feedback of a custom builder without requiring you to code a proprietary drag-and-drop application.
By flattening the image structure and placing a pre-built graphical interface over the JSON, you remove the architectural friction and drastically speed up production.
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What I would really want it to do is in a first time to start from the natural recipe and extract keywords for potential images that would be used on this recipe this would be 80% of the ingredients in recipients or maybe even 95% because I think that would be easy to do after that I have an interface where I can choose what each step looks like there would be 5 or 6 cases that represent 95% of what I need.
Actually it could start with the output of the AI work (arount 80%), or any Json file That is perfectly structured as one of my recipes. then I would build from there with the interface allowing me to add ingredients add actions left or right add recipients text notes Badges and so on that would just have this empty field waiting with a little plus sign in there and then I could lock and unlock steps this would be such a great tool I need this tool.
so yeah tell me based on what I just said how I could build this what are our options as far as technology stack and approach goes. ...
This is a fantastic pivot. You are thinking exactly like a Product Owner here: the LLM gets you 80% of the way there instantly, and instead of fighting the LLM to get that last stubborn 20%, you build a purpose-built GUI to let a human close the gap efficiently.
You are essentially describing a specialized IDE (Integrated Development Environment) for your visual recipes.
To get that highly visual, drag-and-drop, "Canva-like" experience, standard desktop GUI frameworks in Python (like Tkinter or PyQt) or Rust (like egui) are going to feel clunky and take a massive amount of engineering to look and behave the way you want. The fastest, most robust way to build this kind of fluid interface—especially if you want to leverage AI code assistants to help you build it—is to use web technologies (HTML/CSS/JS) but package it as a standalone local application.
Here is how you can architect this tool.
Option A: Local React/Vite App with File System Access (Recommended)
How it works: It runs in your local Chrome or Edge browser, but it uses the modern File System Access API. You give it permission to read/write directly to your Eleventy _data or visual-recipes folders on your hard drive.
Why it makes sense: Extremely lean. No backend, no desktop packaging required. It’s just a local dashboard. Because it uses React (or Vue), building complex drag-and-drop interfaces with libraries like @dnd-kit is highly documented and very AI-friendly to code.
Option B: Tauri (The "Rust" Route)
How it works: Tauri uses Rust for the backend (accessing your files, running heavy processes) and standard web tech (React/Svelte/HTML) for the frontend interface.
Why it makes sense: It compiles into a blazing-fast, lightweight native desktop app (.exe or .app). If you have scripts that need to heavily process images or parse files, Rust handles it beautifully in the background.
Option C: Electron
How it works: Similar to Tauri, but uses Node.js for the backend. It's what apps like VS Code and Figma use for their desktop versions.
Why it makes sense: It is the industry standard. However, it results in a much heavier application size than Tauri and might be overkill for editing JSON and moving images around.
Phase 1: The Importer (The 80% baseline) The app opens, and you either paste the raw LLM output or select an existing JSON file. The app parses this data and loads it into a global "State" (using a tool like Zustand or Redux). This state is the single source of truth while you edit.
Phase 2: The Visual IDE (The 20% human touch) This is the Canva-like interface. It should consist of three main areas:
The Asset Library (Left Sidebar): This reads your local /images/ingredients, /actions, and /recipients folders. It displays them as a visual grid.
The Canvas (Center): This displays your recipe steps.
The Properties Panel (Right Sidebar): When you click an ingredient on the canvas, this panel opens, allowing you to edit the quantity, unit, transformed labels, or add a "From Step X" badge.
Phase 3: The Exporter A simple "Save to Eleventy" button that takes the current global state, formats it strictly into your exact JSON schema, and overwrites the file in your Eleventy project directory.
Instead of an open, chaotic canvas, your builder should use a Slot-Based Architecture:
You click "Add Step".
The app asks: "Which template?" (e.g., Left Panel Ingredients -> Right Panel Heat/Time/Recipient).
The app renders the step with empty slots featuring the "little plus sign" you mentioned.
You drag a red onion from your Asset Library and drop it into an ingredient slot. The app automatically links the filename.
You click the "Lock" icon on the step, which disables drag-and-drop for that row to prevent accidental edits.
This approach gives you the speed of Canva but enforces the rigid structure your static website needs to render perfectly.
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Me : I know its not super agile to think like this, but I'm slightly considering the option of a future where users ca access the app themselves. So web based and fast sounds ideal. I don't think it needs to be that heavy, as truly, it's just an HTML rendering tool with a bunch of constraints (temperature goes here, textboxes, badges work like this, ...)
Thinking a few steps ahead to user-generated content isn't necessarily anti-agile; it is solid product vision. If there is a world where visitors eventually log in to build their own visual recipes, standardizing on a hosted web application from day one makes a lot of sense.
You are entirely right about the nature of the tool. At its core, it isn't a complex physics simulator; it is a highly constrained visual form. You are mapping JSON data (ingredients, heat values, textboxes) to a real-time visual preview.
If we are pivoting from a strictly local desktop tool to a hosted web app that can serve you now and users later, we just need to adjust the mechanics of how the data is saved.
Here is the reality of building it as a web-based tool while keeping the engineering light and fast.
The "Save" Mechanic Pivot The local File System Access API we discussed earlier is strictly for your personal machine. A hosted web app cannot reach into a user's hard drive to read or write files for security reasons.
Therefore, the app needs a different way to handle the JSON data depending on who is using it:
Phase 1 (Just You): The web app features a "Copy JSON" or "Download JSON" button. You use the web interface to quickly build the recipe, download the resulting file, and drop it directly into your Eleventy repository.
Phase 2 (End Users): You wire that same "Save" button to a cloud database.
The Ideal Lean Stack To avoid over-engineering while setting up a foundation that an AI code assistant can easily help you build, you want a widely adopted, lightweight stack.
The Frontend: React (via Vite) React is the industry standard for component-based interfaces. Because your visual recipes are essentially blocks of UI (the left panel, the right panel, the tiles, the chevrons), React handles this state perfectly. When you drag an onion into a slot, React updates the JSON object in the background and instantly repaints the preview. It is incredibly snappy.
The State Management: Zustand To keep track of all the recipe data while you are editing it, you need a "store." Zustand is a tiny, bare-bones state manager for React. It is much simpler to grasp than older tools like Redux, keeping your codebase lean.
The "Future" Backend: Supabase or Firebase When you are ready for Phase 2 (user logins and saving recipes to the cloud), you do not need to write a custom backend from scratch. Backend-as-a-Service tools like Supabase provide a ready-made database and user authentication system that plug directly into a React app.
The Build Strategy You can start building the React interface immediately, hosting it on a hidden URL just for yourself. You can rely on your LLM pipeline to generate the initial 80% JSON draft, paste that raw text into your new web builder, use the visual interface to snap the remaining 20% into place, and download the finished file.
iFrame dimensions VS SEO concerns
Ok... so... my visual recipes (which would be the content of the iframes) are often 3 to 7 screen heights long. Also, recipe pages, across the web, are broadly considered to already be pretty long (that's why the "jump to recipe" button is so prevalent). With that considered, what other options do I have, besides iframes, or creating a whole new page... ? (Also, I don't want to get to carried away, as I'm market-testing this, so we may suffer the imperfections of the iframe for a while)
Your instinct to not get carried away is spot on. In product development, suffering the imperfections of an MVP to get actual market validation is usually the smartest play. Premature optimization is the enemy of launching.
That said, a 3 to 7 viewport-height iframe introduces a specific UX hazard you should be aware of, especially on mobile: scroll trapping. If a user is trying to scroll down the parent page and their thumb lands inside your massive iframe, they will end up scrolling your recipe instead of the page they are on. They get "trapped" until they reach the bottom of your embed.
If you want to move away from a standard, massive inline iframe—either now or in V2—here are the most pragmatic alternatives, ordered from lowest to highest engineering effort:
How it works: You display just the recipe title, the hero image, and the first step. You overlay a fading gradient at the bottom with a "Read Full Recipe" button. When clicked, you use the iframe-resizer script to expand it to its full height.
Why it's good: It completely solves the immediate SEO/CLS concerns and respects the host site's real estate. It only becomes massive when the user explicitly asks it to be.
The MVP factor: You don't have to change your hosting or your site structure; you just add a tiny bit of UI to the embed layer.
How it works: The host site places a small snippet that renders an attractive "View Visual Recipe" card. When clicked, the recipe opens in a full-screen overlay (a modal) that sits on top of the parent page.
Why it's good: This is the ultimate solution for 7-screen-tall content. The user enters a dedicated, distraction-free "recipe mode." When they are done, they hit a close button (or tap outside it), and they are exactly where they left off on the parent article. Zero layout shift, zero scroll trapping.
The MVP factor: This requires writing a bit of JavaScript to handle creating and destroying the modal overlay when the button is clicked.
How it works: You keep the iframe, but you fix its height to exactly one screen (or less). Users click "Next" or swipe left-to-right to advance through the recipe steps.
Why it's good: It acts like an Instagram story or a slideshow embedded in the page. It takes up a predictable amount of vertical space, completely eliminating the 7-screen scrolling problem.
The MVP factor: This requires redesigning the layout of iconicspoon.com/embeds/* to support horizontal pagination rather than vertical scrolling.