Arnaud
f0340859fa
The photo-import flow used one vision-capable model to both read the photo and structure the full recipe (quantities, steps, timing) in a single call. Split it into two: a vision model recognizes what's in the picture, then a text model reconstructs the recipe from that description — same pattern the pantry photo-scan already uses for recognition, and lets structuring use whichever model is actually configured for text generation. Still counts as one AI-quota unit. v0.37.0
31 lines
1.5 KiB
TypeScript
31 lines
1.5 KiB
TypeScript
import type { AiConfig } from "../factory";
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import { recognizePhoto } from "./recognize-photo";
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import { generateRecipeFromRecognition, type GeneratedRecipeFromRecognition } from "./generate-recipe-from-recognition";
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export type ImportedRecipe =
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| { found: false; recipeType: "dish" | "drink"; title: string; ingredients: []; steps: [] }
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| ({ found: true; recipeType: "dish" | "drink" } & GeneratedRecipeFromRecognition);
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/** Orchestrates the two-step photo-import flow: a vision model recognizes
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* what's in the photo (`recognizePhoto`), then a text model reconstructs the
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* full structured recipe from that recognition (`generateRecipeFromRecognition`).
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* Splitting the call lets each step use the model best suited to it — vision
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* for the photo, text for structuring — instead of one model doing both. */
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export async function importFromPhoto(
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imageBase64: string,
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mimeType: "image/jpeg" | "image/png" | "image/webp",
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visionConfig: AiConfig | undefined,
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textConfig: AiConfig | undefined,
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locale?: string,
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): Promise<ImportedRecipe> {
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const recognition = await recognizePhoto(imageBase64, mimeType, visionConfig);
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if (!recognition.found) {
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return { found: false, recipeType: recognition.recipeType, title: recognition.title, ingredients: [], steps: [] };
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}
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const generated = await generateRecipeFromRecognition(recognition, { ...textConfig, language: locale ?? "en" });
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return { found: true, recipeType: recognition.recipeType, ...generated };
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}
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