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
45 lines
2.2 KiB
TypeScript
45 lines
2.2 KiB
TypeScript
import { generateObject } from "ai";
|
|
import { z } from "zod";
|
|
import { resolveModel, type AiConfig } from "../factory";
|
|
|
|
const PhotoRecognitionSchema = z.object({
|
|
found: z.boolean().describe("false if the image does not show a dish, drink, or recipe (random photo, no visible food)"),
|
|
recipeType: z.enum(["dish", "drink"]).describe("\"drink\" only for a beverage/cocktail — \"dish\" for anything else, including if unsure."),
|
|
title: z.string().describe("Best guess at the dish or drink's name"),
|
|
visibleIngredients: z.array(z.string()).describe("Ingredients visibly identifiable in the photo"),
|
|
description: z.string().describe("What's visible — presentation, garnish, cooking state, texture, any visible labels/text — detailed enough that someone who never saw the photo could write the full recipe from this alone"),
|
|
});
|
|
|
|
export type PhotoRecognition = z.infer<typeof PhotoRecognitionSchema>;
|
|
|
|
/** Step 1 of the photo-import flow: a vision model looks at the photo and
|
|
* describes what it sees. Deliberately recognition-only — the actual recipe
|
|
* (quantities, steps, timing) is reconstructed afterwards by a text model in
|
|
* `generateRecipeFromRecognition`, the same split pantry photo-scan uses
|
|
* between "what's in this photo" and downstream structuring. */
|
|
export async function recognizePhoto(
|
|
imageBase64: string,
|
|
mimeType: "image/jpeg" | "image/png" | "image/webp",
|
|
config?: AiConfig,
|
|
): Promise<PhotoRecognition> {
|
|
const model = resolveModel(config);
|
|
|
|
const { object } = await generateObject({
|
|
model,
|
|
schema: PhotoRecognitionSchema,
|
|
system:
|
|
"You are a food-recognition specialist. Look at the image and describe the dish or drink it shows in enough visual detail — ingredients, presentation, cooking state — that someone who never saw the photo could write a full recipe from your description alone. If the image doesn't show food, a drink, or a recipe, set found to false and leave the other fields minimal.",
|
|
messages: [
|
|
{
|
|
role: "user",
|
|
content: [
|
|
{ type: "image", image: imageBase64, mediaType: mimeType },
|
|
{ type: "text", text: "What dish or drink is shown in this photo?" },
|
|
],
|
|
},
|
|
],
|
|
});
|
|
|
|
return object;
|
|
}
|