fix

src/lib/server/parser.ts

@@ -0,0 +1,130 @@
+import { createLLM } from './llm';
+import { zodResponseFormat } from 'openai/helpers/zod';
+import { z } from 'zod';
+
+const RecipeSchema = z.object({
+	name: z.string(),
+	servings: z.number().nullable(),
+	description: z.string().nullable(),
+	ingredients: z.array(
+		z.object({
+			item: z.string(),
+			amount: z.string(),
+			unit: z.string()
+		})
+	).nullable(),
+	steps: z.array(z.string()).nullable(),
+	image: z.string().nullable().optional()
+});
+
+export type Recipe = z.infer<typeof RecipeSchema>;
+
+/**
+ * Detect if the text contains a recipe using binary classification
+ * @param text - The text to analyze
+ * @returns True if a recipe is detected, false otherwise
+ */
+export async function detectRecipe(text: string): Promise<boolean> {
+	try {
+		const { client, model } = createLLM();
+
+		const detectionResponse = await client.chat.completions.create({
+			model,
+			messages: [
+				{
+					role: 'system',
+					content:
+						"You are a recipe detector. Answer with ONLY 'yes' or 'no' - nothing else. A recipe MUST have: (1) name/title, (2) ingredients with quantities, (3) numbered cooking steps. If ANY are missing, answer 'no'."
+				},
+				{
+					role: 'user',
+					content: `Does this text contain a recipe?\n\n${text}`
+				}
+			],
+			max_tokens: 10
+		});
+
+		const detectionResult = detectionResponse.choices[0].message.content?.toLowerCase() ?? '';
+		return detectionResult.includes('yes');
+	} catch (e) {
+		console.error('Recipe detection error:', e);
+		throw new Error('Failed to detect recipe');
+	}
+}
+
+/**
+ * Extract recipe data from text using LLM structured output
+ * @param text - The text containing the recipe
+ * @returns Parsed recipe object
+ */
+export async function parseRecipe(text: string): Promise<Recipe> {
+	try {
+		const { client, model } = createLLM();
+
+		const completion = await client.beta.chat.completions.parse({
+			model,
+			messages: [
+				{
+					role: 'system',
+					content: `You are a RECIPE EXTRACTOR. Extract the recipe from the provided text.
+
+✅ REQUIREMENTS:
+1. Extract the exact recipe name from the text
+2. List all ingredients with their quantities and units
+3. List all cooking steps in order
+4. Translate everything to Italian
+5. Convert measurements to SI units (g, mL, °C)
+
+📋 CONVERSION TABLE:
+- 1 cup = 240 mL, 1 tbsp = 15 mL, 1 tsp = 5 mL
+- 1 oz = 28.35 g, 1 lb = 453.59 g
+- 1 stick butter = 113 g
+- °F→°C: (°F–32)×5/9
+
+🔄 OUTPUT FORMAT:
+{
+  "name": "recipe name in Italian",
+  "servings": number or null,
+  "description": "description in Italian or null",
+  "ingredients": [{"item": "ingredient name", "amount": "quantity", "unit": "SI unit"}],
+  "steps": ["1. First step", "2. Second step", ...]
+}
+
+Extract ONLY what's explicitly in the text. Be accurate and literal.
+				`
+				},
+				{
+					role: 'user',
+					content: `Extract the recipe from this text:\n\n${text}`
+				}
+			],
+			response_format: zodResponseFormat(RecipeSchema, 'recipe')
+		});
+
+		const recipe = completion.choices[0].message.parsed;
+
+		if (!recipe || !recipe.name) {
+			throw new Error('Failed to extract recipe - missing name');
+		}
+
+		return recipe;
+	} catch (e) {
+		console.error('Recipe parsing error:', e);
+		throw new Error('Failed to parse recipe');
+	}
+}
+
+/**
+ * Complete workflow: detect recipe and parse if found
+ * @param text - The text to analyze
+ * @returns Parsed recipe object if detected, null otherwise
+ */
+export async function extractRecipe(text: string): Promise<Recipe | null> {
+	const isRecipe = await detectRecipe(text);
+
+	if (!isRecipe) {
+		return null;
+	}
+
+	return parseRecipe(text);
+}