feat: refactor frontend and fix LLM extraction

- Fix critical await bug in extract-stream endpoint
- Add comprehensive logging to LLM and parser modules
- Implement fallback to standard completion for incompatible models
- Create enhanced v2.0 prompts with social media handling and few-shot examples
- Add LLM health check endpoint
- Decompose share page into 6 focused Svelte 5 snippets

Resolves LM Studio integration issues and improves code maintainability

src/lib/server/parser.ts

@@ -1,6 +1,7 @@
 import { createLLM } from './llm';
 import { zodResponseFormat } from 'openai/helpers/zod';
 import { z } from 'zod';
+import { RECIPE_DETECTION_PROMPT, RECIPE_EXTRACTION_PROMPT } from './prompts/recipe-extraction';
 
 const RecipeSchema = z.object({
 	name: z.string(),
@@ -28,27 +29,34 @@ export async function detectRecipe(text: string): Promise<boolean> {
 	try {
 		const { client, model } = createLLM();
 
+		console.log('[LLM] Starting recipe detection...');
+		console.log('[LLM] Model:', model);
+		console.log('[LLM] Text length:', text.length);
+
 		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'."
+					content: RECIPE_DETECTION_PROMPT
 				},
 				{
 					role: 'user',
 					content: `Does this text contain a recipe?\n\n${text}`
 				}
 			],
-			max_tokens: 10
+			max_tokens: 10,
+			temperature: 0
 		});
 
 		const detectionResult = detectionResponse.choices[0].message.content?.toLowerCase() ?? '';
+		console.log('[LLM] Detection response:', detectionResult);
+
 		return detectionResult.includes('yes');
 	} catch (e) {
-		console.error('Recipe detection error:', e);
-		throw new Error('Failed to detect recipe');
+		console.error('[LLM] Recipe detection error:', e);
+		console.error('[LLM] Stack trace:', (e as Error).stack);
+		throw new Error(`Failed to detect recipe: ${(e as Error).message}`);
 	}
 }
 
@@ -61,47 +69,27 @@ export async function parseRecipe(text: string): Promise<Recipe> {
 	try {
 		const { client, model } = createLLM();
 
+		console.log('[LLM] Starting recipe parsing...');
+		console.log('[LLM] Model:', model);
+
 		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.
-				`
+					content: RECIPE_EXTRACTION_PROMPT
 				},
 				{
 					role: 'user',
 					content: `Extract the recipe from this text:\n\n${text}`
 				}
 			],
-			response_format: zodResponseFormat(RecipeSchema, 'recipe')
+			response_format: zodResponseFormat(RecipeSchema, 'recipe'),
+			temperature: 0.3
 		});
 
 		const recipe = completion.choices[0].message.parsed;
+		console.log('[LLM] Parse response:', recipe?.name);
 
 		if (!recipe || !recipe.name) {
 			throw new Error('Failed to extract recipe - missing name');
@@ -109,8 +97,17 @@ Extract ONLY what's explicitly in the text. Be accurate and literal.
 
 		return recipe;
 	} catch (e) {
-		console.error('Recipe parsing error:', e);
-		throw new Error('Failed to parse recipe');
+		console.error('[LLM] Recipe parsing error:', e);
+		console.error('[LLM] Stack trace:', (e as Error).stack);
+
+		// If structured output fails, try standard completion
+		if ((e as any).message?.includes('response_format') ||
+			(e as any).message?.includes('structured output')) {
+			console.warn('[LLM] Falling back to standard completion');
+			return await parseRecipeWithStandardCompletion(text);
+		}
+
+		throw new Error(`Failed to parse recipe: ${(e as Error).message}`);
 	}
 }
 
@@ -128,3 +125,56 @@ export async function extractRecipe(text: string): Promise<Recipe | null> {
 
 	return parseRecipe(text);
 }
+
+/**
+ * Fallback parser using standard completion (no structured output)
+ * Used when the model doesn't support beta.chat.completions.parse()
+ */
+async function parseRecipeWithStandardCompletion(text: string): Promise<Recipe> {
+	const { client, model } = createLLM();
+
+	console.log('[LLM] Using standard completion fallback');
+
+	const completion = await client.chat.completions.create({
+		model,
+		messages: [
+			{
+				role: 'system',
+				content: `You are a recipe extractor. Return ONLY valid JSON matching this schema:
+{
+  "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", ...]
+}
+
+Convert all measurements to SI units (g, mL, °C).
+Translate everything to Italian.
+Extract ONLY what's in the text.`
+			},
+			{
+				role: 'user',
+				content: `Extract the recipe from this text:\n\n${text}`
+			}
+		],
+		max_tokens: 2000,
+		temperature: 0.3
+	});
+
+	const jsonResponse = completion.choices[0].message.content;
+	if (!jsonResponse) {
+		throw new Error('Empty response from LLM');
+	}
+
+	console.log('[LLM] Standard completion raw response:', jsonResponse.substring(0, 200));
+
+	// Parse and validate JSON (remove code fences if present)
+	const cleanedJson = jsonResponse.replace(/```json\n?|```\n?/g, '').trim();
+	const parsedData = JSON.parse(cleanedJson);
+	const recipe = RecipeSchema.parse(parsedData);
+
+	console.log('[LLM] Standard completion parsed recipe:', recipe.name);
+
+	return recipe;
+}