fix
This commit is contained in:
Giancarmine Salucci
@@ -1,162 +1,42 @@
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import { createBrowserContext } from '$lib/server/browser';
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import { createLLM } from '$lib/server/llm';
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import { extractTextAndThumbnail } from '$lib/server/extraction';
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import { extractRecipe } from '$lib/server/parser';
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import { json } from '@sveltejs/kit';
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import fs, { writeFileSync } from 'fs';
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import { zodResponseFormat } from 'openai/helpers/zod';
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import path from 'path';
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import { z } from 'zod';
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const RecipeSchema = z.object({
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name: z.string(),
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servings: z.number().nullable(),
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description: z.string().nullable(),
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ingredients: z.array(z.object({
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item: z.string(),
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amount: z.string(),
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unit: z.string()
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})).nullable(),
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steps: z.array(z.string()).nullable(),
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image: z.string().nullable().optional()
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});
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export async function POST({ request }) {
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const { url } = await request.json();
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// 1. Browser Connection - now managed by SvelteKit
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console.log('Creating browser context for URL:', url);
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const { url } = await request.json();
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// Try to find auth storage
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const authPathDocker = '/app/secrets/auth.json';
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const authPathLocal = './secrets/auth.json';
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const authPath = fs.existsSync(authPathDocker) ? authPathDocker :
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fs.existsSync(authPathLocal) ? authPathLocal :
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undefined;
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console.log('Processing URL:', url);
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const context = await createBrowserContext(authPath);
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const page = await context.newPage();
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// Set a fixed viewport size (Instagram feed width)
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await page.setViewportSize({ width: 1080, height: 1920 });
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let bodyText = '';
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let thumbnail: string | null = null;
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try {
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// Step 1: Extract text and thumbnail from page
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const { bodyText, thumbnail } = await extractTextAndThumbnail(url);
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try {
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await page.goto(url, { waitUntil: 'domcontentloaded' });
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bodyText = (await page.evaluate(() => document.body.innerText)).replace(/^(?:.*\n){6}/, '').split('More posts from')[0].trim();
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bodyText = bodyText.replace(/@\w+/g, '').replace(/#\w+/g, '');
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// Step 2: Parse recipe from extracted text
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const recipe = await extractRecipe(bodyText);
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writeFileSync(path.resolve('debug_page.txt'), bodyText); // Save for debugging, overwriting if exists
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const videoBounds = await page.evaluate(() => {
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const video = document.querySelector('video');
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if (!video) return null;
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const rect = video.getBoundingClientRect();
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return {
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x: Math.max(0, rect.left),
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y: Math.max(0, rect.top),
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width: Math.min(rect.width, window.innerWidth),
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height: Math.min(rect.height, window.innerHeight)
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};
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});
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if (!recipe) {
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return json({ error: 'No recipe found in provided text' }, { status: 400 });
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}
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if (videoBounds && videoBounds.width > 0 && videoBounds.height > 0) {
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const screenshotBuffer = await page.screenshot({
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type: 'jpeg',
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quality: 85,
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clip: videoBounds
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});
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thumbnail = `data:image/jpeg;base64,${screenshotBuffer.toString('base64')}`;
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} else {
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console.warn('Video element not found or has no size, taking full page screenshot');
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const screenshotBuffer = await page.screenshot({ type: 'jpeg', quality: 85 });
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thumbnail = `data:image/jpeg;base64,${screenshotBuffer.toString('base64')}`;
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}
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} catch (e) {
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console.error('Scraping error:', e);
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return json({ error: 'Failed to scrape URL' }, { status: 500 });
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} finally {
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await page.close();
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await context.close();
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}
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// Step 3: Enrich recipe with metadata
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if (recipe.description) {
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recipe.description += `\n\nLink: ${url}`;
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} else {
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recipe.description = `Link: ${url}`;
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}
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// 2. LLM Processing - Two-step validation
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try {
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const { client, model } = createLLM();
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// STEP 1: Binary recipe detection (yes/no only)
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const detectionResponse = await client.chat.completions.create({
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model,
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messages: [
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{ role: "system", content: "You are a recipe detector. Answer with ONLY 'yes' or 'no' - nothing else. A recipe MUST have: (1) name/title, (2) ingredients with quantities, (3) numbered cooking steps. If ANY are missing, answer 'no'." },
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{ role: "user", content: `Does this text contain a recipe?\n\n${bodyText}` }
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],
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max_tokens: 10,
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});
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if (thumbnail) {
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recipe.image = thumbnail;
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}
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const detectionResult = detectionResponse.choices[0].message.content?.toLowerCase() ?? '';
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const hasRecipe = detectionResult.includes("yes");
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return json({ recipe, bodyText });
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} catch (error) {
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const errorMessage = error instanceof Error ? error.message : 'Unknown error';
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console.error('Recipe extraction pipeline error:', errorMessage);
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if (!hasRecipe) {
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return json({ error: "No recipe found in provided text" }, { status: 400 });
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}
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// STEP 2: Extract recipe (only proceeds if recipe detected)
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const completion = await client.beta.chat.completions.parse({
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model,
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messages: [
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{ role: "system", content: `You are a RECIPE EXTRACTOR. Extract the recipe from the provided text.
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✅ REQUIREMENTS:
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1. Extract the exact recipe name from the text
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2. List all ingredients with their quantities and units
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3. List all cooking steps in order
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4. Translate everything to Italian
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5. Convert measurements to SI units (g, mL, °C)
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📋 CONVERSION TABLE:
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- 1 cup = 240 mL, 1 tbsp = 15 mL, 1 tsp = 5 mL
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- 1 oz = 28.35 g, 1 lb = 453.59 g
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- 1 stick butter = 113 g
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- °F→°C: (°F–32)×5/9
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🔄 OUTPUT FORMAT:
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{
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"name": "recipe name in Italian",
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"servings": number or null,
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"description": "description in Italian or null",
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"ingredients": [{"item": "ingredient name", "amount": "quantity", "unit": "SI unit"}],
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"steps": ["1. First step", "2. Second step", ...]
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}
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Extract ONLY what's explicitly in the text. Be accurate and literal.
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` },
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{ role: "user", content: `Extract the recipe from this text:\n\n${bodyText}` }
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],
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response_format: zodResponseFormat(RecipeSchema, "recipe")
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});
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console.log('LLM extraction successful:', completion.choices[0].message);
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const recipe = completion.choices[0].message.parsed;
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if (!recipe || !recipe.name) {
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return json({ error: "Failed to extract recipe" }, { status: 400 });
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}
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// Append original Instagram link to description
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if (recipe.description) {
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recipe.description += `\n\nLink: ${url}`;
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} else {
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recipe.description = `Link: ${url}`;
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}
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// Add thumbnail to recipe
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if (thumbnail) {
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recipe.image = thumbnail;
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}
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return json({ recipe, bodyText });
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} catch (e) {
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console.error('LLM error:', e);
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return json({ error: 'Failed to parse recipe', bodyText }, { status: 500 });
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}
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return json(
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{ error: errorMessage || 'Failed to process URL' },
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{ status: error instanceof Error && error.message.includes('scrape') ? 500 : 400 }
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);
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}
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}
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@@ -1,5 +1,5 @@
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import { json } from '@sveltejs/kit';
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import {tandoorConfig} from '$lib/server/tandoor-config';
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export async function GET() {
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return json({...tandoorConfig, token: ''});
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}
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import { json } from '@sveltejs/kit';
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import {tandoorConfig} from '$lib/server/tandoor-config';
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export async function GET() {
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return json({...tandoorConfig, token: ''});
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}
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@@ -1,42 +1,42 @@
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import { json } from '@sveltejs/kit';
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import { uploadRecipeWithIngredientsDTO, uploadRecipeImage } from '$lib/server/tandoor';
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export async function POST({ request }) {
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const { recipe } = await request.json();
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if (!recipe) {
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return json({ error: 'No recipe provided' }, { status: 400 });
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}
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try {
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const result = await uploadRecipeWithIngredientsDTO(recipe);
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if (!result.success) {
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return json({ error: result.error || 'Failed to upload recipe' }, { status: 500 });
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}
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// Upload image if available
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let imageStatus = null;
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if (result.recipeId && result.imageUrl) {
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imageStatus = await uploadRecipeImage(result.recipeId, result.imageUrl);
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if (!imageStatus.success) {
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console.warn('Image upload failed, but recipe created:', imageStatus.error);
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}
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}
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return json({
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success: true,
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message: 'Recipe successfully imported to Tandoor',
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recipeId: result.recipeId,
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imageUpload: imageStatus?.success ? 'successful' : 'failed'
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});
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} catch (error) {
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console.error('Tandoor upload error:', error);
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return json(
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{
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error: error instanceof Error ? error.message : 'Unknown error occurred'
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},
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{ status: 500 }
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);
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}
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}
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import { json } from '@sveltejs/kit';
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import { uploadRecipeWithIngredientsDTO, uploadRecipeImage } from '$lib/server/tandoor';
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export async function POST({ request }) {
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const { recipe } = await request.json();
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if (!recipe) {
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return json({ error: 'No recipe provided' }, { status: 400 });
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}
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try {
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const result = await uploadRecipeWithIngredientsDTO(recipe);
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if (!result.success) {
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return json({ error: result.error || 'Failed to upload recipe' }, { status: 500 });
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}
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// Upload image if available
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let imageStatus = null;
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if (result.recipeId && result.imageUrl) {
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imageStatus = await uploadRecipeImage(result.recipeId, result.imageUrl);
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if (!imageStatus.success) {
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console.warn('Image upload failed, but recipe created:', imageStatus.error);
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}
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}
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return json({
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success: true,
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message: 'Recipe successfully imported to Tandoor',
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recipeId: result.recipeId,
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imageUpload: imageStatus?.success ? 'successful' : 'failed'
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});
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} catch (error) {
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console.error('Tandoor upload error:', error);
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return json(
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{
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error: error instanceof Error ? error.message : 'Unknown error occurred'
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},
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{ status: 500 }
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);
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}
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}
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