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| import { pipeline } from 'stream' | |
| import { promisify } from 'util' | |
| import express from 'express' | |
| import { HfInference } from '@huggingface/inference' | |
| import { daisy } from './daisy.mts' | |
| const pipe = promisify(pipeline) | |
| const hfi = new HfInference(process.env.HF_API_TOKEN) | |
| const hf = hfi.endpoint(process.env.HF_ENDPOINT_URL) | |
| const app = express() | |
| const port = 7860 | |
| const minPromptSize = 16 // if you change this, you will need to also change in public/index.html | |
| const timeoutInSec = 30 * 60 | |
| console.log('timeout set to 30 minutes') | |
| app.use(express.static('public')) | |
| const pending: { | |
| total: number; | |
| queue: string[]; | |
| } = { | |
| total: 0, | |
| queue: [], | |
| } | |
| const endRequest = (id: string, reason: string) => { | |
| if (!id || !pending.queue.includes(id)) { | |
| return | |
| } | |
| pending.queue = pending.queue.filter(i => i !== id) | |
| console.log(`request ${id} ended (${reason})`) | |
| } | |
| app.get('/debug', (req, res) => { | |
| res.write(JSON.stringify({ | |
| nbTotal: pending.total, | |
| nbPending: pending.queue.length, | |
| queue: pending.queue, | |
| })) | |
| res.end() | |
| }) | |
| app.get('/app', async (req, res) => { | |
| if (`${req.query.prompt}`.length < minPromptSize) { | |
| res.write(`prompt too short, please enter at least ${minPromptSize} characters`) | |
| res.end() | |
| return | |
| } | |
| const id = `${pending.total++}` | |
| console.log(`new request ${id}`) | |
| pending.queue.push(id) | |
| const prefix = `<html><head><link href='https://cdn.jsdelivr.net/npm/[email protected]/dist/full.css' rel='stylesheet' type='text/css' /><script defer src='https://cdn.jsdelivr.net/npm/[email protected]/dist/cdn.min.js'></script><script src='https://cdn.tailwindcss.com?plugins=forms,typography,aspect-ratio,line-clamp'></script><title>Generated content</title><body` | |
| res.write(prefix) | |
| req.on('close', function() { | |
| endRequest(id, 'browser asked to end the connection') | |
| }) | |
| setTimeout(() => { | |
| endRequest(id, `timed out after ${timeoutInSec}s`) | |
| }, timeoutInSec * 1000) | |
| const finalPrompt = `# Task | |
| Generate the following: ${req.query.prompt} | |
| # API Documentation | |
| ${daisy} | |
| # Guidelines | |
| - Never repeat the instruction, instead directly write the final code | |
| - Use a color scheme consistent with the brief and theme | |
| - To generate all your images, import from from this route: "/image?prompt=<description or caption of an image, photo or illustration>" | |
| - please be descriptive for the prompt, eg describe the scene in a few words (textures, characters, materials, camera type etc) | |
| - You must use Tailwind CSS and Daisy UI for the CSS classes, vanilla JS and Alpine.js for the JS. | |
| - All the JS code will be written directly inside the page, using <script type='text/javascript'>...</script> | |
| - You MUST use English, not Latin! (I repeat: do NOT write lorem ipsum!) | |
| - No need to write code comments, so please make the code compact (short function names etc) | |
| - Use a central layout by wrapping everything in a \`<div class='flex flex-col items-center'>\` | |
| # HTML output | |
| <html><head></head><body` | |
| try { | |
| let result = '' | |
| for await (const output of hf.textGenerationStream({ | |
| inputs: finalPrompt, | |
| parameters: { max_new_tokens: 1024 } | |
| })) { | |
| if (!pending.queue.includes(id)) { | |
| break | |
| } | |
| result += output.token.text | |
| process.stdout.write(output.token.text) | |
| res.write(output.token.text) | |
| if (result.includes('</html>')) { | |
| break | |
| } | |
| if (result.includes('<|end|>') || result.includes('<|assistant|>')) { | |
| break | |
| } | |
| } | |
| endRequest(id, `normal end of the LLM stream for request ${id}`) | |
| } catch (e) { | |
| console.log(e) | |
| endRequest(id, `premature end of the LLM stream for request ${id} (${e})`) | |
| } | |
| try { | |
| res.end() | |
| } catch (err) { | |
| console.log(`couldn't end the HTTP stream for request ${id} (${err})`) | |
| } | |
| }) | |
| app.get('/image', async (req, res) => { | |
| try { | |
| const blob = await hfi.textToImage({ | |
| inputs: [ | |
| `${req.query.prompt || 'generic placeholder'}`, | |
| 'award winning', | |
| 'high resolution', | |
| 'beautiful', | |
| '[trending on artstation]' | |
| ].join(','), | |
| model: 'stabilityai/stable-diffusion-2', | |
| parameters: { | |
| negative_prompt: 'blurry, cropped, low quality, ugly', | |
| } | |
| }) | |
| const buffer = Buffer.from(await blob.arrayBuffer()) | |
| res.setHeader('Content-Type', blob.type) | |
| res.setHeader('Content-Length', buffer.length) | |
| res.end(buffer) | |
| } catch (err) { | |
| console.error(`Error when generating the image: ${err.message}`); | |
| res.status(500).json({ error: 'An error occurred when trying to generate the image' }); | |
| } | |
| }) | |
| app.listen(port, () => { console.log(`Open http://localhost:${port}`) }) | |