import express from "express"
import { python } from 'pythonia'
import { daisy } from "./daisy.mts"
import { alpine } from "./alpine.mts"
// import Python dependencies
const { AutoModelForCausalLM } = await python('ctransformers')
// define the CSS and JS dependencies
const css = [
"/css/daisyui@2.6.0.css",
].map(item => ``)
.join("")
const script = [
"/js/alpinejs@3.12.2.js",
"/js/tailwindcss@3.3.2.js"
].map(item => ``)
.join("")
// import the language model (note: need a fast internet link)
const llm = await AutoModelForCausalLM.from_pretrained$(
"TheBloke/WizardCoder-15B-1.0-GGML", {
model_file: "WizardCoder-15B-1.0.ggmlv3.q4_0.bin",
model_type: "starcoder"
})
const app = express()
const port = 7860
const timeoutInSec = 60 * 60
console.log("timeout set to 60 minutes")
app.use(express.static("public"))
const maxParallelRequests = 1
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})`)
}
// we need to exit the open Python process or else it will keep running in the background
process.on('SIGINT', () => {
try {
(python as any).exit()
} catch (err) {
// exiting Pythonia can get a bit messy: try/catch or not,
// you *will* see warnings and tracebacks in the console
}
process.exit(0)
})
app.get("/debug", (req, res) => {
res.write(JSON.stringify({
nbTotal: pending.total,
nbPending: pending.queue.length,
queue: pending.queue,
}))
res.end()
})
app.get("/", async (req, res) => {
// naive implementation: we say we are out of capacity
if (pending.queue.length >= maxParallelRequests) {
res.write("sorry, max nb of parallel requests reached")
res.end()
return
}
// alternative approach: kill old queries
// while (pending.queue.length > maxParallelRequests) {
// endRequest(pending.queue[0], 'max nb of parallel request reached')
// }
const id = `${pending.total++}`
console.log(`new request ${id}`)
pending.queue.push(id)
const prefix = `${css}${script}`
res.write(prefix)
req.on("close", function() {
endRequest(id, "browser ended the connection")
})
// for testing we kill after some delay
setTimeout(() => {
endRequest(id, `timed out after ${timeoutInSec}s`)
}, timeoutInSec * 1000)
const finalPrompt = `# Context
Generate a webpage written in English about: ${req.query.prompt}.
# Documentation
${daisy}
# Guidelines
- Do not write a tutorial or repeat the instruction, but directly write the final code within a script tag
- Use a color scheme consistent with the brief and theme
- You need to use Tailwind CSS and DaisyUI for the UI, pure vanilla JS and AlpineJS for the JS.
- You vanilla JS code will be written directly inside the page, using
- You MUST use English, not Latin! (I repeat: do NOT write lorem ipsum!)
- No need to write code comments, and try to make the code compact (short function names etc)
- Use a central layout by wrapping everything in a \`
\`
# Result output
${prefix}`
try {
// be careful: if you input a prompt which is too large, you may experience a timeout
const inputTokens = await llm.tokenize(finalPrompt)
console.log("initializing the generator (may take 30s or more)")
const generator = await llm.generate(inputTokens)
console.log("generator initialized, beginning token streaming..")
for await (const token of generator) {
if (!pending.queue.includes(id)) {
break
}
const tmp = await llm.detokenize(token)
process.stdout.write(tmp)
res.write(tmp)
}
endRequest(id, `normal end of the LLM stream for request ${id}`)
} catch (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.listen(port, () => { console.log(`Open http://localhost:${port}/?prompt=a%20landing%20page%20for%20a%20company%20called%20Hugging%20Face`) })