up

.dockerignore

@@ -0,0 +1,3 @@
+node_modules
+npm-debug.log
+models

.gitignore

@@ -0,0 +1,6 @@
+node_modules
+*.log
+*.bin
+.DS_Store
+.venv
+models/

.nvmrc

@@ -0,0 +1 @@
+v18.16.0

Dockerfile

@@ -0,0 +1,69 @@
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
+LABEL maintainer="Hugging Face"
+
+ENV PYTHONUNBUFFERED 1
+
+EXPOSE 7860
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
+SHELL ["sh", "-lc"]
+
+RUN apt update
+
+RUN apt --yes install curl
+
+RUN curl -fsSL https://deb.nodesource.com/setup_18.x | bash -
+
+RUN apt --yes install nodejs
+
+RUN apt --yes install git git-lfs libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
+
+RUN git lfs install
+
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+# prepare to install the Node app
+COPY --chown=user package*.json .
+
+RUN npm install
+
+# then we need to install pytorch with cuda support
+
+# If set to nothing, will install the latest version
+ARG PYTORCH='2.0.1'
+ARG TORCH_VISION=''
+ARG TORCH_AUDIO=''
+# Example: `cu102`, `cu113`, etc.
+ARG CUDA='cu118'
+
+RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' ||  VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+
+# do we need to run this?
+# RUN python3 -m pip uninstall -y tensorflow flax
+
+# Install the rest of dependencies
+COPY requirements.txt ./
+RUN pip install -r requirements.txt
+
+COPY --chown=user . .
+
+RUN git clone https://huggingface.co/WizardLM/WizardCoder-15B-V1.0
+
+# help Pythonia by giving it the path to Python
+ENV PYTHON_BIN /usr/bin/python3
+
+RUN python3 test.py
+
+# CMD [ "npm", "run", "start" ]
+CMD [ "npm", "run", "test" ]

LICENSE.txt

@@ -0,0 +1,201 @@
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+   limitations under the License.

README.md

@@ -0,0 +1,94 @@
+---
+title: Webapp Factory WizardCoder Transformers
+emoji: 🏭🧙
+colorFrom: brown
+colorTo: red
+sdk: docker
+pinned: false
+app_port: 7860
+---
+
+A minimalist Docker project to generate apps on demand.
+
+Ready to be used in a Hugging Face Space.
+
+# WARNING
+
+WARNING! This is an experimental version of the "Webapp Factory" project, and it might not work properly!
+
+# Examples
+
+## Local prompt examples
+
+```
+http://localhost:7860/?prompt=a%20pong%20game%20clone%20in%20HTML,%20made%20using%20the%20canvas
+```
+```
+http://localhost:7860/?prompt=a simple html canvas game where we need to feed tadpoles controlled by an AI. The tadpoles move randomly, but when the user click inside the canvas to add some kind of food, the tadpoles will compete to eat it. Tadpole who didn't eat will die, and those who ate will reproduce.
+```
+
+## Installation
+
+### Prerequisites
+
+**A powerful machine is required! You need at least 24 Gb of memory!**
+
+- Install NVM: https://github.com/nvm-sh/nvm
+- Install Docker https://www.docker.com
+
+### Python
+
+This project relies on Python dependencies called through Pythonia.
+
+To install those dependencies, first you should create and activate a new virtual environment for Python:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+```
+
+Then install the dependencies in it:
+```bash
+pip install -r requirements.txt
+```
+
+### Installing the model
+
+For this project we need to download the model in advance:
+
+```
+# go to the models directory
+cd models
+
+# Make sure you have git-lfs installed (https://git-lfs.com)
+git lfs install
+
+git clone https://huggingface.co/WizardLM/WizardCoder-15B-V1.0
+
+cd ..
+```
+
+Note: the Dockerfile script will do this automatically
+
+### Building and run without Docker
+
+```bash
+nvm use
+npm i
+npm run start
+```
+
+### Building and running with Docker
+
+```bash
+npm run docker
+```
+
+This script is a shortcut executing the following commands:
+
+```bash
+docker build -t webapp-factory-wizardcoder-transformers .
+docker run -it -p 7860:7860 webapp-factory-wizardcoder-transformers
+```
+

package-lock.json

@@ -0,0 +1,955 @@
+{
+  "name": "webapp-factory-wizardcoder-transformers",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "webapp-factory-wizardcoder-transformers",
+      "version": "1.0.0",
+      "license": "Apache License",
+      "dependencies": {
+        "@types/express": "^4.17.17",
+        "express": "^4.18.2",
+        "pythonia": "^1.0.2",
+        "ts-node": "^10.9.1"
+      }
+    },
+    "node_modules/@cspotcode/source-map-support": {
+      "version": "0.8.1",
+      "resolved": "https://registry.npmjs.org/@cspotcode/source-map-support/-/source-map-support-0.8.1.tgz",
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+      "dependencies": {
+        "@jridgewell/trace-mapping": "0.3.9"
+      },
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/@jridgewell/resolve-uri": {
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+      }
+    },
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+      "dependencies": {
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+        "@jridgewell/sourcemap-codec": "^1.4.10"
+      }
+    },
+    "node_modules/@tsconfig/node10": {
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+    },
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+}

package.json

@@ -0,0 +1,21 @@
+{
+  "name": "webapp-factory-wizardcoder-transformers",
+  "version": "1.0.0",
+  "description": "A minimalist project to generate webapps without any 3rd party API. Designed to run in a Hugging Face space.",
+  "main": "src/index.mts",
+  "scripts": {
+    "start": "node --loader ts-node/esm src/index.mts",
+    "test": "node --loader ts-node/esm src/test.mts",
+    "docker": "npm run docker:build && npm run docker:run",
+    "docker:build": "docker build -t webapp-factory-wizardcoder-transformers .",
+    "docker:run": "docker run -it -p 7860:7860 webapp-factory-wizardcoder-transformers"
+  },
+  "author": "Julian Bilcke <[email protected]>",
+  "license": "Apache License",
+  "dependencies": {
+    "@types/express": "^4.17.17",
+    "express": "^4.18.2",
+    "pythonia": "^1.0.2",
+    "ts-node": "^10.9.1"
+  }
+}

public/css/[email protected]

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public/index.html

@@ -0,0 +1,136 @@
+<html>
+  <head>
+    <title>Webapp Factory 🏭</title>
+    <link href="/css/[email protected]" rel="stylesheet" type="text/css">
+  </head>
+  <body>
+    <div class="flex flex-row" x-data="app()" x-init="init()">
+      <div
+        class="hero min-h-screen bg-stone-100 transition-[width] delay-150 ease-in-out"
+        :class="open ? 'w-2/6' : 'w-6/6'">
+        <div class="hero-content text-center">
+          <div class="flex flex-col max-w-xl space-y-6">
+            <h1
+              class="font-bold text-stone-600 mb-4 transition-all delay-150 ease-in-out"
+              :class="open ? 'md:text-3xl lg:text-4xl' : 'text-6xl'"
+            >Webapp Factory 🏭</h1>
+            <div
+              class="py-2 space-y-4 text-stone-600 transition-all delay-150 ease-in-out"
+              :class="open ? 'md:text-lg lg:text-xl' : 'text-2xl'">
+              <p>A Hugging Face space to generate web applications using a local LLM (WizardCoder).</p>
+              <p>No 3rd party service or token needed: feel free to duplicate and create interesting forks of this space 🔧</p>
+            </div>
+            <textarea
+              name="draft"
+              x-model="draft"
+              rows="10"
+              placeholder="a pong clone made using the canvas.."
+              class="input input-bordered w-full rounded text-lg text-stone-500 bg-stone-300 font-mono h-48"
+            ></textarea>
+            <button
+              class="btn disabled:text-stone-400"
+              @click="open = true, state = state === 'stopped' ? 'loading' : 'stopped'"
+              :class="draft.length < minPromptSize ? 'btn-neutral' : state === 'stopped' ? 'btn-accent' : 'btn-warning'"
+              :disabled="draft.length < minPromptSize"
+            >
+              <span x-show="draft.length < minPromptSize">Prompt too short to generate</span>
+              <span x-show="draft.length >= minPromptSize && state !== 'stopped'">Stop now</span>
+              <span x-show="draft.length >= minPromptSize && state === 'stopped'">Generate!</span>
+            </button>
+            <span class="py-3" x-show="state === 'loading'">Waiting for the stream to begin (might take a few minutes)..</span>
+            <span class="py-3" x-show="state === 'streaming'">
+              Streamed <span x-text="humanFileSize(size, true, 2)"></span> so far<br/> (hang on, this may take a while ☕)</span>
+          </div>
+        </div>
+      </div>
+      <div
+        class="flex transition-[width] delay-150 ease-in-out"
+        :class="open ? 'w-4/6' : 'w-0'">
+        <iframe
+          id="iframe"
+          class="border-none w-full h-screen"
+          :src="(state === 'stopped' || draft.length < minPromptSize)
+            ? '/placeholder.html'
+            : `/app?prompt=${draft}`
+          "></iframe>
+      </div>
+    </div>
+    <script>
+      /**
+ * Format bytes as human-readable text.
+ * 
+ * @param bytes Number of bytes.
+ * @param si True to use metric (SI) units, aka powers of 1000. False to use 
+ *           binary (IEC), aka powers of 1024.
+ * @param dp Number of decimal places to display.
+ * 
+ * @return Formatted string.
+ */
+function humanFileSize(bytes, si=false, dp=1) {
+  const thresh = si ? 1000 : 1024;
+
+  if (Math.abs(bytes) < thresh) {
+    return bytes + ' B';
+  }
+
+  const units = si 
+    ? ['kB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'] 
+    : ['KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB'];
+  let u = -1;
+  const r = 10**dp;
+
+  do {
+    bytes /= thresh;
+    ++u;
+  } while (Math.round(Math.abs(bytes) * r) / r >= thresh && u < units.length - 1);
+
+
+  return bytes.toFixed(dp) + ' ' + units[u];
+}
+function app() {
+  return {
+    open: false,
+    draft: new URLSearchParams(window.location.search).get('prompt') || '',
+    size: 0,
+    minPromptSize: 16, // if you change this, you will need to also change in src/index.mts
+    timeoutInSec: 10, // time before we determine something went wrong
+    state: 'stopped',
+    lastTokenAt: + new Date(),
+    init() {
+      setInterval(() => {
+        if (this.state === 'stopped') {
+          this.lastTokenAt = +new Date()
+          return
+        }
+        const html = document?.getElementById('iframe')?.contentWindow?.document?.documentElement?.outerHTML
+        const size = Number(html?.length) // count how many characters we have generated
+  
+        if (isNaN(size) || !isFinite(size)) {
+          this.size = 0
+          this.state = 'loading'
+          return
+        }
+        this.size = new Blob([html]).size
+        this.state = 'streaming'
+        const lastTokenAt = + new Date()
+        const elapsed = (lastTokenAt - this.lastTokenAt) / 1000
+        this.lastTokenAt = lastTokenAt
+        if (elapsed > this.timeoutInSec) {
+          console.log(`Something went wrong, it too more than ${timeoutInSec} seconds to generate a token.`)
+          this.state = 'stopped'
+          return
+        }
+        if (html.endsWith('</html>')) {
+          console.log('We reached the natural end of the stream, it seems.')
+          // this.state === 'stopped'
+          return
+        }
+      }, 100)
+    },
+  }
+}
+    </script>
+    <script src="/js/[email protected]"></script>
+    <script src="/js/[email protected]"></script>
+  </body>
+</html>

public/js/README.md

@@ -0,0 +1 @@
+URL used for tailwind: https://cdn.tailwindcss.com/[email protected],[email protected],[email protected],[email protected]

public/js/[email protected]

@@ -0,0 +1,5 @@
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public/placeholder.html

@@ -0,0 +1,17 @@
+<html>
+  <head>
+    <title>Nothing to show (yet)</title>
+    <link href="/css/[email protected]" rel="stylesheet" type="text/css">
+  </head>
+  <body>
+    <div class="hero min-h-screen bg-stone-100">
+      <div class="hero-content text-center">
+        <div class="flex flex-col max-w-xl space-y-6">
+          <h1 class="font-bold text-stone-600 mb-4">Nothing to show here (note: minimum prompt size is 10 characters)</h1>
+        </div>
+      </div>
+    </div>
+    <script src="/js/[email protected]"></script>
+    <script src="/js/[email protected]"></script>
+  </body>
+</html>

requirements.txt

@@ -0,0 +1 @@
+transformers

src/alpine.mts

@@ -0,0 +1,220 @@
+
+interface FeatureAPI {
+  title: string
+  description: string
+  pattern: string
+}
+
+const getPromptFromFeatures = (feats: FeatureAPI[]) =>
+  feats.map(({ title, description, pattern }) => `## "${title}": ${description}.\nExample: \`${pattern}\``).join("\n")
+
+
+export const attributes: FeatureAPI[] = [
+  {
+    title: "x-data",
+    description: "Declare a new Alpine component and its data for a block of HTML",
+    pattern:
+`<div x-data="{ open: false }">
+...
+</div>`
+  },
+  {
+    title: "x-bind",
+    description: "Dynamically set HTML attributes on an element",
+    pattern:
+`<div x-bind:class="! open ? 'hidden' : ''">
+...
+</div>`
+  },
+  {
+    title: "x-on",
+    description: "Listen for browser events on an element",
+    pattern:
+`<button x-on:click="open = ! open">
+Toggle
+</button>`
+  },
+  {
+    title: "x-text",
+    description: "Set the text content of an element",
+    pattern:
+`<div>
+Copyright ©
+
+<span x-text="new Date().getFullYear()"></span>
+</div>`
+  },
+  {
+    title: "x-html",
+    description: "Set the inner HTML of an element",
+    pattern:
+`<div x-html="(await axios.get('/some/html/partial')).data">
+...
+</div>`
+  },
+  {
+    title: "x-model",
+    description: "Synchronize a piece of data with an input element",
+    pattern:
+`<div x-data="{ search: '' }">
+<input type="text" x-model="search">
+
+Searching for: <span x-text="search"></span>
+</div>`
+  },
+  {
+    title: "x-show",
+    description: "Toggle the visibility of an element",
+    pattern:
+`<div x-show="open">
+...
+</div>`
+  },
+  {
+    title: "x-transition",
+    description: "Transition an element in and out using CSS transitions",
+    pattern:
+`<div x-show="open" x-transition>
+...
+</div>`
+  },
+  {
+    title: "x-for",
+    description: "Repeat a block of HTML based on a data set",
+    pattern:
+`<template x-for="post in posts">
+<h2 x-text="post.title"></h2>
+</template>`
+  },
+  {
+    title: "x-if",
+    description: "Conditionally add/remove a block of HTML from the page entirely",
+    pattern:
+`<template x-if="open">
+<div>...</div>
+</template>`
+  },
+  {
+    title: "x-init",
+    description: "Run code when an element is initialized by Alpine",
+    pattern:
+`<div x-init="date = new Date()"></div>`
+  },
+  {
+    title: "x-effect",
+    description: "Execute a script each time one of its dependencies change",
+    pattern:
+`<div x-effect="console.log('Count is '+count)"></div>`
+  },
+  {
+    title: "x-ref",
+    description: "Reference elements directly by their specified keys using the $refs magic property",
+    pattern:
+`<input type="text" x-ref="content">
+ 
+<button x-on:click="navigator.clipboard.writeText($refs.content.value)">
+  Copy
+</button>`
+  },
+  {
+    title: "x-cloak",
+    description: "Hide a block of HTML until after Alpine is finished initializing its contents",
+    pattern:
+`<div x-cloak>
+...
+</div>`
+  },
+  {
+    title: "x-ignore",
+    description: "Prevent a block of HTML from being initialized by Alpine",
+    pattern:
+`<div x-ignore>
+...
+</div>`
+  },
+]
+
+export const attributesPrompt = getPromptFromFeatures(attributes)
+
+export const properties: FeatureAPI[] = [
+  {
+    title: "$store",
+    description: "Access a global store registered using Alpine.store(...)",
+    pattern: `<h1 x-text="$store.site.title"></h1>`
+  },
+  {
+    title: "$el",
+    description: "Reference the current DOM element",
+    pattern:`<div x-init="new Pikaday($el)"></div>`
+  },
+  {
+    title: "$dispatch",
+    description: "Dispatch a custom browser event from the current element",
+    pattern:
+`<div x-on:notify="...">
+  <button x-on:click="$dispatch('notify')">...</button>
+</div>`
+  },
+  {
+    title: "$watch",
+    description: "Watch a piece of data and run the provided callback anytime it changes",
+    pattern:
+`<div x-init="$watch('count', value => {
+  console.log('count is ' + value)
+})">...</div>`
+  },
+  {
+    title: "$refs",
+    description: "Reference an element by key (specified using x-ref)",
+    pattern:
+`<div x-init="$refs.button.remove()">
+<button x-ref="button">Remove Me</button>
+</div>`
+  },
+  {
+    title: "$nextTick",
+    description: "Wait until the next \"tick\" (browser paint) to run a bit of code",
+    pattern:
+`<div
+x-text="count"
+x-text="$nextTick(() => {"
+  console.log('count is ' + $el.textContent)
+})
+>...</div>`
+  },
+]
+
+export const propertiesPrompt = getPromptFromFeatures(properties)
+
+export const methods: FeatureAPI[] = [
+  {
+    title: "Alpine.data",
+    description: "Reuse a data object and reference it using x-data",
+    pattern:
+`<div x-data="dropdown">
+...
+</div>`
+  },
+  {
+    title: "Alpine.store",
+    description: "Declare a piece of global, reactive, data that can be accessed from anywhere using $store",
+    pattern:
+`<button @click="$store.notifications.notify('...')">
+Notify
+</button>
+
+...
+
+Alpine.store('notifications', {
+items: [],
+
+notify(message) { 
+  this.items.push(message)
+}
+})`
+  },
+]
+
+export const methodsPrompt = getPromptFromFeatures(methods)
+
+export const alpine = "# Alpine.js docs\n"+ attributesPrompt // + propertiesPrompt + methodsPrompt

src/daisy.mts

@@ -0,0 +1,5 @@
+export const daisy = `# DaisyUI docs
+## To create a nice layout, wrap each article in:
+\`<article class="prose"></article>\`
+## Buttons
+\`<button class="btn"></button>\``

src/index.mts

@@ -0,0 +1,206 @@
+import os from "node:os"
+import path from "node:path"
+import express from "express"
+import { python } from "pythonia"
+
+import { daisy } from "./daisy.mts"
+import { alpine } from "./alpine.mts"
+
+const transformers = await python('transformers')
+const { AutoGPTQForCausalLM, BaseQuantizeConfig } = await python ('auto_gptq')
+
+const modelName = "TheBloke/WizardCoder-15B-1.0-GPTQ"
+
+const use_triton = false // no NVIDIA Triton Inference Server
+
+const tokenizer = await transformers.AutoTokenizer.from_pretrained$(modelName, { use_fast: true })
+
+const model = await AutoGPTQForCausalLM.from_quantized$(modelName, {
+  use_safetensors: true,
+  device: 'cuda:0',
+  use_triton: false,
+  quantize_config: null
+})
+
+// Prevent printing spurious transformers error when using pipeline with AutoGPTQ
+await transformers.logging.set_verbosity(transformers.logging.CRITICAL)
+
+const pipe = await transformers.pipeline$("text-generation", { model, tokenizer })
+
+
+// define the CSS and JS dependencies
+const css = [
+  "/css/[email protected]",
+].map(item => `<link href="${item}" rel="stylesheet" type="text/css"/>`)
+.join("")
+
+const script = [
+  "/js/[email protected]",
+  "/js/[email protected]"
+].map(item => `<script src="${item}"></script>`)
+.join("")
+
+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 = 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("/app", async (req, res) => {
+
+  if (`${req.query.prompt}`.length < minPromptSize) {
+    res.write(`prompt too short, please enter at least ${minPromptSize} characters`)
+    res.end()
+    return
+  }
+  // naive implementation: we say we are out of capacity
+  if (pending.queue.length >= maxParallelRequests) {
+    res.write("sorry, max nb of parallel requests reached")
+    res.end()
+    return
+  }
+  // alternative approach: kill old queries
+  // while (pending.queue.length > maxParallelRequests) {
+  //   endRequest(pending.queue[0], 'max nb of parallel request reached')
+  // }
+
+  const id = `${pending.total++}`
+  console.log(`new request ${id}`)
+
+  pending.queue.push(id)
+
+  const prefix = `<html><head>${css}${script}`
+  res.write(prefix)
+
+  req.on("close", function() {
+    // console.log("browser asked to close the stream for some reason.. let's ignore!")
+    endRequest(id, "browser asked to end the connection")
+  })
+
+  // for testing we kill after some delay
+  setTimeout(() => {
+    endRequest(id, `timed out after ${timeoutInSec}s`)
+  }, timeoutInSec * 1000)
+
+
+  const finalPrompt = `# Context
+Generate this webapp: ${req.query.prompt}.
+# Documentation
+${daisy}
+# Guidelines
+- Do not write a tutorial! This is a web app!
+- Never repeat the instruction, instead 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.
+- 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, and try to make the code compact (short function names etc)
+- Use a central layout by wrapping everything in a \`<div class="flex flex-col justify-center">\`
+# HTML Code of the final app:
+${prefix}`
+
+      
+  try {
+
+    // be careful: if you input a prompt which is too large, you may experience a timeout
+    // const inputTokens = await llm.tokenize(finalPrompt)
+    console.log("initializing the generator (may take 30s or more)")
+
+    const query = "How do I sort a list in Python?"
+    const prompt = `<|system|>\n<|end|>\n<|user|>\n${query}<|end|>\n<|assistant|>`
+
+    //  We use a special <|end|> token with ID 49155 to denote ends of a turn
+    const outputs = await pipe({
+      prompt,
+      max_new_tokens: 256,
+      do_sample: true,
+      temperature: 0.2,
+      top_k: 50,
+      top_p: 0.95,
+      eos_token_id: 49155
+    })
+
+    // You can sort a list in Python by using the sort() method. Here's an example:\n\n```\nnumbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]\nnumbers.sort()\nprint(numbers)\n```\n\nThis will sort the list in place and print the sorted list.
+    const tmp = outputs[0]['generated_text']
+
+    process.stdout.write(tmp)
+    res.write(tmp)
+    /*
+    const inputTokens = await tokenizer.tokenize("def fibbinacci(")
+    console.log('inputTokens:', inputTokens)
+    const tmp = await llm.generate_tokens$({
+      tokens: inputTokens,
+      max_length: 64,
+      include_prompt_in_result: false,
+    })
+    process.stdout.write(tmp)
+    res.write(tmp)
+    console.log("generator initialized, beginning token streaming..")
+    for await (const token of generator) {
+      if (!pending.queue.includes(id)) {
+        break
+      }
+      const tmp = await tokenizer.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%20pong%20game%20clone%20in%20HTML,%20made%20using%20the%20canvas`) })
+

src/test.mts

@@ -0,0 +1,40 @@
+import path from "node:path"
+import { python } from "pythonia"
+
+const transformers = await python('transformers')
+const { AutoGPTQForCausalLM, BaseQuantizeConfig } = await python ('auto_gptq')
+
+const modelName = "TheBloke/WizardCoder-15B-1.0-GPTQ"
+
+const use_triton = false // no NVIDIA Triton Inference Server
+
+const tokenizer = await transformers.AutoTokenizer.from_pretrained$(modelName, { use_fast: true })
+
+const model = await AutoGPTQForCausalLM.from_quantized$(modelName, {
+  use_safetensors: true,
+  device: 'cuda:0',
+  use_triton: false,
+  quantize_config: null
+})
+
+// Prevent printing spurious transformers error when using pipeline with AutoGPTQ
+await transformers.logging.set_verbosity(transformers.logging.CRITICAL)
+
+const pipe = await transformers.pipeline$("text-generation", { model, tokenizer })
+
+const query = "How do I sort a list in Python?"
+const prompt = `<|system|>\n<|end|>\n<|user|>\n${query}<|end|>\n<|assistant|>`
+
+//  We use a special <|end|> token with ID 49155 to denote ends of a turn
+const outputs = await pipe({
+  prompt,
+  max_new_tokens: 256,
+  do_sample: true,
+  temperature: 0.2,
+  top_k: 50,
+  top_p: 0.95,
+  eos_token_id: 49155
+})
+
+// You can sort a list in Python by using the sort() method. Here's an example:\n\n```\nnumbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]\nnumbers.sort()\nprint(numbers)\n```\n\nThis will sort the list in place and print the sorted list.
+console.log(outputs[0]['generated_text'])

test.py

@@ -0,0 +1,45 @@
+import sys
+import os
+import fire
+import torch
+import transformers
+import json
+import jsonlines
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+try:
+    if torch.backends.mps.is_available():
+        device = "mps"
+except:
+    pass
+
+base_model = "./models/WizardCoder-15B-V1.0"
+load_8bit = False
+
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+if device == "cuda":
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model,
+        load_in_8bit=load_8bit,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+elif device == "mps":
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model,
+        device_map={"": device},
+        torch_dtype=torch.float16,
+    )
+model.config.pad_token_id = tokenizer.pad_token_id
+if not load_8bit:
+    model.half()
+
+model.eval()
+if torch.__version__ >= "2" and sys.platform != "win32":
+  model = torch.compile(model)

tsconfig.json

@@ -0,0 +1,12 @@
+{
+   "compilerOptions": {
+    "allowJs": true,
+    "esModuleInterop": true,
+    "allowSyntheticDefaultImports": true,
+    "module": "nodenext",
+    "noEmit": true,
+    "allowImportingTsExtensions": true,
+    "target": "es2017"
+  },
+  "include": ["**/*.ts", "**/*.mts"],
+}