sveltekit-python-vercel v0.4.1

sveltekit-python-vercel

Write Python endpoints in SvelteKit and seamlessly deploy them to Vercel.

• Current Features
• Installing
• Testing Locally
• Deploying to Vercel
• Example
  • Backend Caveats
• Fork of sveltekit-modal
• Possible future plans

This is very much in beta.

Current Features

• Write +server.py files nearly the same way you would write +server.js files
• Deploy (quasi) automatically to Vercel Serverless

Installing

• Open or set up your SvelteKit project
• Install SvelteKit's Vercel adapter: pnpm i -D @sveltejs/adapter-vercel
• Install with pnpm i -D sveltekit-python-vercel

• Update your vite.config.js

  import { defineConfig } from "vite";
  import { sveltekit } from "@sveltejs/kit/vite";
  import { sveltekit_python_vercel } from "sveltekit-python-vercel/vite";
  
  export default defineConfig(({ command, mode }) => {
    return {
      plugins: [sveltekit_python_vercel(), sveltekit()],
    };
  });

• Update your svelte.config.js:

  import adapter from "@sveltejs/adapter-vercel"; // Use the vercel adapter
  import { vitePreprocess } from "@sveltejs/kit/vite";
  
  /** @type {import('@sveltejs/kit').Config} */
  const config = {
    preprocess: vitePreprocess(),
    kit: {
      adapter: adapter(),
      moduleExtensions: [".js", ".ts", ".py"], // add ".py" to resolve +server.py endpoints
    },
  };
  
  export default config;

• Update your vercel.json

  • The build command prepares all your endpoints and copies them to the /api directory where Vercel looks for functions
  • Functions and Routes tell Vercel how to run and redirect function calls

  {
    "buildCommand": "node ./node_modules/sveltekit-python-vercel/esm/src/vite/sveltekit_python_vercel/bin.mjs; vite build",
    "functions": {
      "api/**/*.py": {
        "runtime": "@vercel/python@3.0.7"
      }
    },
    "routes": [
      {
        "src": "/api/(.*)",
        "dest": "api/index.py"
      }
    ]
  }

• Write some +server.py endpoints. See the example section below.

Testing Locally

Using Poetry to manage your virtual environments with this package is recommended.

• Run poetry init to create a new virtual environment, and follow the steps. Or simply create a pyproject.toml like the one below.

  [tool.poetry]
  name = "sveltekit-python-example"
  version = "0.1.0"
  description = ""
  authors = ["Your Name <email@gmail.com>"]
  readme = "README.md"
  
  [tool.poetry.dependencies]
  python = "^3.9"
  fastapi = "^0.95.2"
  uvicorn = "^0.22.0"
  
  [tool.poetry.group.dev.dependencies]
  watchfiles = "^0.19.0"
  
  [build-system]
  requires = ["poetry-core"]
  build-backend = "poetry.core.masonry.api"

• Required packages are python3.9 (that is what Vercel's runtime uses), fastapi, and uvicorn.

• Install whatever other dependencies you need from pypi using poetry add package-name

• Enter your virtual env with poetry shell

• Run pnpm dev or npm dev
  • You should see both the usual SvelteKit server start as well as the unvicorn server (by default on http://0.0.0.0:8000) in the console.

Deploying to Vercel

• At the moment this requires a tiny bit of extra labor besides just pushing to your repository. I believe this is because of the way Vercel looks for serverless functions, but I hope to make this a bit easier in the future.

• When you make changes to your python endpoints, you have to manually regenerate the /api folder by running:

  1. poetry export -f requirements.txt --output requirements.txt --without-hashes
  2. node ./node_modules/sveltekit-python-vercel/esm/src/vite/sveltekit_python_vercel/bin.mjs
• Then commit requirements.txt and the changes in /api and push.

Note:

• To make this a bit smoother, you can add a script to you package.json:

  "scripts": {
    ...
    "py-update": "poetry export -f requirements.txt --output requirements.txt --without-hashes; node ./node_modules/sveltekit-python-vercel/esm/src/vite/sveltekit_python_vercel/bin.mjs"
  }

  • and then just run pnpm py-update

Example

• Frontend: /src/routes/py/+page.svelte

  <script lang="ts">
    let a = 0;
    let b = 0;
    let total = 0;
  
    async function pyAddPost() {
      const response = await fetch("/py", {
        method: "POST",
        body: JSON.stringify({ a, b }),
        headers: {
          "content-type": "application/json",
        },
      });
      let res = await response.json();
      total = res.sum;
    }
  
    async function pyAddGet() {
      const response = await fetch(`/py?a=${a}&b=${b}`, {
        method: "GET",
        headers: {
          "content-type": "application/json",
        },
      });
  
      let res = await response.json();
      total = res.sum;
    }
  </script>
  
  <h1>This is a SvelteKit page with a python backend.</h1>
  
  <h3>POST Example</h3>
  <form>
    <input type="number" name="a" placeholder="Number 1" bind:value="{a}" />
    <input type="number" name="b" placeholder="Number 2" bind:value="{b}" />
    <button on:click|preventDefault="{pyAddPost}">Add</button>
  </form>
  <h4>Total: {total}</h4>
  
  <br />
  
  <h3>GET Example</h3>
  <form>
    <input type="number" name="a" placeholder="Number 1" bind:value="{a}" />
    <input type="number" name="b" placeholder="Number 2" bind:value="{b}" />
    <button on:click|preventDefault="{pyAddGet}">Add</button>
  </form>
  <h4>Total: {total}</h4>

• Backend: /src/routes/py/+server.py

  from pydantic import BaseModel

class NumberSet(BaseModel): a: float b: float

async def POST(data: NumberSet): return {"sum": data.a + data.b}

async def GET(a: float, b: float): return {"sum": a + b}

### Backend Caveats

There are currently a few things that have to be worked around.

- `GET` endpoints are directly fed the parameters from the url, so when you define an endpoint
- All other endpoints are fed the body as a JSON. The recommended way to deal with this is to use a pydantic model and pass it as the singular input to the function.

See the example above.

## Fork of `sveltekit-modal`

Check out the awesome [sveltekit-modal](https://github.com/semicognitive/sveltekit-modal) package by [@semicognitive](https://github.com/semicognitive), the original way to get your python code running in SvelteKit. Modal even has GPU support for running an entire ML stack within SvelteKit.

## Possible future plans

- [X] Add hot reloading in dev mode
- [ ] Generate endpoints (/api folder) automatically during build
- [ ] Auto create requirements.txt from pyproject.toml (both related to vercel functions being checked/handled before build)
- [ ] Add form actions
- [ ] Add load functions
- [ ] Add helper functions to automatically call API endpoints in project\