Jar is a toy Python web framework, implemented in about 200 lines of code (see cli.py). It's open source.
I built it to explore some ideas around framework APIs. Please don't actually use it.
It deploys to Vercel via the Build Output API.
It's called Jar because it has almost no features and you need to fill it up yourself!
Jar uses file-system routing.
Pages are Python files that render content. They're put in the pages directory.
Public files (like CSS and other media files) go in the public directory and are served from the root path.
A typical project is structured like this:
project/
├─ pages/
│ ├─ index.py
├─ public/
│ ├─ favicon.ico
Checkout the source for the kitchen sink example, or this docs website.
Generated once at build time. Served as a static file.
import time
def render(data):
return f"<h1>{data['text']} I was built at {data['time']}</h1>", {}
def data():
return {
"text": "Hello, World!",
"time": time.time(),
}
Generated for each request. Similar to Server-Side Rendering (SSR).
import json
import time
def render(data):
content = f"<h1>Fresh Page rendered at {data['time']}</h1>"
content += f"<code>{data['request']}</code>"
return content, {}
def data(request):
return {
"time": time.time(),
"request": json.dumps({
"method": request.method,
"path": request.path,
"headers": request.headers,
"body": request.body
}, indent=4)
}
def config():
return {
"fresh": {}
}
Similar to Next.js's Incremental Static Regeneration.
import time
def render(data):
return f"<h1>Regenerated Page, last rendered at {data['time']}</h1>", {}
def data(request=None):
return {
"time": time.time(),
}
def config():
return {
"regenerate": {
"every": 5
}
}
The data and config functions are optional. The properties that config returns defines the type of page. The default page is a build page.
Build pages don't have access to a request object.
The response that render returns is a tuple of body, meta where meta can have status_code: int and/or headers: dict keys e.g. {"status_code": 200, "headers": {"some":"header"}}.
If you are using packages (i.e. something you install with pip) you have to install them locally inside your project before building the project with the CLI.
e.g. with pip you can run pip3 install -r requirements.txt --target . at the project's root.
See how this docs website is deployed for an example of this (deploy-docs.sh).