japronto | fast Python 3.5+ HTTP toolkit | HTTP library
kandi X-RAY | japronto Summary
kandi X-RAY | japronto Summary
Japronto (from Portuguese "já pronto" /ˈʒa pɾõtu/ meaning "already done") is a screaming-fast, scalable, asynchronous Python 3.5+ HTTP toolkit integrated with pipelining HTTP server based on uvloop and picohttpparser. It's targeted at speed enthusiasts, people who like plumbing and early adopters. You can read more in the release announcement on medium.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of japronto
japronto Key Features
japronto Examples and Code Snippets
from japronto import Application
def hello(request):
return request.Response(text='Hello world!')
app = Application()
app.router.add_route('/', hello)
app.run(debug=True)
import random
from http.cookies import SimpleCookie
from japronto.app import Application
# Providing just a text argument yields a `text/plain` response
# encoded with `utf8` codec (charset set accordingly)
def text(request):
return request.Re
import os.path
import sqlite3
from functools import partial
from japronto import Application
def add_todo(request):
cur = request.cursor
todo = request.json["todo"]
cur.execute("""INSERT INTO todos (todo) VALUES (?)""", (todo,))
la
from japronto import Application, RouteNotFoundException
# These are our custom exceptions we want to turn into 200 response.
class KittyError(Exception):
def __init__(self):
self.greet = 'meow'
class DoggieError(Exception):
def _
from collections import namedtuple
WILDCARD = None
Rule = namedtuple("Rule", ["Country", "Criteria1", "Criteria2"])
rules = [
Rule("UK", "Somestring1", "Somestring3"),
Rule("UK", "Somestring1", "Somestring2"),
Rule("US", "So
Community Discussions
Trending Discussions on japronto
QUESTION
I am creating a Python web server with japronto in order to serve results from a Keras neural network trained on the Kaggle higgs boson dataset. I get this in the logs of the server:
...ANSWER
Answered 2018-Mar-15 at 07:49Looking at the print, seems like you have a numpy array; the shape (28,) can be misleading.
Simply do data = data.reshape((1,28))
to reshape the input into a proper nd-array with the required shape.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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