Framework web SnakeServer.

Overview

SnakeServer - Framework Web 🐍

BADGE BADGE BADGE

Documentação oficial do framework SnakeServer.

Conteúdo

Sobre

O SnakeServer é um framework web sendo desenvolvido para Python com o foco em segurança. Nele, será possível receber solicitações GET e POST, definir headers de resposta, obter parâmetros de URL, retornar páginas HTML e outros tipos de conteúdo, e muitas outras funções.

Como contribuir

Veja como você pode ajudar no SnakeServer:

Enviar relatórios de segurança

Um dos nossos princípios é a segurança, para isso, semanalmente, diversos testes são feitos na última e penúltima versão lançada, para corrigir erros que possam comprometer o servidor.

Se você tem interesse em ajudar desta forma, acesse o arquivo PENTEST.txt para melhores informações.

Pull Requests

Revise o código, encontre bugs, erros, melhorias no código e faça seu pull request!

Todas as pull request são analisadas o mais rápido possível, sendo aceita caso o que você fez seja beneficiante para o código.

Primeiros passos

Aqui daremos o primeiro passo na criação de um servidor utilizando o SnakeServer!

Hello World

Veja um simples hello world utilizando nosso framework:

from snake import Snake

# Função que retorna um Hello World
def hello():
    return 'Hello World powered by SnakeServer!'

# Instanciando a classe Snake e definindo Host e Port
server = Snake(host='127.0.0.1', port=5500)

# Registrando rota e iniciando servidor
server.add_new_route(name='/', method='POST', target=hello)
server.start()

Para entender e aprender mais sobre o SnakeServer, acesse a documentação oficial.

Licença

MIT © 2021 Jaedson Silva

Owner
Jaedson Silva
Python developer in web area.
Jaedson Silva
Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

Pau Riba 310 Nov 07, 2022
FeTaQA: Free-form Table Question Answering

FeTaQA: Free-form Table Question Answering FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form

Language, Information, and Learning at Yale 40 Dec 13, 2022
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code

Athar Sefid 6 Nov 02, 2022
A python interface for training Reinforcement Learning bots to battle on pokemon showdown

The pokemon showdown Python environment A Python interface to create battling pokemon agents. poke-env offers an easy-to-use interface for creating ru

Haris Sahovic 184 Dec 30, 2022
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Rahul Vigneswaran 1 Jan 17, 2022
This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google.

This repository contains an overview of important follow-up works based on the original Vision Transformer (ViT) by Google.

75 Dec 02, 2022
Spherical Confidence Learning for Face Recognition, accepted to CVPR2021.

Sphere Confidence Face (SCF) This repository contains the PyTorch implementation of Sphere Confidence Face (SCF) proposed in the CVPR2021 paper: Shen

Maths 70 Dec 09, 2022
A project that uses optical flow and machine learning to detect aimhacking in video clips.

waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che

waldo.vision 542 Dec 03, 2022
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
Pointer networks Tensorflow2

Pointer networks Tensorflow2 原文:https://arxiv.org/abs/1506.03134 仅供参考与学习,内含代码备注 环境 tensorflow==2.6.0 tqdm matplotlib numpy 《pointer networks》阅读笔记 应用场景

HUANG HAO 7 Oct 27, 2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation [arxiv] This is the official repository for CDTrans: Cross-domain Transformer for

238 Dec 22, 2022
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
tensorflow code for inverse face rendering

InverseFaceRender This is tensorflow code for our project: Learning Inverse Rendering of Faces from Real-world Videos. (https://arxiv.org/abs/2003.120

Yuda Qiu 18 Nov 16, 2022
A PyTorch Implementation of the Luna: Linear Unified Nested Attention

Unofficial PyTorch implementation of Luna: Linear Unified Nested Attention The quadratic computational and memory complexities of the Transformer’s at

Soohwan Kim 32 Nov 07, 2022
PyTorch implementation for the paper Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime Created by Prarthana Bhattacharyya. Disclaimer: This is n

Prarthana Bhattacharyya 5 Nov 08, 2022
SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

SuMa++: Efficient LiDAR-based Semantic SLAM This repository contains the implementation of SuMa++, which generates semantic maps only using three-dime

Photogrammetry & Robotics Bonn 701 Dec 30, 2022
HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep.

HODEmu HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep. and emulates satellite abundance as a function of co

Antonio Ragagnin 1 Oct 13, 2021
A framework for attentive explainable deep learning on tabular data

🧠 kendrite A framework for attentive explainable deep learning on tabular data 💨 Quick start kedro run 🧱 Built upon Technology Description Links ke

Marnix Koops 3 Nov 06, 2021
A simple python library for fast image generation of people who do not exist.

Random Face A simple python library for fast image generation of people who do not exist. For more details, please refer to the [paper](https://arxiv.

Sergei Belousov 170 Dec 15, 2022
Single-Shot Motion Completion with Transformer

Single-Shot Motion Completion with Transformer 👉 [Preprint] 👈 Abstract Motion completion is a challenging and long-discussed problem, which is of gr

FuxiCV 78 Dec 29, 2022