PassAPI is a password generator in hash format and fully developed in Python, with the aim of teaching how to handle and build

Related tags

Deep LearningPassAPI
Overview

simple, elegant and safe

Introduction

PassAPI is a password generator in hash format and fully developed in Python, with the aim of teaching how to handle and build

  • Strong points:

    • Token verification by authorization header 🎉
    • Fully OpenSource 🎉
    • Already comes with ratelimit 🎉
    • Easy to understand 🎉
    • Frequently updated 🎉
  • Weak points:

    • Possible lack of functions, but we will update with time 🔍

Requirements

Recommended Python 3.6+

Developed in Python 3.10.1

Installation

$ pip install -r requirements.txt


--> 100%

Using

Starting

  • Starting the main.py, in your console it should work like this:
INFO:     Started server process [2364]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8080 (Press CTRL+C to quit)
  • Note: if this appears in your console, don't worry just add the hash in your .env
[!] You didn't generate a hash to protect your APIKEY, so we generated one for you!
 
[+] Here: "Hash"
[+] You should put it there in .env  

Using the PassAPI

  • You will need some application to inject the API (REST Client), for example:

    • REST Client which is an extension of Visual Studio Code
    • You can use FastAPI application by accessing your server url and adding /docs
      • http://127.0.0.1/docs
  • and others...

Testing

  • Here is an example POST (Using the REST Client):
POST http://127.0.0.1:8080/ HTTP/1.1
Content-Type: application/json
Authorization: "HASH"

{
    "length": 5
}
  • Output:
{
    "detail": [
      {
        "password":"phG[P",
        "timestamp":"2022-03-01 23:14:06"
      }
    ]
}
  • Note: The rate limit already configured is 50 requests in 1 minute, but you can change it in /src/routers/routers.py

License

PassAPI is released under the MIT License. Check LICENSE file for more information.

You might also like...
Build fully-functioning computer vision models with PyTorch
Build fully-functioning computer vision models with PyTorch

Detecto is a Python package that allows you to build fully-functioning computer vision and object detection models with just 5 lines of code. Inferenc

UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Neon: an add-on for Lightbulb making it easier to handle component interactions

Neon Neon is an add-on for Lightbulb making it easier to handle component interactions. Installation pip install git+https://github.com/neonjonn/light

A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.
A fast poisson image editing implementation that can utilize multi-core CPU or GPU to handle a high-resolution image input.

Poisson Image Editing - A Parallel Implementation Jiayi Weng (jiayiwen), Zixu Chen (zixuc) Poisson Image Editing is a technique that can fuse two imag

An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

EasyDatas An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results Installation pip install git+https

Torch-ngp - A pytorch implementation of the hash encoder proposed in instant-ngp
Torch-ngp - A pytorch implementation of the hash encoder proposed in instant-ngp

HashGrid Encoder (WIP) A pytorch implementation of the HashGrid Encoder from ins

BTC-Generator - BTC Generator With Python

Что такое BTC-Generator? Это генератор чеков всеми любимого @BTC_BANKER_BOT Для

Simple ONNX operation generator. Simple Operation Generator for ONNX.
Simple ONNX operation generator. Simple Operation Generator for ONNX.

sog4onnx Simple ONNX operation generator. Simple Operation Generator for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools Key concept V

Releases(1.0.1)
Owner
Johnsz
Johnsz
Pre-trained BERT Models for Ancient and Medieval Greek, and associated code for LaTeCH 2021 paper titled - "A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek"

Ancient Greek BERT The first and only available Ancient Greek sub-word BERT model! State-of-the-art post fine-tuning on Part-of-Speech Tagging and Mor

Pranaydeep Singh 22 Dec 08, 2022
PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection?

PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Toyota Research Institute - Machine Learning 364 Dec 27, 2022
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes (CVPR 2021 Oral)

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces Official code release for NGLOD. For technical details, please refer t

659 Dec 27, 2022
PyTorch implementation of PP-LCNet

PP-LCNet-Pytorch Pre-Trained Models Google Drive p018 Accuracy Models Top1 Top5 PPLCNet_x0_25 0.5186 0.7565 PPLCNet_x0_35 0.5809 0.8083 PPLCNet_x0_5 0

24 Dec 12, 2022
Train emoji embeddings based on emoji descriptions.

emoji2vec This is my attempt to train, visualize and evaluate emoji embeddings as presented by Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko

Miruna Pislar 17 Sep 03, 2022
Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

Jurijs Nazarovs 7 May 03, 2022
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation The code of: Context Decoupling Augmentation for Weakly Supervised Semanti

54 Dec 12, 2022
A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)

EfficientNet PyTorch Quickstart Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with: from efficientnet_pytorch impor

Luke Melas-Kyriazi 7.2k Jan 06, 2023
Official pytorch implementation of "Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization" ACMMM 2021 (Oral)

Feature Stylization and Domain-aware Contrastive Loss for Domain Generalization This is an official implementation of "Feature Stylization and Domain-

22 Sep 22, 2022
Tools for investing in Python

InvestOps Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction This is a Python package with simple and effective

24 Nov 26, 2022
X-VLM: Multi-Grained Vision Language Pre-Training

X-VLM: learning multi-grained vision language alignments Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts. Yan Zeng, Xi

Yan Zeng 286 Dec 23, 2022
Voila - Voilà turns Jupyter notebooks into standalone web applications

Rendering of live Jupyter notebooks with interactive widgets. Introduction Voilà turns Jupyter notebooks into standalone web applications. Unlike the

Voilà Dashboards 4.5k Jan 03, 2023
A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

CoVA: Context-aware Visual Attention for Webpage Information Extraction Abstract Webpage information extraction (WIE) is an important step to create k

Keval Morabia 41 Jan 01, 2023
TGS Salt Identification Challenge

TGS Salt Identification Challenge This is an open solution to the TGS Salt Identification Challenge. Note Unfortunately, we can no longer provide supp

neptune.ai 123 Nov 04, 2022
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

Differential Privacy (DP) Based Federated Learning (FL) Everything about DP-based FL you need is here. (所有你需要的DP-based FL的信息都在这里) Code Tip: the code o

wenzhu 83 Dec 24, 2022
Greedy Gaussian Segmentation

GGS Greedy Gaussian Segmentation (GGS) is a Python solver for efficiently segmenting multivariate time series data. For implementation details, please

Stanford University Convex Optimization Group 72 Dec 07, 2022
This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

Soomin Park 28 Dec 16, 2022
What can linearized neural networks actually say about generalization?

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

gortizji 11 Dec 09, 2022
Collection of in-progress libraries for entity neural networks.

ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio

25 Dec 01, 2022
Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"

dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com

1 Dec 17, 2021