Api for getting bin info and getting encrypted card details for adyen.

Related tags

Deep Learningadyen-enc-and-bin-info
Overview

Bin Info And Adyen Cse Enc Python

api for getting bin info and getting encrypted card details for adyen.

GitHub stars GitHub forks Maintenance Website shields.io Ask Me Anything ! License

Installation

Local Installation

git clone http://www.github.com/r0ld3x/adyen-enc-and-bin-info
cd adyen-enc-and-bin-info
pip install -r requirements.txt
uvicorn index:app

Deploy

Usage

website.com = your heroku website name

BIN INFO:-

curl -X 'GET' \
  'https://adyen-enc-and-bin-info.herokuapp.com/bin/458578' \
  -H 'accept: application/json'

Request URL: https://adyen-enc-and-bin-info.herokuapp.com/bin/458578 Return:

{
  "bin": "458578",
  "bank": "PJSC CB EUROBANK",
  "country_iso": "UA",
  "country": "UA",
  "flag": "🇺🇦",
  "vendor": "VISA",
  "type": "DEBIT",
  "level": "CLASSIC",
  "prepaid": false
}

Return status code 200 if success else return 404 if bin not found

ADYEN ENC:-

curl -X 'POST' \
  'https://adyen-enc-and-bin-info.herokuapp.com/adyen/' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "card": 5415900002240330,
  "month":7,
  "year": 2024,
  "cvv": 544,
  "adyen_key": "10001|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
  "adyen_version": "_0_1_25"
}'

Request URL: https://adyen-enc-and-bin-info.herokuapp.com/bin/458578 Return:

{
  "card": "adyenjs_0_1_25%24pd91Sl9SF1eTx%2BZrn3b9uL8dh%2BmO6HJrNQsf%2BmQ%2F2185qXMACyys4OCwKEpeBuT9j4%2FdLCfqeVGS0gdRIZRKDLvO689pTqvFnJ1tTiXwEEYkvCJ73bSGjPzPNexi%2FWo3KmoiAPWLwHWf514AKSCb1luoztp%2BZKxpg6IuqwQR%2FtsFBkrq761AQw6TwMtMxSr%2Fzs%2Fl6WjkTOBv5GviiKKHjOCpr1Y5eMv6t%2F9cjuDIYF9AWNx4F9o4qraNeAKl%2BVjs%2Fpm9aFV16QeYWpvjO24Rpb865V6%2BgQJW%2F8I8jRbpy6wlS3Mo%2FOSUBrOZqcrw8GBn8Qtf8q74kUXRdhtywGQ%2Bgg%3D%3D%2465MDJ9nl42hYDvxIYIow9ydXvjc3HPHXZFziT8yCuulYjzpQU7QXPJcev0eP35n5k5KIRbep5zxVX6ZX3n8saXsWwwKiZhonmtPbzSmc6T262Zc%2FJmW8K6mofH7qyteM",
  "month": "adyenjs_0_1_25%24lpdea4MvYqJm4YRdufTpwKGiem3UqLHia4kJ0Q5rb6uvNyKlL9J18M9HPYH%2F3Y37lbmPIgMmGNCYoK5%2BaTj5uquRtQ1njRP55T%2F6EudhpIQMKYaw4M6gQjdIrqosVplnps%2FD%2BnmuwHJM0DWIzZC8z30ZCz4Sl6RFBL3DPj0OhvjR9MvoAUwOHqJYc%2FF9zmtTq8vA5XCYAhVisBiqX7fj547almWBEcthyYw6LEg3BYMcs4MdJahEwUa17zDDKwLlLhvkI3m0qsDc8cTFjmYtnTsxVVSEttbUe0ljQJfVrRRPtcMGHNSA5JzWGf5mMfevjciQeAXRVFolIG6283qUnw%3D%3D%24%2FjDUAJFl4B1563Tw2p76GjeHnz03b0jhFrINlCYln1v81Omn4BbnEGnp7dzD3dpx6krXpg0P%2FCq1i1lEnG4B1v1texUPMUZ9%2Bm6AT0QUI3u%2BeuJ%2BxDs%3D",
  "year": "adyenjs_0_1_25%24btmuqQyBocIYHkfdrzowdn5EeJMsrmMcbSUX6DtlOA4Gu%2BlrNunyCwsovndkApfE6A9PYTCrsqUkJ%2F4iDizHkX4Ri%2FY24UfGjUzDbUjyHzhlM3f3ktgU4afyPT3Nb%2FoMf7gbreBJApdbxxh4Zz5jh%2BOb2znoEMM0MgoQ0HTVDy7CkNEKtbYxA72g1rz32lVJHlnTE7Urd2NkQVR5j6Js9PVkNfwRLiUUnZJN6p68WcShP0nUiptciJnMiP%2F3W6LgsQ9rS9PKCxcySSqXaW2ncgXX2pRgmCLjzR6yHKClzrcc%2BUqQ6D6br7vbACXv8OO877JGZVJp9lEqJ1tyQAZBnA%3D%3D%24s%2BlEPjpYoMMZIH8%2B75KqLOkCnKvajNHrNuEq8YmvCT3nw42cRQOASN5Xd34hWbdStKXQNfOVfD0RT64ebbXLJoHSvgB5nnwwB4Ps4n2aPWXbbK8789fY8w%3D%3D",
  "cvv": "adyenjs_0_1_25%24pwHRvu2ys6zXTUaabbjtXW6kZGZhojK1WoxqSFxkO44vvRZUzaIzWwost4mRvyaTE%2F%2FXv%2FSanWXPW4vCPJzqred%2F2atsz%2FzYuNBbUT9C1%2Bga9rgX7gXKRujS5lZFf18QXlG%2BBDERhtav1CuxbsMTmyaa4QLJ9BwohZgDHvEZW%2BOThw2yQTi5GlgwauTJbiw%2BCYgzKEqk24yeUSLQGKz4yD0R2wvILFJaWzV%2B0NBnMQ8ZWEdtTRL2PY%2BHHb9uwTMBJKcdZn7qDWGT6Acxjh4HMLaI5%2FkgCch6JRsUEq63L6ulqcw6kDYGCaCZ%2BFvPmPssNFzJK6IpX%2F%2BKESxfGPBIRQ%3D%3D%246WruUcmWAV4a2Ve3SKzjTx1usXSSIf3RiZxZkdMly%2Fc97CWO5pRsMiXGUlZyB8KKctoM0iiMacnPcPN%2F%2B1Iamw8z1xriaPCdeCuGCqwGx1o%3D"
}

Return enc_card,enc_month,enc_year,enc_cvv

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

• MADE BY > Roldex

License

MIT

Owner
Roldex Stark
BEYOND YOUR LIMITS
Roldex Stark
GitHub Repository
Age and Gender prediction using Keras

cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span

XN3UR0N 58 May 03, 2022
Open-source Monocular Python HawkEye for Tennis

Tennis Tracking 🎾 Objectives Track the ball Detect court lines Detect the players To track the ball we used TrackNet - deep learning network for trac

ArtLabs 188 Jan 08, 2023
improvement of CLIP features over the traditional resnet features on the visual question answering, image captioning, navigation and visual entailment tasks.

CLIP-ViL In our paper "How Much Can CLIP Benefit Vision-and-Language Tasks?", we show the improvement of CLIP features over the traditional resnet fea

310 Dec 28, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

Yu 1.4k Jan 01, 2023
Inteligência artificial criada para realizar interação social com idosos.

IA SONIA 4.0 A SONIA foi inspirada no assistente mais famoso do mundo e muito bem conhecido JARVIS. Todo mundo algum dia ja sonhou em ter o seu própri

Vinícius Azevedo 2 Oct 21, 2021
根据midi文件演奏“风物之诗琴”的脚本 "Windsong Lyre" auto play

Genshin-lyre-auto-play 简体中文 | English 简介 根据midi文件演奏“风物之诗琴”的脚本。由Python驱动,在此承诺， ⚠️ 项目内绝不含任何能够引起安全问题的代码。 前排提示：所有键盘在动但是原神没反应的都是因为没有管理员权限，双击run.bat或者以管理员模式

御坂17032号 386 Jan 01, 2023
Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19 (Oral).

Pose-Transfer Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19(Oral). The paper is available here. Video generation

Tengteng Huang 679 Jan 04, 2023
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
This repository contains the code used in the paper "Prompt-Based Multi-Modal Image Segmentation".

Prompt-Based Multi-Modal Image Segmentation This repository contains the code used in the paper "Prompt-Based Multi-Modal Image Segmentation". The sys

Timo Lüddecke 305 Dec 30, 2022
Python implementation of "Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation"

MIPNet: Multi-Instance Pose Networks This repository is the official pytorch python implementation of "Multi-Instance Pose Networks: Rethinking Top-Do

Rawal Khirodkar 57 Dec 12, 2022
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Race Wang 57 Dec 27, 2022
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Learning to Reconstruct 3D Manhattan Wireframes From a Single Image This repository contains the PyTorch implementation of the paper: Yichao Zhou, Hao

Yichao Zhou 50 Dec 27, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 07, 2023
Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

U-GAT-IT — Official PyTorch Implementation : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Imag

Hyeonwoo Kang 2.4k Jan 04, 2023
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Nervana 3.9k Dec 20, 2022
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021 [Projec

Zhengqi Li 583 Dec 30, 2022
A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Wilderness Scavenger: 3D Open-World FPS Game AI Challenge This is a platform for intelligent agent learning based on a 3D open-world FPS game develope

46 Nov 24, 2022
A library of multi-agent reinforcement learning components and systems

Mava: a research framework for distributed multi-agent reinforcement learning Table of Contents Overview Getting Started Supported Environments System

InstaDeep Ltd 463 Dec 23, 2022
BMVC 2021: This is the github repository for "Few Shot Temporal Action Localization using Query Adaptive Transformers" accepted in British Machine Vision Conference (BMVC) 2021, Virtual

FS-QAT: Few Shot Temporal Action Localization using Query Adaptive Transformer Accepted as Poster in BMVC 2021 This is an official implementation in P

Sauradip Nag 14 Dec 09, 2022