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
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and t

305 Dec 16, 2022
A Model for Natural Language Attack on Text Classification and Inference

TextFooler A Model for Natural Language Attack on Text Classification and Inference This is the source code for the paper: Jin, Di, et al. "Is BERT Re

Di Jin 418 Dec 16, 2022
Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method

C++/ROS Source Codes for "Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method" published in IEEE Trans. Intelligent Transportation Systems

Bai Li 88 Dec 23, 2022
This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

USSBriefs2021 This provides the R code and data to replicate results in "The USS Trustee’s risky strategy" by Neil M Davies, Jackie Grant and Chin Yan

1 Oct 30, 2021
Official implementation of "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

NVIDIA Research Projects 1.4k Dec 31, 2022
TransCD: Scene Change Detection via Transformer-based Architecture

TransCD: Scene Change Detection via Transformer-based Architecture

wangzhixue 29 Dec 11, 2022
Asymmetric metric learning for knowledge transfer

Asymmetric metric learning This is the official code that enables the reproduction of the results from our paper: Asymmetric metric learning for knowl

20 Dec 06, 2022
Neural Caption Generator with Attention

Neural Caption Generator with Attention Tensorflow implementation of "Show

Taeksoo Kim 510 Nov 30, 2022
LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

donglee 279 Dec 13, 2022
Data for "Driving the Herd: Search Engines as Content Influencers" paper

herding_data Data for "Driving the Herd: Search Engines as Content Influencers" paper Dataset description The collection contains 2250 documents, 30 i

0 Aug 17, 2021
[ICCV21] Self-Calibrating Neural Radiance Fields

Self-Calibrating Neural Radiance Fields, ICCV, 2021 Project Page | Paper | Video Author Information Yoonwoo Jeong [Google Scholar] Seokjun Ahn [Google

381 Dec 30, 2022
realsense d400 -> jpg + csv

Realsense-capture realsense d400 - jpg + csv Requirements RealSense sdk : Installation Python3 pyrealsense2 (RealSense SDK) Numpy OpenCV Tkinter Run

Ar-Ray 2 Mar 22, 2022
ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm

ManipulaTHOR: A Framework for Visual Object Manipulation Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha

AI2 65 Dec 30, 2022
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Diego Porres 185 Dec 24, 2022
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Introduction Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Datasets: WebFG-496

21 Sep 30, 2022
This repository collects 100 papers related to negative sampling methods.

Negative-Sampling-Paper This repository collects 100 papers related to negative sampling methods, covering multiple research fields such as Recommenda

RUCAIBox 119 Dec 29, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Authors implementation of LieTransformer: Equivariant Self-Attention for Lie Groups

LieTransformer This repository contains the implementation of the LieTransformer used for experiments in the paper LieTransformer: Equivariant self-at

35 Oct 18, 2022
Toontown House CT Edition

Toontown House: Classic Toontown House Classic source that should just work. âť“ W

Open Source Toontown Servers 5 Jan 09, 2022