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

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": "πŸ‡ΊπŸ‡¦",
  "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.

β€’ MADE BY > Roldex

License

MIT

Owner
Roldex Stark
BEYOND YOUR LIMITS
Roldex Stark
PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Siyu Huang 33 Dec 06, 2022
Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Hold me tight! Influence of discriminative features on deep network boundaries This is the source code to reproduce the experiments of the NeurIPS 202

EPFL LTS4 19 Dec 10, 2021
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering Paper: https://arxiv.org/abs/2103.00762 Running Run on the provided DTU scene cd run ba

Fanbo Xiang 67 Dec 28, 2022
Safe Local Motion Planning with Self-Supervised Freespace Forecasting, CVPR 2021

Safe Local Motion Planning with Self-Supervised Freespace Forecasting By Peiyun Hu, Aaron Huang, John Dolan, David Held, and Deva Ramanan Citing us Yo

Peiyun Hu 90 Dec 01, 2022
CUda Matrix Multiply library.

cumm CUda Matrix Multiply library. cumm is developed during learning of CUTLASS, which use too much c++ template and make code unmaintainable. So I de

49 Dec 27, 2022
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

SGCN β € A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

Benedek Rozemberczki 251 Nov 30, 2022
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Introduction This is an implementation of the model used for breast

757 Dec 30, 2022
An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit

Pengfei Liu 388 Dec 18, 2022
Catalyst.Detection

Accelerated DL R&D PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentatio

Catalyst-Team 12 Oct 25, 2021
Pytorch cuda extension of grid_sample1d

Grid Sample 1d pytorch cuda extension of grid sample 1d. Since pytorch only supports grid sample 2d/3d, I extend the 1d version for efficiency. The fo

lyricpoem 24 Dec 03, 2022
TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons

TensorFlow-LiveLessons Note that the second edition of this video series is now available here. The second edition contains all of the content from th

Deep Learning Study Group 830 Jan 03, 2023
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
5 Jan 05, 2023
Posterior predictive distributions quantify uncertainties ignored by point estimates.

Posterior predictive distributions quantify uncertainties ignored by point estimates.

DeepMind 177 Dec 06, 2022
LibMTL: A PyTorch Library for Multi-Task Learning

LibMTL LibMTL is an open-source library built on PyTorch for Multi-Task Learning (MTL). See the latest documentation for detailed introductions and AP

765 Jan 06, 2023
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
[CVPR 2021] Unsupervised 3D Shape Completion through GAN Inversion

ShapeInversion Paper Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, Chen Change Loy "Unsupervised 3D

100 Dec 22, 2022
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
Fuzzing JavaScript Engines with Aspect-preserving Mutation

DIE Repository for "Fuzzing JavaScript Engines with Aspect-preserving Mutation" (in S&P'20). You can check the paper for technical details. Environmen

gts3.org (<a href=[email protected])"> 190 Dec 11, 2022
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

HFGI: High-Fidelity GAN Inversion for Image Attribute Editing High-Fidelity GAN Inversion for Image Attribute Editing Update: We released the inferenc

Tengfei Wang 371 Dec 30, 2022