Simple (and safe*) token authentication for Rails apps or API with Devise.

Devise strategy for token authentication in API-only Ruby on Rails applications

The devise-api gem is a convenient way to add authentication to your Ruby on Rails application using the devise gem. It provides support for access tokens and refresh tokens, which allow you to authenticate API requests and keep the user's session active for a longer period of time on the client side

rails 4, rails-api, devise, doorkeeper, oauth, cancancan, rspec, puma, vcr, factory_girl, reqres_rspec, pg, dotenv, rack-cors, active_model_serializers, rubocop

Rails-API authentication solution based on JWT and inspired by Devise

an app engine for your business. Seamlessly implement business logic with a powerful API. Out of the box CMS, blog, forum and email functionality. Developer friendly & easily extendable for your next SaaS/XaaS project. Built with Rails 6, Devise, Sidekiq & PostgreSQL

Simple PostgreSQL REST API in Crystal with devise-like auth.

Token based authentication for API using Devise

Abstracting Zoho’s API into a set of Ruby classes, with reflection of Zoho’s fields using a more familiar ActiveRecord lifecycle, but without ActiveRecord. Works with Rails and Devise.

Bare-bones template for Devise JWT-auth API-only Rails applications.

Rails Versioned API solution template for hipsters! (Ruby, Ruby on Rails, REST API, GraphQL, Docker, RSpec, Devise, Postgress DB)

:key: Example for Angular2-Token (a token based authentication) with Devise Token Auth and Rails 5.0 API.

Rails sample API using Devise, Doorkeeper and OAuth2

Example of implementing API key authentication in Ruby on Rails without Devise

Rails API Simple Token Authentication with Devise example (November 2014).

Devise integration gem for Grape API framework

No description available

Working example of a RESTful API using devise for mobile client authentication.

A sample to demonstrate Setup of authentication with JWT tokens using devise and devise-JWT libraries

Using-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.

Tokenable is a Rails gem that allows API-only applications a way to authenticate users. This can be helpful when building Single Page Applications, or Mobile Applications. Works with Devise, Sorcery, has_secure_password, and any other auth system you may want to use.

Adds support for devise helpers in grape APIs

Rails 5 app having API authentication using Devise and JWT

No description available

Rails API for this: https://github.com/dakotalmartinez/rails-devise-jwt-tutorial

CRA + Rails API + Devise Login + ActiveAdmin Boilerplate

A simple registration react-native with rails api by using devise and devise_auth_token gem.

Cheatsheet résumant toutes les étapes nécessaires au setup de l'authentification avec devise et devise-jwt sur une API Rails

Devise is a CRUD Api generator based on C# 9 Source Generators

A Rails 7 API-only setup with Devise (confirmable) & Devise JWT (AllowList revocation strategy + AUD claim)