Requirements:

This project requires Python 3.6 and Pipenv to manage virtual environments and package dependencies.

Building a RESTful API with Flask for image recognition

In a previous project, I created a Convolutional Neural Network (CNN) that can identify brand logos (specifically Nike and Altra) in untagged/ unlabeled photos from a social media feed. The model I used implemented a previously trained network and transfer learning.

For this project I wanted to build a REST API that allowed me to send requests to that neural network and return a response in JSON.

The original incarnation of this project was developed as a solution for a GapJumpers Challenge. That version is still available in the old-master-p27 branch here

The challenge specified the following requirements for a successful submission:

• Write a simple Rest API with PHP, Node.js, Ruby, Python or Go.
• Your API must have:
  • at least three endpoints
  • all endpoints must be linked in some way
• Pay careful attention to REST best practices
• Describe the application in detail in a design document including why you chose the approach you did.
• include tests that validate success.

Deliverables

• Using Python and Flask, I created this website that allows for retrieval of data from my endpoints. The 3 endpoints are described in detail in the API Documentation section.
• In order to share my application and provide links where it can be tested - links and curl embeds are included throughout that document that allow for interacting with the API directly. The API is currently live at http://10la.pythonanywhere.com/img/api/v1.0/images and all references to a [hostname] in this documentation refers to http://10la.pythonanywhere.com. The application, it's unit tests, and all supporting documentation can be found at the project's GitHub.
• This website represents the design document. In particular the sections dedicated to the REST Architecture and API Documentation discuss the design process in detail.
• Unit tests, including those that validate success, are available in a number of ways. Basic functionality can be verified by executing each curl command in order it appears in the the API Documentation. A copy of the output of the unit test application is included in the Unit Tests section. Finally, the test application can be downloaded from GitHub and run locally.