Recognizing the vast quantities of photos that are possible today as a result of the omnipresence of smartphones, we saw that the vast quantities of photos from years of photography are not well organized or easily searchable. This is where we come in. Using ClarifAI and MongoDB, we created a searchable database based on AI image recognition and tagging.

What it does

It takes photos and organizes them using the ClarifAI API's tagging functionality.

How we built it

We built the front-end using HTML, PHP, CSS, Bootstrap, and a bit of jQuery to create an interface for getting the pictures and handling the raw photo files. The back-end was programmed using python utilizing ClarifAI API class and MongoDB property-based database.

Challenges we ran into

Python, API calls, and MongoDB were all new technologies for most of us. We spend a large portion of the time learning and practicing with them before applying them. While doing the work, we went through continual debugging and google search.

Accomplishments that we're proud of

We were able to apply concepts in these various technologies and bring our various technologies together to into a working product regardless of issues we faced along the way. Some of us also strengthened their knowledge of various programming languages and discovered better ways to accomplish what they needed to, such as connecting the front and back ends using PHP.

What we learned

We split off into to larger groups, front-end and back-end. The front-end group was able to learn many design elements and create an effective interface for our work. The back-end group was able to gain valuable insight into making API calls, handling the outputs, and storing the important data in a database.

What's next for CollectifAI

Expanding on making it work

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