Our inspiration was drawn from a drive to give consumers the ability to know who and what they were supporting by buying a product.
What it does
It starts with a person using the mobile app, and takes a picture of a logo which is sent to Firebase. The image is then scanned by the Google Cloud Platform Cloud-Vision API, and the company is identified. Next, information and news is web-scraped, searching for keywords revealing environmental concerns, boycotts, scandals, and more. The information is then relayed back to the web app, which populates a results page for the user, allowing them to decide if they want to support these actions.
How we built it
We built it by first identifying our minimum viable product as a logo scanner, which brings back some results. After laying the framework about what we aimed to accomplish, we broke up the work and got down to business! Some initial key steps were establishing the HTML/CSS framework, and getting the image upload system together. At the same time, half the team worked on putting together the web-scraping function, and found a way to get it all together.
Challenges we ran into
Accomplishments that we're proud of
Overcoming (almost) all of the above! Actually though, we all worked to learn something new and we definitely got there.
What we learned
We learned lots about how to incorporate backend APIs such as firebase and GCP to more frontend ideas. Things like the image upload system and web-scraper was a challenge,
What's next for biased
Other improvements we thought would be interesting would be to incorporate score based icons and website grading, an auto-generated content summary, and a pros and cons list based on content as well!
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