An interactive accessibility map
People with physical disabilities or who may use wheelchairs face many challenges in navigating public spaces. Standard maps, such as Google Maps and Apple Maps, lack details on accessible areas and services for disabled persons, such as ramps, wheelchair-friendly washrooms, and elevators. MapAbled is a map built for people with disabilities to help them find accessible spaces and supports in their everyday lives.
Alyssa wrote the models, used the created maps function to implement them into the web app, mainly worked on the accessibility section, debugged the code, and wrote the rough draft for the description. Sienna - designed the website frontend, designed the website logo, helped with pitch and project submission Ethan - used Google Maps API and Python modules to create the interactive map. Bianca - Created the camera app (including backend and frontend), which reads photos and converts them to text and speech; worked on the front end, worked on the search bar function, debugged the code a lot,
BUIDL Description (describe this project. Insert a link or image by selecting the link icon and image icon, respectively)
MapAbled is a web app of a map that displays the locations of disability-friendly facilities, such as ramps, elevators, special washrooms, parking spots, etc. The web app is programmed using the Django framework and uses multiple other APIs to support the functions of this website. The base map is generated using Google Maps API. Users can input new locations with disabled-friendly facilities by submitting a form, and the map will be updated with a new pin. When a user clicks on a pin already on a map, a sidebar will pop up and display the location details, where other users can read, comment, like and dislike. There’s also a search bar function to search up different locations and view related information such as available facilities, descriptions, location names, likes and dislikes. Additionally, a camera function can check pictures taken of locations to verify whether they match the inputted description. It uses easyocr for image recognition, cohere generates text that explains the photo in words, and gTTs to convert the text to speech.