The problem Zoobi solves

The catalogue app "Zoobi", which we have created provides a convenient and efficient way for users to access and explore products from various stores without physically going there. This saves users time and effort while allowing them to easily find the products they are interested in.

With the app, users can search for products based on their preferences and current fashion trends. They can also compare and explore products from multiple stores in a particular vicinity. This not only saves time but also provides a more comprehensive view of the products available in the market.

The QR functionality in our catalogue app provides a quick and easy way for users to access the store catalogue of their choice. Users are directed to a visually appealing and user-friendly catalogue that showcases all the available products. It eliminates the need for typing in web addresses or searching for the store's website manually.

The app also helps small shop owners to compete with big shopkeepers by providing them with an equal opportunity to showcase their products to a wider audience. This can lead to increased sales and growth for small businesses.

Furthermore, the app allows users to save their favorite products in a wishlist, which can be accessed at any time. The shop name and address are also displayed for easy access when the user decides to physically go and try the products.

Our catalogue app has been enhanced with an ML model that provides personalized product recommendations to users based on their previous purchases or liked products. This feature utilizes advanced algorithms that analyze the user's shopping behavior and preferences to generate recommendations that are tailored to their individual needs.

Overall, the catalogue app makes shopping easier, safer, and more convenient for users while also promoting small businesses.

Challenges we ran into

We faced problems with IPFS as data that was sent to the network by us was only accessible by a localized portion of the network. Whereas we used an IPFS provider which was out of India, which caused some of our static files to not be accessible. We had to overcome this by restricting ourselves to local peers itself.

We ran into another problem to find a gopod dataset to train our recommendation system. After a long web search we got the data to train it.