Do you know how healthy your diet was last month? Do you have time to enter every meal that you eat? This was the question that triggered this idea. We were not sure how well/healthy we ate the last month. All the existing apps needed us to enter the meals we have had every time of the day, but I do not even remember what I had yesterday. In that case, what can I do?? Can I fill this gap by analyzing the online ordering transactions to observe the consumption behavior?? We here, tried to provide a solution for this and make sure you shop and eventually eat healthy. There are many applications which suggest food items based on users previous diet, but most of the apps expect users to manually enter the data by themselves frequently. To suggest the user, their diet, we wanted to remove the burden from user end, where he should recollect what all he had last week and enter it in the app to get suggestions. The idea here is to record users previous orders (as of now considered groceries but can extend the same to restaurants as well), we analyse the past orders of the users in terms of calories, fat%, carbs % etc., and suggest the user what to order for the next time so that he can maintain a healthy lifestyle throughout a period of time. We also, suggest user some healthy products which are usually not in his previous diets or orders but are ordered by some similar users so that he can try out those healthy products too.

What it does

To stay healthy, we need to shop healthy. In order to maintain a healthy lifestyle, smart choices in the grocery store and kitchen are vital in achieving a balanced and nutritious diet. It is never too late to adopt healthier habits and making simple changes to your shopping and cooking routines will help you achieve your goal.

We the SeaWolves are here to make that happen. There are numerous apps that focus on calorie intake or ingredients of a product and how harmless it is for the customer to consume. All the apps require user to input their meal every step of the day whenever they have a meal to measure their health index. Though it covers an aspect, it totally misses the aspect of foods that are eaten but are not logged maybe due to laziness or memorability. We propose a solution, that will make customers purchase the healthier food in the first place rather than look at health as a consequence of food intake.

Through this app, we look forward to providing a seamless user experience for people to order grocery or food from their local restaurants. The app then using data at its disposal, crunches the numbers for the user and provides a metric on a how well a consumer has eaten over the months/years.

The app uses details from the users order history and makes recommendations of food/grocery items. Say for example, if the previous order had a fried chips in the cart, then our app would remove it from the cart and move it to section that lists the chips along with other alternatives like baked chips option so that, in case user still wants to order chips, he can choose among the healthier option. Here we plan to use the Google Recommendations API to help us with the recommendations based on the rules in data store.

Our business model will be similar to the aggregator business model, where we would partner with stores to provide the cart items to user. Apart from the existing store apps like Aldi’s, more customers who prefer a healthy lifestyle would continue to order products from these vendors giving them additional business rather than buying from Heath food stores like or Thrive market.

How I built it

We used front end technology like HTML5/CSS3/JavaScript and Python Flask for the backend. We have also used Google Cloud Platform(For Deployment of App), Google FireStore(Database) for pilot. We have tried to build with Google Recommender AI(Recommendation System) but its still in progress.

Challenges I ran into

Deployment of the application took a bit of time as the platform was new to us, we had spent good amount of time to fix the deployment errors.

Also integrating and exploring with third party APIs like Google Firebase, Google Recommendation AI. Exploratory Analysis about how we are going to suggest user based on his previous orders, also recommending products to the user which are ordered by the similar user, going through existing recommendation systems and inculcating those ideas into the current problem statement took time.

Accomplishments that I'm proud of

We were also able to learn new technologies quickly and were able to get the full stack up and running in quick time, considering the fact that we took almost a day to ideate and choose a topic.

What I learned

The workshops along with Hackathon were helpful. Design workshop helped us ideate.

Google Cloud APIs are totally new to us and we are very happy to go through them quickly in short span of time and use them efficiently to deploy the applicaiton using Google Cloud Platform.

Also we relied on Google Firestore to store data, as it is one of the best databases when it comes to low latency for large scale data , also it is very easy to integrate from server side programming to perform CRUD operations.

We have gone through documentations of Google Recommmender AI for recommmending users with products which are bought by similar users and are very healthy.

What's next for Shop Healthy

We are able to see a potential business opportunity here and looking at the success of health and fitness apps in the recent days, we are planning to continue working on this and make a completely working model with all that we did and planned to do. More details on the screenshot page.

This is just a prototype model with stubs and we are looking forward to enhance it.

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