## Inspiration

What do you think is the most effective incentive to make people behave more sustainably? If you think it’s money, me too! But apparently we’re wrong. Multiple peer-reviewed research has shown that social framings are more effective than financial. For instance a paper by Asensio & Delmas from the Journal of Economic Behavior & Organization showed that wordings that frames energy savings with information disclosures about the environmental and health implications have a longer impact than ones that disclose the financial savings. Multiple other papers also found the same phenomenon. ([link](https://www.sciencedirect.com/science/article/abs/pii/S0167268116300257)) ([link](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260932))

Additionally a lot of people want to make a change to make their lives more sustainable but struggle to start or even view it as a huge sacrifice. What I realized is different emission reduction methods might be easier on some then others. For instance, one person might find it easy to carry a reusable water bottle but resist lowering the thermostat. If behavioral messaging focuses on a personalized approach—such as encouraging them to bring a water bottle daily while introducing subtle changes to their heating habits—it frames sustainability as a gradual and manageable process rather than an overwhelming sacrifice. Over time, the habit of carrying a reusable bottle can evolve into larger efforts, like investing in energy-efficient appliances or using smart thermostats to regulate heat​. ([link](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260932)) ([link](https://www.mdpi.com/2071-1050/15/21/15429))

This approach leverages psychological principles of incrementalism and ease, where simpler, less demanding steps lead to greater sustainable behaviors.

## What it does

This website helps recommend the easiest "first steps" according to each user's preference and use the research recommended framing to highlight the impact. Such as highlighting the health impacts that they saved.

Users will communicate with a Gemini-powered chatbot that is trained to ask questions about your preference and your habits to recommend a way to reduce emissions and pollutions that is the most comfortable for the users. It'll be doing so in five questions or less and focusing on following up and asking about scenarios.

Then we will forward the user to a page where they can view more information about the method. For instance, if it's recycling, they can scan an item they want to recycle, it'll then be analyzed by Hyperbolic's AI Inference to identify the item, then the inference will also return tutorial/ideas!

Finally, they will be shown to the thank you page where it'll highlight the health and environmental impact that you might've saved by doing these small steps.

## How we built it

**Hyperbolic AI Inference:**

* We used the LLAMA vision hyperbolic model to analyze the items that the user wants to recycle
* Then we used hyperbolic text generation to return potential recycling ideas
* Finally we used the AI inference to also calculate the estimated emissions or pollutions reduced by the chosen methods and use social framing to make it more effective

**Gemini API / Google AI Studio**

* We used Gemini as a way to analyze the users preference through tuning the model to specifically ask case-based questions which then leads to the method-recommendations

**General**

* We used Flask, Python, and Javascript for most of the project.

## What's next for ReduceNow

This is a very simple version, but I think the principle of it can be scaled widely to help make incremental changes for people to be more sustainable. For instance, in companies, it can be used to help find the easiest and most cost-effective ways for processes or employees to reduce their emissions. Or it can be implemented in educational institutions to help build the sustainable habit.

## Challenges we ran into

Initially we wanted to add a voice component to as an option, but ran into an issue with pyaudio and audio playback, therefore we had to scrap it. Additionally it's my first solo project on a hackathon so finishing everything was a huge personal accomplishment haha.
## Inspiration

Every day, human activities continue to have a negative impact on the world around us. Although most of this negative impact is not caused by the everyday citizen, there are actions each of us can take in our daily lives to better our planet and drive society towards environmental consciousness.

Through our combined effort, we can save our planet! There’s no better time than today to start building environmentally-friendly habits. We were inspired by apps that emphasize personal progress and community-building, like Strava and Duolingo, to track

## What it does

Eco Log is a social media app that lets you track and post about your daily efforts to become more environmentally conscious. We specifically decided to let users log recycling, composting, eating less meat, taking alternative forms of transportation, and picking up trash because we thought that these behaviors were both very doable and very impactful.

Eco Log incentivizes continued engagement by letting users see the impact their actions are having on the real world. Through the dashboard, which shows your progress over time (based on how many actions you log and how many points those actions are worth), users have a clear and comprehensive log of their actions over time.

Our app emphasizes also the community based nature of change by enabling users who live near each other see the efforts their neighbors are taking to become more environmentally conscious. Users can participate in community-organized events publicized on our platform, and can also choose to post certain actions they’re proud of for others in their city to see, like going a week without meat! For those who aren’t as enthusiastic about sharing, posts can be marked ‘private’ so that progress can be kept personal. Further adding to the community aspect, users have the ability to see leaderboards featuring the users with the highest points both locally and nationally.

Our hope is that by using this app, people find it easier and more fun to make environmentally conscious decisions!

## How we built it

React.js on the Frontend
Firestore for our database
Node.js with express for Backend

## Challenges we ran into

One of our collaborators dropped out 18 hours in due to an unavoidable conflict. Because he had the most experience with databases/backend and our other member with backend experience was based in India, our US-based members spent a while figuring out how to integrate the frontend with our partially-built backend.

We were a bit too ambitious with our original goals. We scrambled a bit towards the end to put together our submission.

## Accomplishments that we're proud of

We made it to the end, have a final product working, and learned a lot along the way!!

## What we learned

* Divide and CONQUER!!
	+ Planning and communication are the keys of a good project! Making sure that everyone was on the same page at the very beginning helped us communicate about what we wanted later on (even when we were very sleep deprived).
	+ How to write server requests
	+ How to write/read information to/from a database
	+ react-bootstrap SAVES LIVES

## What's next for Eco Log

We’re hoping to reinforce the community aspect of the app. We want users to be able to follow their friends and people they’re interested in to better reinforce the community aspect of the app. Additionally, users will eventually be able to create “clubs” with coworkers, classmates, or just friends to compete more directly for points and for further incentivization with club leaderboards.

We also want to let users use their points for entries in rewards/raffles at events and have schedule page to let users see their upcoming events.
## Inspiration

Using “nutrition” as a keyword in the Otsuka valuenex visualization tool, we found that there is a cluster of companies creating personalized nutrition and fitness plans. But when we used the “and” function to try to find intersections with mental wellness, we found that none of those companies that offer a personalized plan really focused on either stress or mental health. This inspired us to create a service where we use a data-driven, personalized, nutritional approach to reduce stress.

## What it does

We offer healthy recipe recommendations that are likely to reduce stress. We do this by looking at how the nutrients in the users’ past diets contributed to changes in stress. We combine this with their personal information to generate a model of which nutrients are likely to improve their mental wellness and recommend recipes that contain those nutrients. Most people do want to eat healthier, but they don’t because it is inconvenient and perceived to be less delicious. Our service makes it easy and convenient to eat delicious healthy food by making the recipe selection and ingredient collection processes simpler.

To personally tailor each user's recipe suggestions to their stress history, we have users save food items to their food log and fill out a daily questionnaire with common stress indicators. We then use the USDA food database to view the specific nutrients each food item has and use this data to calculate the relationship between the user's stress and the specific nutrients they consumed. Finally, we use this model to suggest future recipes that contain nutrients likely to reduce stress for that user.

In order to proactively reduce user's stress, we offer a simple interface to plan meals in advance for your entire week, an auto-generated ingredients list based on selected recipes for the week, and a seamless way to order and pay for the ingredients using the Checkbook API service. To make it easier for users, the food log will automatically populate each day with the ingredients from the recipes the user selected to eat for that day.

## How we built it

We used Bubble to build a no-code front end by dragging and dropping the design elements we needed. We used Bubble's USDA plugin to let users search for and enter foods that they consumed. The USDA food database is also used to determine the nutrients present in each of the foods the user reports consuming. We also used Bubble's Toolbox plugin to write Javascript code that allows us to run the Checkbook API. We use the Spoonacular API to get the recipe suggestions based on the user's personalized preference quiz answers where they give the types of cuisines they like and their dietary restrictions. We use a linear regression model to suggest future recipes for users based on the nutrients that are likely to reduce their stress.

## Challenges we ran into

One challenge for us was using Bubble. Only one team member had previous design experience, but everyone on the team wanted to contribute to building the front-end, so we faced a huge learning curve for all members of the team to learn about good design principles and how to space elements out on Bubble. Grouping elements together was sometimes weird on Bubble and caused issues with parts of our header not lining up the way we wanted it to. Elements would sometimes disappear under other elements and it would become hard to get them out. Formatting was weird because our laptops had different aspect ratios, so what looked good on one person's laptop looked bad on another person's laptop. We were debugging Bubble until the end of the hackathon.

## Accomplishments that we're proud of

Our team had little to no front-end design experience coming into this hackathon. We are proud of how much we all learned about front-end design through Bubble. We feel that moving forward, we can create better front-end designs for our future projects.

We are also proud of finding and figuring out how to use the Spoonacular API to get recipe recommendations. This API had very thorough documentation, so it was relatively easy to pick it up and figure out how to implement it.

## What we learned

The first thing we learned was how to use the Otsuka valuenex visualization tool for ideation. We found this tool to be extremely helpful with our brainstorming process and when coming up with our idea.

We learned how to use Bubble to create a front-end without needing to write code. Despite the struggles we initially faced, we grew to find Bubble more intuitive and fun to use with more practice and managed to create a pretty nice front-end for our service. We hosted our server on Bubble.

We also learned how to use the Checkbook API to seamlessly send and receive payments.

Finally, we learned how to use the Spoonacular API to generate recipe recommendations based on a set of parameters that includes preferred types of cuisines, dietary restrictions, and nutrients present in each dish.

## What's next for mood food

We feel very passionate about exploring this project in more depth by applying more complex machine learning models to determine better recipe recommendations for users. We would also like to have the opportunity to deploy this service to collect real data to analyze.

## Thanks

Thank you very much to Koji Yamada from Otsuka for meeting up with our team to discuss our idea and offer us feedback. We are very grateful to you for offering our team support in this form.

Thank you to the amazing TreeHacks organizers for putting together this awesome event. Everyone on our team learned a lot and had fun.