Eatelligent

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  Login In Page

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  HomePage

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  Add Ingredient Page

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  Fridge Inventory Page

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  LLM Generated Recipes

Inspiration

According to NRDC and Harvard Law’s study, around 91% of individuals throw perfectly good food away due to labels like “sell by” and “best by.” In doing so, these individuals contribute to the huge issue of food waste. More importantly, the perfectly good food that gets wasted ends up rotting in landfills, emitting potent greenhouse gases and contributing to the impending climate crisis.

What it does

Our solution is Eatelligent! Eatelligent is an online platform for users to manage their food waste by allowing users to add data about their groceries and they can find recipes that specifically prioritize ingredients which are going to expire soon.

How we built it

Frontend: Typescript, Javascript, CSS

Database: MongoDB Atlas to store data about user preferences, ingredients, and recipes

Tech Stack: MERN

Authentication: Firebase Authentication

LLM: Python, HuggingFace API, Llama-3.1-8B-Instruct to generate user-specific recipes

Challenges we ran into

Initially, we had planned for our project to be a mobile application. However, due to issues of not being able to convert our MERN stack web application into a mobile application and time constraints, we had to resort to making a web application rather than a mobile application.

Another challenge we faced was trying to get the LLM portion of the project to connect to the rest of web application. Despite a lot of trial and error, we were not able to fix the issue. However, something we would like to do in the future is create a seamless connection between the LLM and the web application to show the full impact of our idea.

Accomplishments that we're proud of

We are proud of ourselves for participating and pushing out a product that has features that work well and convey our vision despite facing a lot of problems in implementation.

What we learned

As it was most of our members' first time implementing the MERN stack, we are happy that we gained valuable experience learning how this specific tech stack works and can be implemented. Additionally, we learned how to manage and connect the stack with authentication by implementing Firebase authentication to handle secure user sign-up, login, and session management.

What's next for Eatelligent

Some things we hope to implement in the future for Eatelligent is:

• Fixing the LLM implementation and integration with our current web application
• Converting the application into mobile application -Add a system that allows the user to take images of their groceries to extract information rather than manually adding information for their groceries
• Implementation push notifications
• Deploy with Vercel

Built With

• css
• firebase
• huggingface
• javascript
• llama-3.1
• mern
• mongodb
• python
• typescript