DripAI

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Inspiration

As Berkeley students, it is often difficult to gauge the weather and decide what clothes to wear. Students end up wearing clothes that suit either the morning chill or the afternoon heat, and it can be frustrating and uncomfortable while they perform their daily activities. An app/website that helps people to select the right clothes for the varying weather conditions would be extremely helpful.

What it does

DripAI solves this problem by making accurate recommendations on what clothes to bring/wear based on daily weather conditions and a person’s style.

The user first answers questions about where they live, what time they will be outside for, what activities they will be doing out, and what clothes they prefer to wear. The user’s location and time they will be out are fed into a weather API, and an LLM makes recommendations and an image that displays what clothes would be ideal in the given weather conditions.

How we built it

We used a Weather API to generate a result of the weather at a given location and time range. This information is fed into the Llama LLM from TogetherAI, which generates a recommendation. Stable diffusion is then used to generate an image based on the recommendation. This is outputted to the user via the website we created using Reflex.

Challenges we ran into

Integrating the front end and the back end together using State
Ensuring that the LLM outputted accurate recommendations based on the user’s data.
Handling the delay in generating answers from the LLM.

Accomplishments that we're proud of

Able to successfully generate an image to illustrate the response given by the AI
Developed an easy-to-use UI to get users their outfit plans ASAP
Combined the response from the Llama LLM and the UI into a product that users can easily interact with.

What we learned

We learned how to extract information from an API. Using the info from the API, we learned how exactly to feed it into the LLM. Using state-of-the-art models to power our product and output recommendations

What's next

A user login system that customizes their wardrobes online & saves input data so that the user can run the app with the same parameters for similar days
Allow users to upload pictures of themselves to see how the suggested outfits would look on them
A VR try-on to allow users to see their recommended fit in real-time
A way to share and comment on fits in a social media sense, from which we can feed this data back into the recommendation system