About us

Here at ÀI, we understand the struggles that many singles face. Some call it the dating game, others the talking phase, or even a situationship. Whatever the phrase used, the experience is an arduous and confusing one for many singles looking to find a partner. After some research and soul searching, we realized that the underlying problem is that:

It is difficult to figure out if your interest is reciprocated.

From deciphering if they are flirty or just friendly to calculating their response times, it is often a guessing game in trying to figure out if they like you back. This asymmetric information may sometimes lead to making the wrong decision such as confessing too early or not at all.

Fret no more! Here at ÀI, we believe that we have found the solution to all your love life problems. Using state of the art technology of sentiment analysis with machine learning, our solution can:

Convert your chat history into visualizable data that tells you how much they actually like you.

Technologies

AI/ML Models and Data Analysis

Our Natural Language Processing (NLP) ML model was adapted from a pre-trained Pytorch model used to gauge customer sentiment in product reviews. Using this model, we extracted positive and negative keywords from the chats and utilized it as one of the measurements to predict the interest of the other party. The score ranges from 1 to 5, where 1 indicates a conversation with negative energy, while 5 indicates a conversation with sparks flying ✨.

The Pytorch ML model is hosted on the cloud with AWS Lambda and AWS Elastic File System (EFS). When users upload their chat history, they receive their predictions using FastAPI which provides a Rest API to link the frontend to the model.

(Unfortunately, due to the size of the model, we were unable to host the Pytorch model onto the cloud. The code is still in the GitHub repository, though the frontend is currently rendering randomly generated data into the charts and dashboard)

However, we quickly became aware that sentiment analysis alone is insufficient due to the multifaceted nature of attraction. Therefore, we did even more data analysis through using Matplotlib to parse the chats and calculating the average response time for each person for each day. With this additional data, we were able to do a weighted average between all our data points to come up with an indication of interest 😍.

Front End Technologies and Data Visualization

We utilized Next.js, TypeScript and Tailwind CSS for the frontend. Next.js has static-site generation capabilities coupled with client-side rendering to ensure that the static portions of the website are easily indexed by web crawlers and load quickly while the analyzer renders with low response times. TypeScript helps us in our development to prevent bugs as the project scales while increasing efficiency with intellisense 💻.

The graphs are drawn with the Recharts library that utilizes D3 under the hood. With this, we are able to plot out multiline charts so that users can more quickly observe trends in their day-to-day conversations with the other party.

GitHub

Front End GitHub Repository

Pytorch ML Model and Data Analysis Repository

DevPost Submission

YouTube Demo

Contact Us

Amos Liew

Jess Ong

Zechary Au

Rayner Loh