Jam.AI is your personal health assistant for managing diabetes. Using artificial intelligence, Jam.AI provides tailored guidance and support, helping you monitor blood sugar levels, offering nutritional advice, and sending medication reminders. With Jam.AI, managing your diabetes has never been easier.


Jam.AI is based on the GPT 3.5 model. We tuned our own model by feeding it around 600 lines of Q&A data in JSONL format about different scenarios for diabetic patients. Each line contains a person's age, height, weight, glucose level, and the choice of food in question. Our front-end utilized bootstrap icons and our fantastic logo. For back-end, we used flask to make our app. JavaScript to POST input from the user to our model, GET to update the HTML, and POST the output back to the user.


Initially, we chose text-bison@001 as our base model. Yet, the tuning failed more than 10 times. We tried changing the JSONL format many times, reducing the dataset, yet nothing worked. So, we changed to Gemini. While training Gemini, we noticed it was taking more than 40 minutes to train our data. We also lost a lot of time debugging some security issues. We ended up using GPT 3.5 because it had less training time and more readable documentation. During this process, one of our teammates finished the frontend but was stuck with implementing a secure API process using JavaScript. We realized JavaScript CAN'T keep our API key secure. We opted to use Flask in Python to make an app and import the model. We spent the next 10 hours with no sleep debugging our app. The app received the API call, but the model didn't receive any content from the user's input. At 4 AM, our chatbot was finally running, but we have another problem: How do we deploy it? We used Heroku to deploy our app right now.


Joseph Chamdani

Back End Developer

Back End Developer

Abraham Guan

Michael Han

Front End Developer