You can interpret it however you want. If you leave in the interaction, you interpret the interaction. If you take it out, you don't interpret the interaction.
I'm assuming by a "high-low graph" you mean you fit a separate model for "high extraversion" and "low extraversion" people and plot both over the data. If that's what you mean, and you're wondering why you find a graphical interaction, it's because you have essentially assumed that an interaction coefficient exists. Then OLS just tried to estimate one for you.
"Nonsignificant" doesn't always mean "close to zero." It means "indistinguishable from random noise about zero." Therefore you can have a very large coefficient that is also nonsignificant, if your data is very noisy (or you have very few data points). "Nonsignificant" also does not mean "definitely due to noise." It just means you can't rule out the possibility that it's due to noise. So maybe the interaction does exist in principle but for whatever reason, maybe a small sample, it's not coming through.
In your case, because you're trying to make a prediction, I would just go with whichever model fits your data best. If the best model is a univariate linear regression, so be it. You interpret the results you have, not the results you think you should have.
Also remember that a linear regression is rarely "wrong" in the sense that the error term will always account for your misspecification. This is a problem if you want to find the "true" parameter for some theoretical model, but it doesn't seem like that's what you want. You're estimating a conditional mean around which your data varies.
Apologies if I misinterpreted what you're asking.