Updating model on PyMC3 with new observed data

I have measured the diameter of 80 fruits last year, and after checking what is the best distribution of the values, I've created a PyMC3 model

with Model() as diam_model:
    mu = Normal('mu',mu=57,sd=5.42)
    sigma = Uniform('sigma',0,10)

after, as far as I understand, I've "trained" the model with my prior data (the 80 values)

with diam_model:
    dist = Normal('dist',mu=mu,sd=sigma, observed=prior_data.values)

with diam_model:
    samples=fit().sample(1000)

then I used the plot_posteriorof the samples, returning also the mean and HPD.

My idea is to measure again this year using Bayesian update to reduce the sample size. How can I add single values, and update the posterior, expecting that the HPD gets smaller and smaller?

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