Stateful LSTM and stream predictions

I've trained an LSTM model (built with Keras and TF) on multiple batches of 7 samples with 3 features each, with a shape the like below sample (numbers below are just placeholders for the purpose of explanation), each batch is labeled 0 or 1:

Data:

[
   [[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]]
   [[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]]
   [[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]]
   ...
]

i.e: batches of m sequences, each of length 7, whose elements are 3-dimensional vectors (so batch has shape (m*7*3))

Target:

[
   [1]
   [0]
   [1]
   ...
]

On my production environment data is a stream of samples with 3 features ([1,2,3],[1,2,3]...). I would like to stream each sample as it arrives to my model and get the intermediate probability without waiting for the entire batch (7) - see the animation below.

One of my thoughts was padding the batch with 0 for the missing samples, [[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0],[1,2,3]] but that seems to be inefficient.

Will appreciate any help that will point me in the right direction of both saving the LSTM intermediate state in a persistent way, while waiting for the next sample and predicting on a model trained on a specific batch size with partial data.

from Stateful LSTM and stream predictions