Real Time Memory for Dataset Transformation of Inputs and Outputs, Dynamic Examples in Gradio

I recently have been playing with an ASR example which saves the ASR stream to a dataset. I would like to employ a process used in multiagent systems which will check the updated dataset periodicially and perform further operations (including forgeting records by deletion, or processing NER and Seq2Seq results on the stored data to process it and create identified important terms to the user, AutoQA from Seq2Se2 by identifying terms across sessions, and then too to provide a recorded output of chatbot responses to input to utter back answers and insights for the user.

In order to pass a Turing test an ASR program (and maybe the larger AI Pipeline Multi-Agent system) the AI would not forget anything said to it which works inside sessions as saved context, and works outside sessions by persisting the dataset. Ultimately it would also need a speaker identification classifier and perhaps a password which would allow it to keep ASR results private to a given user.

With datasets (lets say just comma seperated lists, is it possible to auto generate the examples list to edit or revise the data possibly allowing users to score or classify the output? I would like to have the UI and example list generated somehow so it could be dynamic based on input types.

Here is the space I am using as input method via voice:

Here is the space I am using as input method for text and chatbot response:

They both write to CardData.csv located here:

The program I would want to use to upsert the CSV dataset is here:

When I resave the dataset any active client would need to reload since they are appending text and git committing the changes and additions.

If I bounce the space it clears the memory (yet keeps the persistent memory in the dataset).

Any help in the dynamic capability would be appreciated. The examples are all gradio based.