Predicting unseen data on Target variable based Encoding Technique
What you have outlined above is the training pipeline. In a test (inference) scenario, the pipeline would be slightly modified.  Data upload and data cleaning should be performed identically as in the training scenario, but as you acknowledge there is no need (or even possibility) for performing target encoding since the target is what we are trying to predict using the model during testing. In this case encoding is not performed, and the model is used to predict the target based on the cleaned data.
In short, the model pipeline should be nearly identical for train/test, with the exceptions that target encoding is not performed in the test scenario, and the final step will be a fit in the train scenario and a predict in the testing scenario.
I am working on an automated ML (Regression) algorithm where the flow of process is:
User uploads a data -- Data Cleaning --  Encoding(Target Encoder) -- Fitting model -- results.
I am completely fine until this point, my confusion is when the user wants to test this in an unseen data without target variable, then I need to again perform Data cleaning -- Encoding and encoding technique I have used while fitting the model can work only if there is a target variable (unseen data will not have a target variable) and I cannot change the Encoding technique on unseen data as the testing data needs to go through the same procedure as the data used while fitting the model as per my knowledge.
Could someone please help me in finding a way to overcome this issue or any suggestions would be of great help.
Thanks in advace.