After completing the evaluation, how to predict PTM lysine or any other modification with unlabeled data for making a web server? I have created a model with SVM classifier and evaluated it with 5-fold cross-validation. The dataset was sequence data containing lysine modified sites. Now I want to build a web server where anyone can input any sequence in FASTA format. Apparently, I have no label information at this point. So how can I will be able to extract features or use any feature selection method which requires label information? How can I extract or create the test set to predict? Suppose I have used f-score feature selection method in my model and selected 50 features from the given dataset. But for web server, having no label information, how can I select 50 features from the sequence that are provided by an user and then test it with my previously trained model?

As being a novice in the world of bioinformatics, I am seeking the answer which might be improper for others. But it will be very much helpful for me.
I am giving some link of some papers and its corresponding web servers.


iPTM-mLys: identifying multiple lysine PTM sites and their different type (https://academic.oup.com/bioinformatics/article/32/20/3116/2196525)

Web server link : http://www.jci-bioinfo.cn/iPTM-mLys
iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC 
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5190098/)

Web server link : http://www.jci-bioinfo.cn/iHyd-PseCp
Interesting application. I would suggest using Python's Flask system, which is described in a few pages in O'Reilly's Programming PyTorch, chapter 8. PyTorch in Production. Django is not really recommended (don't say that on Stackoverflow however). O'Reilly do an entire book on Flask, but thats to make the site pretty.

Obviously, the explanation here is Deep Learning rather than ML but the basic gist will hold. You should also think about building a docker for distribution which is easy (same chapter). We're in lockdown so I don't have this book with me. 

The difficult part will be web architecture because you shouldn't have the CPU/GPU on the same server as the quiry interface, otherwise you are prone to a denial of service attack or just sytem overload. To do this you need to consider  Representational state transfer (RESTful) and that is not easy at all (at least for me). Flask facilitates RESTful, but thats like systems admin stuff. I guess before getting into RESTful I'd see how popular your website is, but if its the same server as your institute website you'd need to be really careful because an external user could bring your institutional website down (you wouldn't be popular).