**Problem**

I am a total beginner with pytorch and machine learning in general.

I trained a time series classification model for predicting if a mountainbiker is in the air (jumping) or not, based on X-,Y- and Z - acceleration data of the biker.

I used a tutorial from Venelin Valkov as a template, where he predicted the surfaces robots where standing on, based on acceleration data from the robots.

As i followed his instructions with my own dataset, i am pretty confident, that my model makes sense and looking at the results and a confusion matrix it performs pretty well.

Now i want to evaluate the model by predicting the airtime of whole runs of a biker. Every run was filmed, so i could compare the predicted airtime with the ground truth from the video.

So i would like to upload a csv file containing the data of one run and try my model on it, to get predictions of every sample if its āAirtimeā or āNo Airtimeā.

I have converted the csv to a tensor and i know that I have to use model.eval and torch.no_grad for inferences, but up to now I did not succeed.

So my question is, if it is possible to run my model on a single csv file (not a whole dataset as in training) and make predictions and what is the easiest way to do so?