The problem with your implementation seems to be here:
for t in tqdm(x["proba"].unique()):
You seem to get through each unique values of probabilities, but these are in range 0-1 (probably) and are most likely barely unique, which leads to very long run. You need to translate probability into the label. If you are using binary labels (which from your attempt seems so), you can do following list comprehension:
df["prediction"] = [0 if x<0.5 else 1 for x in df["proba"]]
This way you translate the probability to label and then can sort according to prediction and use unique values in predictions. If you use multilabel predictions, you can extend the above condition according to your needs.