I have a deployed ML model. I am using a Flask app to use the model and return predictions. It is a Post request which input payload in this form

{
“input_features”: [
feature_list1,
feature_list2,
feature_listn
]
}

It returns predictions in this format:

{
“predictions”: [
prediction1,
prediction2,
predictionsn
]
}

In power Bi, I want multiple slicers in which user can a add/subract percentage of values to the current feature values . I should be able to hit the forcasting api with the adjusted values and return the predictions and visualize them

For example – I have a visualizations of around 1000 rows with 10 features. I should have 10 slicers . I can select a percentage value from slicer which should be added to the current features values . Then the forecasting API should be called adjusted values and the predictions should be visualized.

I have tried Power Query and inbuily python code runner in power BI. Please provide a direction . Also please let me know if such scenario is even possible with Power BI