import sys
from py.arguments import Arguments
from py.modell import *

if not sys.argv[1:]:
    print("Usage: python3 main.py <path to csv>")
    sys.exit(1)

def main():

    repeat = True
    args = Arguments(sys.argv[1], "v", False, False)
    args.set_mode("v")
    args.set_information(False)
    args.set_graph(False)

    while repeat:
        print("Currently selected setting:")
        print(f"File: {args.get_file_path()}")
        print(f"Mode: {args.get_mode()}")
        print(f"Display information: {args.get_information()}")
        print(f"Display graphs: {args.get_graph()}")

        prompt = input("Change settings [y / exit / blank]: ")

        if prompt == "y":
            try:
                args.set_file_path(input("Change file <path to file>: "))
                args.set_mode(input("Change mode [v, a, c]: "))
                args.set_information(eval(input("Display information [True / False]: ")))
                args.set_graph(eval(input("Display graphs [True / False]: ")))
            except ValueError as error:
                print(f"Value {error}")
        elif prompt == "exit":
            quit()

        # use verctor length of (x,y) as feature
        if args.get_mode() == "v":
            load_dataframe = make_dataframe(lambda df: df)
            features = make_features(radius)
            score = make_score_function(lambda x, y: [np.sqrt(x**2 + y**2)])
        # use absoult values of (x,y) as feature
        elif args.get_mode() == "a":
            load_dataframe = make_dataframe(lambda df: df.abs())
            features = make_features(xy)
            score = make_score_function(lambda x, y: [x, y])
        # use unaltered values of (x,y) as feature
        elif args.get_mode() == "c":
            load_dataframe = make_dataframe(lambda df: df)
            features = make_features(xy)
            score = make_score_function(lambda x, y: [x, y])
        # default use vector length
        else:
            load_dataframe = make_dataframe(lambda df: df)
            features = make_features(radius)
            score = make_score_function(lambda x, y: [np.sqrt(x**2 + y**2)])

        print("\n")

        apply_model(load_dataframe(args.get_file_path()), features, score, args.get_information(), args.get_graph())
        
if __name__ == "__main__":
    main()