import argparse
import numpy as np
from train import train
from LinearRegression import LinearRegression
from matplotlib import animation


def get_thetas(path):
    try:
        thetas = np.genfromtxt(path, delimiter=',')
    except:
        print ('info: path not found, asking for train')
        return "error"
    try:
        return [thetas[0], thetas[1]]
    except:
        print ('warning: path in wrong format, asking for train')
        return "error"

def get_price(mileage, thetas):
    return (thetas[0] + mileage * thetas[1])

def predict_subject():
    thetas = get_thetas('thetas.csv')
    if thetas == "error":
        print("")
        try:
            input_thetas = input("model isn't trained, would you like to train it before predicting car price ? y/n\n");
            if (input_thetas == 'y'):
                raw_data = np.genfromtxt('data.csv', delimiter=',', skip_header=1)
                train(raw_data, np.zeros(2), 'thetas.csv', False);
                thetas = np.genfromtxt('thetas.csv', delimiter=',')
            else:
                print ('info: wrong input format, setting thetas to 0')
                thetas = [0, 0]
        except:
            print ('info: wrong input format or fail to train, setting thetas to 0')
            thetas = [0, 0]
    try:
        mileage = int(eval(input("Enter mileage\n")))
    except:
        print ('info: input a number')
        return
    if mileage < 0:
        print ('info: mileage should be superior to 0 ! aborting')
        return
    price = get_price(mileage, thetas)
    if price < 0:
        print('This car belongs in a museum ! (price inferior to 0)')
    else:
        print('Predicted car value is ', price,)

def get_y(x, thetas):
    h = 0
    for i in range(len(x)):
        h += x[i] * thetas[i]
    return h

def predict(thetas_path):
    try:
        thetas = np.genfromtxt(thetas_path,delimiter=',')
    except:
        print('wrong name or format')
        return
    x = np.empty(len(thetas) - 1)
    x[0] = 1;
    for i in range(1 ,len(thetas) - 1):
        try:
            s = "Enter feature " + str(i) + "\n"
            x[i] = int(eval(input(s)))
        except:
            print('input a number')
            return
    print("y is equal to ", get_y(x, thetas))

def main():
    parser = argparse.ArgumentParser(description='DSLR is a 2 day project, if you do Linear Regression in two weeks :pepethefrog:')
    parser.add_argument("-p", "--path", type=str, default=False, help="thetas file path")
    args = parser.parse_args()
    if (args.path == False):
        predict_subject()
    else:
        predict(args.path)

if __name__ == '__main__':
    main()