from time import time 

import warnings

import joblib

import sys

import os

warnings.filterwarnings('ignore')

# Load trained model

try:

    model = joblib.load(open(os.path.join(os.path.dirname(__file__), '../../models/gradient_boosting.joblib'), 'rb'))

except FileNotFoundError as err:

    print(f'Ann error occoured: {err}')

# Predict new data

def predict(data):

    return model.predict([data])[0]

# Get the prediction outcome

def proba(data):

    return f'{(model.predict_proba([data])[0][predict(data)] * 100):.0f}%'

# Result

def main(data):

    timer = time()

    prediction = predict(data)

    if prediction == 1:

       return f'Prediction: {proba(data)} - Affected | Prediction speed: {(time() - timer):.3f}'

    else:

       return f'Prediction: {proba(data)} - Benign | Prediction speed: {(time() - timer):.3f}'

if __name__ == '__main__':

   print(main([1,24,1,1,2,1,1,2,1,1,1])) # My health status