import os
from trainingData import TrainingData
from sklearn import tree
import joblib
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np

def _read_training_data() -> TrainingData:
    attributes = []
    classes = []
    location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
    file = open(os.path.join(location, 'training_data.csv'))
    lines = file.readlines()[1:]
    file.close()
    for line in lines:
        actual_row = line.replace('\n', '')
        values = actual_row.split(',')
        line_attributes = values[:-1]
        line_class = values[-1]
        attributes.append(line_attributes)
        classes.append(line_class.strip())
    return TrainingData(attributes, classes)

def _attributes_to_floats(attributes: list[str]) -> list[float]:
    output: list[float] = []
    if attributes[0] == 'Longitiudonal':
        output.append(0)
    elif attributes[0] == 'Round':
        output.append(1)
    elif attributes[0] == 'Flat':
        output.append(2)
    elif attributes[0] == 'Irregular':
        output.append(3)
    
    if attributes[1] == 'Low':
        output.append(0)
    elif attributes[1] == 'Medium':
        output.append(1)
    elif attributes[1] == 'High':
        output.append(2)
    

    if attributes[2] == "Yes":
        output.append(0)
    else:
        output.append(1)
    
    if attributes[3] == 'Low':
        output.append(0)
    elif attributes[3] == 'Medium':
        output.append(1)
    elif attributes[3] == 'High':
        output.append(2)

    if attributes[4] == 'Low':
        output.append(0)
    elif attributes[4] == 'Medium':
        output.append(1)
    elif attributes[4] == 'High':
        output.append(2)

    if attributes[5] == 'Transparent':
        output.append(0)
    elif attributes[5] == 'Light':
        output.append(1)
    elif attributes[5] == 'Dark':
        output.append(2)
    elif attributes[5] == "Colorful":
        output.append(3)

    if attributes[6] == 'Low':
        output.append(0)
    elif attributes[6] == 'Medium':
        output.append(1)
    elif attributes[6] == 'High':
        output.append(2)

    if attributes[7] == "Yes":
        output.append(0)
    else:
        output.append(1)
    return output



trainning_data = _read_training_data()

X = trainning_data.attributes
Y = trainning_data.classes


model = tree.DecisionTreeClassifier()
encoded = [_attributes_to_floats(x) for x in X]
dtc = model.fit(encoded, Y)
joblib.dump(model, 'model.pkl')