#!/usr/bin/python

import torch
from torch import nn
import pandas as pd
from sklearn import preprocessing
import numpy as np
from torch.autograd import Variable
from sklearn.metrics import accuracy_score, f1_score
from csv import DictWriter
import torch.nn.functional as F
import sys
import os
import matplotlib.pyplot as plt


class Model(nn.Module):
    def __init__(self, input_dim):
        super(Model, self).__init__()
        self.layer1 = nn.Linear(input_dim, 100)
        self.layer2 = nn.Linear(100, 60)
        self.layer3 = nn.Linear(60, 5)

    def forward(self, x):
        x = F.relu(self.layer1(x))
        x = F.relu(self.layer2(x))
        x = F.softmax(self.layer3(x))  # To check with the loss function
        return x


def prepare_labels_features(dataset):
    """ Label make column"""
    dataset = dataset.dropna()
    le = preprocessing.LabelEncoder()
    mark_column = np.array(dataset[:]['0'])
    le.fit(mark_column)

    print(list(le.classes_))
    lab = le.transform(mark_column)
    feat = dataset.drop(['0'], axis=1).to_numpy()

    mm_scaler = preprocessing.StandardScaler()
    feat = mm_scaler.fit_transform(feat)

    return lab, feat


def print_metrics(test_labels, predictions):
    # take column with max predicted score
    f1 = f1_score(labels_test, np.argmax(predictions, axis=1), average='weighted')
    accuracy = accuracy_score(test_labels, np.argmax(predictions, axis=1))
    print(f"The F1_score metric is: {f1}")
    print(f"The accuracy metric is: {accuracy}")

    if len(sys.argv) != 2:
        return


    build_number = sys.argv[1]
    print(f"Build number: {build_number}")
    field_names = ['BUILD_NUMBER', 'F1', 'ACCURACY']
    dict = {'BUILD_NUMBER': build_number, 'F1': f1, 'ACCURACY': accuracy }
    filename = "./metrics.csv"
    file_exists = os.path.isfile(filename)

    with open(filename, 'a') as metrics_file:
        dictwriter_object = DictWriter(metrics_file, fieldnames=field_names)
        if not file_exists:
            dictwriter_object.writeheader()
        dictwriter_object.writerow(dict)
        metrics_file.close()


"""
Load model and data
"""
model = torch.load("CarPrices_pytorch_model.pkl")
cars_dev = pd.read_csv('data/Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names=[str(i) for i in range(5)])


"""
Prepare data
"""
cars_dev = cars_dev.loc[(cars_dev['0'] == 'audi') | (cars_dev['0'] == 'bmw') | (cars_dev['0'] == 'ford') | (cars_dev['0'] == 'opel') | (cars_dev['0'] == 'volkswagen')]
labels_test, features_test = prepare_labels_features(cars_dev)
x_test = Variable(torch.from_numpy(features_test)).float()

"""
Make predictions
"""
pred = model(x_test)
pred = pred.detach().numpy()
print_metrics(labels_test, pred)