[##midway check-up##] - Searching File with Button

# ========================= 라이브러리 호출 =========================
import sys
import time
import random
import joblib
import librosa
import numpy as np
import pandas as pd
import librosa.display
import matplotlib.pyplot as plt

from math import pi
from PyQt5 import uic
from scipy import stats
from fastdtw import fastdtw
from PyQt5.QtWidgets import *
from sklearn import preprocessing
from scipy.spatial.distance import euclidean
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas

gui_name = "test6_ui.ui"

form_class = uic.loadUiType(gui_name)[0]

class MyWindow(QMainWindow, form_class) :

    def __init__(self):
        super().__init__()
        self.setUI()
        self.setWindowTitle("Plagiarism Checker")


    def setUI(self):
        self.setupUi(self)

        # 입력받을 파일주소
        self.song1_address.setPlaceholderText("ex) C:/dowon/project/IU.wav")
        self.song2_address.setPlaceholderText("ex) C:/dowon/project/IU.wav")

        #클릭 시 메인함수 실행
        self.pushButton.clicked.connect(self.start)

        # 클릭시 파일 박스 오픈
        self.search_1.clicked.connect(self.search_song1)
        self.search_2.clicked.connect(self.search_song2)


    def extractor(self, A, area, sr): # 영역을 시간으로 특정하면 해당 영역의 Data를 가져옴.
        # area = 01:31.5 ~ 02:18.3

        area = str(area)

        # 추출할 영역의 시작점을 초 단위로 저장
        start = int(area[0]) * 600 + int(area[1]) * 60 + int(area[3]) * 10 + int(area[4]) * 1 + int(area[6]) / 10

        # 추출할 영역의 종료점을 초 단위로 저장
        end = int(area[10]) * 600 + int(area[11]) * 60 + int(area[13]) * 10 + int(area[14]) * 1 + int(area[16]) / 10

        # Sampling Rate을 곱해주고, 해당하는 값을 저장
        A_cut = A[int(start * sr): int(end * sr)]

        return A_cut


    def FastDTW(self, A, B): # DTW 계산을 진행하고, 거리를 반환(값이 작을수록 유도가 높음)

        distance, path = fastdtw(A, B, dist=euclidean)

        return distance


    def chroma_score(self, song1, song1_plag_area, song2, song2_plag_area, sr, rand_num): # chroma(12개의 음계)를 비교 분석
        # 나중에 모드를 여러개 정해보자.
        # 만약 random이면 비교대상이 random이고, window면 주기적으로 window를 내는 방식이다.

        song1_ext = np.array(self.extractor(song1, song1_plag_area, sr=sr))
        song2_ext = np.array(self.extractor(song2, song2_plag_area, sr=sr))

        song1_chroma = librosa.feature.chroma_stft(y=song1_ext, sr=sr)
        song2_chroma = librosa.feature.chroma_stft(y=song2_ext, sr=sr)

        # ================================song1의 난수 생성================================

        song1_start = int(song1_plag_area[0]) * 600 + int(song1_plag_area[1]) * 60 + int(song1_plag_area[3]) * 10 + int(
            song1_plag_area[4]) * 1 + int(song1_plag_area[6]) / 10
        song1_end = int(song1_plag_area[10]) * 600 + int(song1_plag_area[11]) * 60 + int(
            song1_plag_area[13]) * 10 + int(song1_plag_area[14]) * 1 + int(song1_plag_area[16]) / 10
        song1_len = int(len(song1) / sr)
        song1_len_plag = int(song1_end - song1_start)

        rand_range = song1_len - song1_len_plag

        song1_rand_saver = random.sample(range(0, rand_range), rand_num)

        cnt_1 = 0
        for i in range(0, rand_num):

            while (abs(song1_rand_saver[i] - int(song1_start)) <= int(song1_len_plag / 2)):
                new_rand = random.sample(range(0, rand_range), 1)
                song1_rand_saver[i] = new_rand[0]
                cnt_1 += 1

        # ================================song2의 난수 생성================================

        song2_start = int(song2_plag_area[0]) * 600 + int(song2_plag_area[1]) * 60 + int(song2_plag_area[3]) * 10 + int(
            song2_plag_area[4]) * 1 + int(song2_plag_area[6]) / 10
        song2_end = int(song2_plag_area[10]) * 600 + int(song2_plag_area[11]) * 60 + int(
            song2_plag_area[13]) * 10 + int(song2_plag_area[14]) * 1 + int(song2_plag_area[16]) / 10
        song2_len = int(len(song2) / sr)
        song2_len_plag = int(song2_end - song2_start)

        rand_range = song2_len - song2_len_plag

        song2_rand_saver = random.sample(range(0, rand_range), rand_num)

        cnt_2 = 0
        for i in range(0, rand_num):

            while (abs(song2_rand_saver[i] - int(song2_start)) <= int(song2_len_plag / 2)):
                new_rand = random.sample(range(0, rand_range), 1)
                song2_rand_saver[i] = new_rand[0]
                cnt_2 += 1

        # ================================모든 난수 생성 완료================================

        # ================================song1 random data 저장 (2차원 배열로)================================
        song1_rand_data = []

        for i in range(0, rand_num):
            song1_rand_data.append([])
            for j in range(0, len(song1_ext)):
                song1_rand_data[i].append(0)

        for i in range(0, rand_num):
            song1_rand_data[i] = song1[song1_rand_saver[i] * sr: (song1_rand_saver[i] + song1_len_plag) * sr]

        # ================================song2 random data 저장 (2차원 배열로)================================
        song2_rand_data = []

        for i in range(0, rand_num):
            song2_rand_data.append([])
            for j in range(0, len(song2_ext)):
                song2_rand_data[i].append(0)

        for i in range(0, rand_num):
            song2_rand_data[i] = song2[song2_rand_saver[i] * sr: (song2_rand_saver[i] + song2_len_plag) * sr]

        # ================================모든 random data 생성 완료================================

        # ================================song1의 chroma Data 생성================================

        song1_rand_chroma = []

        for i in range(0, rand_num):
            song1_rand_chroma.append([])
            for j in range(0, len(song1_chroma[0])):
                song1_rand_chroma[i].append(0)

        for i in range(0, rand_num):
            song1_rand_chroma[i] = librosa.feature.chroma_stft(y=song1_rand_data[i], sr=sr)

        # ================================song2의 chroma Data 생성================================

        song2_rand_chroma = []

        for i in range(0, rand_num):
            song2_rand_chroma.append([])
            for j in range(0, len(song2_chroma[
                                      0])):  ####################################################################################
                song2_rand_chroma[i].append(0)

        for i in range(0, rand_num):
            song2_rand_chroma[i] = librosa.feature.chroma_stft(y=song2_rand_data[i], sr=sr)

        # ================================모든 chroma data 생성 완료================================

        song1_rand_chroma = np.array(song1_rand_chroma)
        song2_rand_chroma = np.array(song2_rand_chroma)

        # ================================chroma data 비교교================================

        col_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']

        song1_vs_song2 = []

        for i in range(0, 12):
            song1_vs_song2.append(self.FastDTW(song1_chroma[i], song2_chroma[i]))

        song1_vs_song2 = np.array(song1_vs_song2)

        save_df = pd.DataFrame([song1_vs_song2], columns=col_names)

        save_list = []

        for i in range(0, rand_num):

            for j in range(0, 12):
                save_list.append(self.FastDTW(song1_chroma[j], song2_rand_chroma[i][j]))

            save_list = np.array(save_list)
            save_list = pd.DataFrame([save_list], columns=col_names)

            save_df = pd.concat([save_df, save_list])

            save_list = []

        for i in range(0, rand_num):

            for j in range(0, 12):
                save_list.append(self.FastDTW(song2_chroma[j], song1_rand_chroma[i][j]))

            save_list = np.array(save_list)
            save_list = pd.DataFrame([save_list], columns=col_names)

            save_df = pd.concat([save_df, save_list])

            save_list = []

        save_df.rename(columns={'C': 0, 'C#': 1, 'D': 2, 'D#': 3,
                                'E': 4, 'F': 5, 'F#': 6, 'G': 7,
                                'G#': 8, 'A': 9, 'A#': 10, 'B': 11}, inplace=True)

        score = 0

        save_df = save_df.round(2)
        save_df = save_df.reset_index(drop=True)

        for i in range(0, 12):

            order = 0

            save_df = save_df.sort_values(by=i)
            index_saver = save_df.index

            for j in range(0, len(index_saver)):

                if (index_saver[j] != 0):
                    order += 1

                else:
                    order += 1
                    score += order

                    break

        score = 100 - ((score - 12) / ((rand_num * 2 + 1) * 12) * 100)

        save_df.rename(columns={0: 'C', 1: 'C#', 2: 'D', 3: 'D#',
                                4: 'E', 5: 'F', 6: 'F#', 7: 'G',
                                8: 'G#', 9: 'A', 10: 'A#', 11: 'B'}, inplace=True)

        save_df = save_df.sort_index(ascending=True)

        return round(score, 2)

    def check_genre(self, song, sr, model):

        col_names_drop = ['chroma_stft_mean', 'chroma_stft_var',
                          'rms_mean', 'rms_var',
                          'spectral_centroid_mean', 'spectral_centroid_var',
                          'spectral_bandwidth_mean', 'spectral_bandwidth_var',
                          'rolloff_mean', 'rolloff_var',
                          'zero_crossing_rate_mean', 'zero_crossing_rate_var',
                          'harmony_mean', 'harmony_var',
                          'perceptr_mean', 'perceptr_var',
                          'tempo',
                          'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean', 'mfcc3_var', 'mfcc4_mean',
                          'mfcc4_var', 'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var',
                          'mfcc8_mean',
                          'mfcc8_var', 'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var', 'mfcc11_mean',
                          'mfcc12_mean', 'mfcc12_var',
                          'mfcc13_mean', 'mfcc14_mean', 'mfcc15_mean', 'mfcc15_var', 'mfcc16_mean', 'mfcc16_var',
                          'mfcc17_mean', 'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean',
                          'mfcc20_var']

        col_names = ['chroma_stft_mean', 'chroma_stft_var', 'rms_mean', 'rms_var',
                     'spectral_centroid_mean', 'spectral_centroid_var', 'spectral_bandwidth_mean',
                     'spectral_bandwidth_var', 'rolloff_mean', 'rolloff_var', 'zero_crossing_rate_mean',
                     'zero_crossing_rate_var', 'harmony_mean', 'harmony_var', 'perceptr_mean', 'perceptr_var',
                     'tempo', 'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean', 'mfcc3_var',
                     'mfcc4_mean', 'mfcc4_var', 'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean',
                     'mfcc7_var', 'mfcc8_mean',
                     'mfcc8_var', 'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var', 'mfcc11_mean', 'mfcc11_var',
                     'mfcc12_mean',
                     'mfcc12_var', 'mfcc13_mean', 'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean',
                     'mfcc15_var', 'mfcc16_mean',
                     'mfcc16_var', 'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean',
                     'mfcc19_var', 'mfcc20_mean', 'mfcc20_var']

        data_3sec = pd.read_csv(r"source/features_3_sec.csv");
        data_30sec = pd.read_csv(r"source/features_30_sec.csv");

        data = pd.concat([data_3sec, data_30sec])

        X = data.drop("label", axis=1)
        X_droped = X.drop(["filename", "length", "mfcc13_var", "mfcc17_var", "mfcc14_var", "mfcc11_var"], axis=1,
                          inplace=False)

        chroma_shift = librosa.feature.chroma_stft(song, n_fft=2048, hop_length=512)  # 음악의 크로마 특징
        rmse = librosa.feature.rms(song, frame_length=512, hop_length=512)  # RMS값
        spectral_centroids = librosa.feature.spectral_centroid(song, sr=sr)  # 스펙트럼 무게 중심
        spec_bw = librosa.feature.spectral_bandwidth(song, sr=sr)  # 스펙트럼 대역폭
        spectral_rolloff = librosa.feature.spectral_rolloff(song, sr=sr)[0]  # rolloff
        zcr = librosa.feature.zero_crossing_rate(song, hop_length=512)  # zero to crossing
        y_harm, y_perc = librosa.effects.hpss(song)  # 하모닉, 충격파
        tempo, _ = librosa.beat.beat_track(song, sr=sr)  # 템포
        mfcc = librosa.feature.mfcc(song, sr=sr, n_mfcc=20)  # mfcc 20까지 추출

        features_extracted = np.hstack([
            np.mean(chroma_shift),
            np.var(chroma_shift),
            np.mean(rmse),
            np.var(rmse),
            np.mean(spectral_centroids),
            np.var(spectral_centroids),
            np.mean(spec_bw),
            np.var(spec_bw),
            np.mean(spectral_rolloff),
            np.var(spectral_rolloff),
            np.mean(zcr),
            np.var(zcr),
            np.mean(y_harm),
            np.var(y_harm),
            np.mean(y_perc),
            np.var(y_perc),
            tempo,
            np.mean(mfcc.T, axis=0),
            np.var(mfcc.T, axis=0)
        ])

        features = features_extracted.reshape(1, 57)
        input_df = pd.DataFrame(features, columns=col_names)
        input_df = input_df.drop(["mfcc13_var", "mfcc17_var", "mfcc14_var", "mfcc11_var"], axis=1)

        df_concated = pd.concat([X_droped, input_df], axis=0)

        ss = StandardScaler()
        concat_scaled = ss.fit_transform(np.array(df_concated.iloc[:, :], dtype=float))

        concat_df = pd.DataFrame(concat_scaled, columns=col_names_drop)

        input_df = concat_df.iloc[-1]
        input_df = pd.Series.to_frame(input_df)
        input_arr = input_df.to_numpy()
        input_arr = input_arr.reshape(1, 53)

        input_df = pd.DataFrame(input_arr, columns=col_names_drop)

        prediction = model.predict(input_df)

        if prediction == 0:
            answer = "blues"
        elif prediction == 1:
            answer = "classical"
        elif prediction == 2:
            answer = "country"
        elif prediction == 3:
            answer = "disco"
        elif prediction == 4:
            answer = "hiphop"
        elif prediction == 5:
            answer = "jazz"
        elif prediction == 6:
            answer = "metal"
        elif prediction == 7:
            answer = "pop"
        elif prediction == 8:
            answer = "reggae"
        else:
            answer = "rock"

        return prediction[0]



    def Data_PreProcessing(self, data1, data2):

        data1 = pd.read_csv(data1);
        data2 = pd.read_csv(data2);

        data = pd.concat([data1, data2])

        X = data.drop("label", axis=1)
        y = data.iloc[:, -1]

        cvt = preprocessing.LabelEncoder()
        y_encoded = cvt.fit_transform(y)

        X_droped = X.drop(["filename", "length", "mfcc13_var", "mfcc17_var", "mfcc14_var", "mfcc11_var"], axis=1,
                          inplace=False)

        ss = StandardScaler()
        X_scaled = ss.fit_transform(np.array(X_droped.iloc[:, :], dtype=float))

        X_df = pd.DataFrame(X_scaled, columns=['chroma_stft_mean', 'chroma_stft_var',
                                               'rms_mean', 'rms_var',
                                               'spectral_centroid_mean', 'spectral_centroid_var',
                                               'spectral_bandwidth_mean', 'spectral_bandwidth_var',
                                               'rolloff_mean', 'rolloff_var',
                                               'zero_crossing_rate_mean', 'zero_crossing_rate_var',
                                               'harmony_mean', 'harmony_var',
                                               'perceptr_mean', 'perceptr_var',
                                               'tempo',
                                               'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean',
                                               'mfcc3_var',
                                               'mfcc4_mean', 'mfcc4_var', 'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean',
                                               'mfcc6_var',
                                               'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean', 'mfcc8_var', 'mfcc9_mean',
                                               'mfcc9_var',
                                               'mfcc10_mean', 'mfcc10_var', 'mfcc11_mean', 'mfcc12_mean', 'mfcc12_var',
                                               'mfcc13_mean', 'mfcc14_mean', 'mfcc15_mean', 'mfcc15_var', 'mfcc16_mean',
                                               'mfcc16_var',
                                               'mfcc17_mean', 'mfcc18_mean', 'mfcc18_var', 'mfcc19_mean', 'mfcc19_var',
                                               'mfcc20_mean', 'mfcc20_var'])

        y_df = pd.DataFrame(y_encoded, columns=['target'])

        X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2, random_state=156, shuffle=True)

        evals = [(X_test, y_test)]

        return X_train, X_test, y_train, y_test, evals





    def load_data(self, song1, song2, sr):

        song1_data, sr = librosa.load(song1, sr=sr)
        song2_data, sr = librosa.load(song2, sr=sr)

        return song1_data, song2_data, sr

    def tempo_score(self, song1, song2, sr):

        song1_harm, song1_perc = librosa.effects.hpss(song1)
        song1_zcr = librosa.feature.zero_crossing_rate(song1, hop_length=512)
        song1_spectral_centroid = librosa.feature.spectral_centroid(song1, sr=sr)

        song1_perceptr_var = np.var(song1_perc)
        song1_zcr_var = np.var(song1_zcr)
        song1_spectral_centroid_mean = np.mean(song1_spectral_centroid)

        song1_perceptr_var_scaled = ((song1_perceptr_var - 4.67 * (10 ** -8)) / (0.058879 - 4.67 * (10 ** -8)))
        song1_zcr_var_scaled = ((song1_zcr_var - 5.02 * (10 ** -6)) / (0.065185 - 5.02 * (10 ** -6)))
        song1_spectral_centroid_scaled = ((song1_spectral_centroid_mean - 300) / (5432.534 - 300))

        song1_score = np.mean(song1_perceptr_var_scaled + song1_zcr_var_scaled + song1_spectral_centroid_scaled)

        song2_harm, song2_perc = librosa.effects.hpss(song2)
        song2_zcr = librosa.feature.zero_crossing_rate(song2, hop_length=512)
        song2_spectral_centroid = librosa.feature.spectral_centroid(song2, sr=sr)

        song2_perceptr_var = np.var(song2_perc)
        song2_zcr_var = np.var(song2_zcr)
        song2_spectral_centroid_mean = np.mean(song2_spectral_centroid)

        song2_perceptr_var_scaled = ((song2_perceptr_var - 4.67 * (10 ** -8)) / (0.058879 - 4.67 * (10 ** -8)))
        song2_zcr_var_scaled = ((song2_zcr_var - 5.02 * (10 ** -6)) / (0.065185 - 5.02 * (10 ** -6)))
        song2_spectral_centroid_scaled = ((song2_spectral_centroid_mean - 300) / (5432.534 - 300))

        song2_score = np.mean(song2_perceptr_var_scaled + song2_zcr_var_scaled + song2_spectral_centroid_scaled)

        song1_score = round(song1_score, 3)
        song2_score = round(song2_score, 3)

        tempo_score = round(100 - ((abs(song1_score - song2_score) / 1.2) * 100), 2)

        return tempo_score

    def dtw_score(self, song1, song1_plag_area, song2, song2_plag_area, sr, rand_num):
        # 나중에 모드를 여러개 정해보자.
        # 만약 random이면 비교대상이 random이고, window면 주기적으로 window를 내는 방식이다.

        song1_ext = np.array(self.extractor(song1, song1_plag_area, sr=sr))
        song2_ext = np.array(self.extractor(song2, song2_plag_area, sr=sr))

        # ================================song1의 난수 생성================================

        song1_start = int(song1_plag_area[0]) * 600 + int(song1_plag_area[1]) * 60 + int(song1_plag_area[3]) * 10 + int(
            song1_plag_area[4]) * 1 + int(song1_plag_area[6]) / 10
        song1_end = int(song1_plag_area[10]) * 600 + int(song1_plag_area[11]) * 60 + int(
            song1_plag_area[13]) * 10 + int(song1_plag_area[14]) * 1 + int(song1_plag_area[16]) / 10
        song1_len = int(len(song1) / sr)
        song1_len_plag = int(song1_end - song1_start)

        rand_range = song1_len - song1_len_plag

        song1_rand_saver = random.sample(range(0, rand_range), rand_num)

        cnt_1 = 0
        for i in range(0, rand_num):

            while (abs(song1_rand_saver[i] - int(song1_start)) <= int(song1_len_plag / 2)):
                new_rand = random.sample(range(0, rand_range), 1)
                song1_rand_saver[i] = new_rand[0]
                cnt_1 += 1

        # ================================song2의 난수 생성================================

        song2_start = int(song2_plag_area[0]) * 600 + int(song2_plag_area[1]) * 60 + int(song2_plag_area[3]) * 10 + int(
            song2_plag_area[4]) * 1 + int(song2_plag_area[6]) / 10
        song2_end = int(song2_plag_area[10]) * 600 + int(song2_plag_area[11]) * 60 + int(
            song2_plag_area[13]) * 10 + int(song2_plag_area[14]) * 1 + int(song2_plag_area[16]) / 10
        song2_len = int(len(song2) / sr)
        song2_len_plag = int(song2_end - song2_start)

        rand_range = song2_len - song2_len_plag

        song2_rand_saver = random.sample(range(0, rand_range), rand_num)

        cnt_2 = 0
        for i in range(0, rand_num):

            while (abs(song2_rand_saver[i] - int(song2_start)) <= int(song2_len_plag / 2)):
                new_rand = random.sample(range(0, rand_range), 1)
                song2_rand_saver[i] = new_rand[0]
                cnt_2 += 1

        # ================================모든 난수 생성 완료================================
        # ================================song1 random data 저장 (2차원 배열로)================================

        song1_rand_data = []

        for i in range(0, rand_num):
            song1_rand_data.append([])
            for j in range(0, len(song1_ext)):
                song1_rand_data[i].append(0)

        for i in range(0, rand_num):
            song1_rand_data[i] = song1[song1_rand_saver[i] * sr: (song1_rand_saver[i] + song1_len_plag) * sr]

        # ================================song2 random data 저장 (2차원 배열로)================================
        song2_rand_data = []

        for i in range(0, rand_num):
            song2_rand_data.append([])
            for j in range(0, len(song2_ext)):
                song2_rand_data[i].append(0)

        for i in range(0, rand_num):
            song2_rand_data[i] = song2[song2_rand_saver[i] * sr: (song2_rand_saver[i] + song2_len_plag) * sr]
        # ================================모든 random data 생성 완료================================

        # ================================dtw scoring================================

        saver = []
        order = 0

        song1song2 = self.FastDTW(song1_ext, song2_ext)

        saver.append(song1song2)

        for i in range(0, rand_num):
            saver.append(self.FastDTW(song1_ext, song2_rand_data[i]))
            saver.append(self.FastDTW(song2_ext, song1_rand_data[i]))

        save_df = pd.DataFrame(saver, columns=['distance'])

        save_df = save_df.sort_values(by='distance')
        index_saver = save_df.index

        for i in range(0, len(index_saver)):
            if (index_saver[i] != 0):
                order += 1

            else:
                order += 1

                break

        score = round(100 - ((order / (2 * rand_num + 1)) * 100), 2)

        return score




    def same_checker(self, song1, song2, sr):

        pearson, _ = stats.pearsonr(np.array(song1[sr * 3:sr * 6]), np.array(song2[sr * 3:sr * 6]))

        return abs(pearson)

    def genre_score(self, song1, song2, sr, model):

        conf_list = [[10, 2, 2, 2, 3, 1, 3, 0, 2, 4],
                     [0, 10, 0, 0, 0, 3, 0, 0, 0, 0],
                     [2, 0, 10, 1, 0, 6, 1, 1, 3, 2],
                     [1, 3, 1, 10, 4, 2, 1, 4, 1, 6],
                     [0, 0, 3, 2, 10, 0, 1, 5, 2, 1],
                     [4, 9, 7, 0, 0, 10, 1, 0, 0, 0],
                     [1, 1, 1, 0, 0, 0, 10, 0, 0, 2],
                     [0, 1, 1, 2, 3, 0, 0, 10, 5, 2],
                     [0, 2, 3, 3, 4, 1, 0, 3, 10, 1],
                     [3, 1, 5, 1, 2, 2, 4, 1, 3, 10]]

        conf_arr = np.array(conf_list)

        song1_genre = self.check_genre(song1, sr=sr, model=model)
        song2_genre = self.check_genre(song2, sr=sr, model=model)

        genre_score = round(((conf_arr[song1_genre][song2_genre] + conf_arr[song2_genre][song1_genre]) / 20) * 100, 2)

        return genre_score

    def run_algo(self, song1, song1_plag_area, song2, song2_plag_area, sr, model, hard):
        # hard : 7

        same_score = self.same_checker(song1, song2, sr)

        if same_score < 0.9:


            genre = self.genre_score(song1, song2, sr, model)


            chroma = self.chroma_score(song1, song1_plag_area, song2, song2_plag_area, sr, rand_num=hard)


            tempo = self.tempo_score(song1, song2, sr=sr)


            song1, song2, sr = self.load_data(song1_file, song2_file, sr=300)
            dtw = self.dtw_score(song1, song1_plag_area, song2, song2_plag_area, sr=300, rand_num=hard - 2)


            final_score = round((dtw * 45 + chroma * 30 + tempo * 15 + genre * 10) / 100, 2)

            print(final_score)



        else:
            print("같은 노래를 입력하셨습니다.")

        return final_score, genre, chroma, tempo, dtw

    def start(self):

        time.sleep(1)

        global song1_file
        global song2_file

        song1_file = self.song1_address.text()
        song2_file = self.song2_address.text()

        start_boon_1 = self.song1_boon_start.value()
        start_cho_1 = self.song1_cho_start.value()
        end_boon_1 = self.song1_boon_end.value()
        end_cho_1 = self.song1_cho_end.value()

        start_boon_2 = self.song2_boon_start.value()
        start_cho_2 = self.song2_cho_start.value()
        end_boon_2 = self.song2_boon_end.value()
        end_cho_2 = self.song2_cho_end.value()

        if (int(start_cho_1) >= 10) :

            if (int(end_cho_1) >= 10) :

                song1_plag = str(0) + str(start_boon_1) + ":" + str(round(start_cho_1, 1)) + " ~ " + str(0) + str(end_boon_1)+ ":" + str(round(end_cho_1, 1))
            else :
                song1_plag = str(0) + str(start_boon_1) + ":" + str(round(start_cho_1, 1)) + " ~ " + str(0) + str(end_boon_1) + ":" + str(0) + str(round(end_cho_1, 1))

        else :
            if (int(end_cho_1) >= 10) :
                song1_plag = str(0) + str(start_boon_1) + ":" + str(0) + str(round(start_cho_1, 1)) + " ~ " + str(0) + str(end_boon_1)+ ":" + str(round(end_cho_1, 1))
            else :
                song1_plag = str(0) + str(start_boon_1) + ":" + str(0) + str(round(start_cho_1, 1)) + " ~ " + str(0) + str(end_boon_1) + ":" + str(0) + str(round(end_cho_1, 1))


        if (int(start_cho_2) >= 10) :
            if (int(end_cho_2) >= 10) :
                song2_plag = str(0) + str(start_boon_2) + ":" + str(round(start_cho_2, 1)) + " ~ " + str(0) + str(end_boon_2)+ ":" + str(round(end_cho_2, 1))
            else :
                song2_plag = str(0) + str(start_boon_2) + ":" + str(round(start_cho_2, 1)) + " ~ " + str(0) + str(end_boon_2) + ":" + str(0) + str(round(end_cho_2, 1))

        else :
            if (int(end_cho_1) >= 10) :
                song2_plag = str(0) + str(start_boon_2) + ":" + str(0) + str(round(start_cho_2, 1)) + " ~ " + str(0) + str(end_boon_2)+ ":" + str(round(end_cho_2, 1))
            else :
                song2_plag = str(0) + str(start_boon_2) + ":" + str(0) + str(round(start_cho_2, 1)) + " ~ " + str(0) + str(end_boon_2) + ":" + str(0) + str(round(end_cho_2, 1))


        print(song1_plag)
        print(song2_plag)

        ### Train Data, Test Data, Evaluation Data

        X_train, X_test, y_train, y_test, evals = self.Data_PreProcessing("source/features_3_sec.csv", "source/features_30_sec.csv")


        ### Call ML model (LGBMClassifier)
        model = joblib.load(r'source/my_model.pkl')

        song1_plag_area = song1_plag
        song2_plag_area = song2_plag

        ### 음원 Data 2개, Sampling Rate
        song1, song2, sr = self.load_data(song1_file, song2_file, sr=22050)

        final_score, genre, chroma, tempo, dtw = self.run_algo(song1, song1_plag_area, song2, song2_plag_area, sr, model=model, hard=10)

        global fin_score
        fin_score = final_score

        global genre_score
        genre_score = genre

        global chroma_score
        chroma_score = chroma

        global tempo_score
        tempo_score = tempo

        global dtw_score
        dtw_score = dtw

        score_list = [genre, chroma, tempo, dtw]

        self.drawGraph()

    def drawGraph(self):

        self.fig = plt.Figure()
        self.canvas = FigureCanvas(self.fig)
        self.verticalLayout.addWidget(self.canvas)


        df = pd.DataFrame(
            data={'genre': [genre_score],
                  'chroma': [chroma_score],
                  'tempo': [tempo_score],
                  'dtw': [dtw_score]},
            columns=['genre', 'chroma', 'tempo', 'dtw']
        )

        var = df.columns.to_list()[0:]

        val_1st = df.loc[0, :].values.tolist()

        val_1st += val_1st[:1]

        num_var = len(var)
        deg = [n /float(num_var) * 2 * pi for n in range(num_var)]
        deg += deg[:1]



        ax = self.fig.add_subplot(111, polar=True)

        ax.set_xticks(deg[:-1])
        ax.set_xticklabels(var, color="g", size=9)

        ax.set_rlabel_position(45)

        ax.set_yticks(range(0, 125, 25))
        ax.set_ylim([0, 100])
        ax.set_yticklabels([0, 25, 50, 75, 100], color="y", size=10)

        ax.plot(deg, val_1st, linewidth=1, linestyle='solid')
        ax.fill(deg, val_1st, 'g', alpha=0.2)

        if (fin_score <= 72) :
            spk = "Score : " + str(fin_score) + "                   Not Plagiarism!"
        else :
            spk = "Score : " + str(fin_score) + "                     Plagiarism!"
        ax.set_title(spk)

        self.canvas.draw()

    def search_song1(self):

        fname_1 = QFileDialog.getOpenFileName(self)
        self.song1_address.setText(fname_1[0])

    def search_song2(self):

        fname_2 = QFileDialog.getOpenFileName(self)
        self.song2_address.setText(fname_2[0])

if __name__ == "__main__" :
    print("hello world");
    app = QApplication(sys.argv)
    myApp = MyWindow()
    myApp.show()
    app.exec_()