How do I add the text output from the console to the PyQT window?

The code below runs a heart disease predictor program and the results are presented by a graph that is generated and shown in the PyQT window. However, we also need the interpretation of the results to be displayed in the dialog box along with the graph. Please help.

import sys from PyQt5.QtWidgets import QDialog, QApplication, QPushButton, QVBoxLayout from numpy import genfromtxt import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from sklearn.svm import LinearSVC from sklearn.decomposition import PCA import pylab as pl from itertools import cycle from sklearn import cross_validation from sklearn.svm import SVC from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar import matplotlib.pyplot as plt import random class Window(QDialog): def __init__(self, parent=None): super(Window, self).__init__(parent) # a figure instance to plot on self.figure = plt.figure() # this is the Canvas Widget that displays the `figure` # it takes the `figure` instance as a parameter to __init__ self.canvas = FigureCanvas(self.figure) # this is the Navigation widget # it takes the Canvas widget and a parent self.toolbar = NavigationToolbar(self.canvas, self) # Just some button connected to `plot` method self.button = QPushButton('Plot the data') self.button.clicked.connect(self.my_model) # set the layout layout = QVBoxLayout() layout.addWidget(self.toolbar) layout.addWidget(self.canvas) layout.addWidget(self.button) self.setLayout(layout) #Method to plot the graph for reduced Dimesions def plot_2D(self,data, target, target_names,ax): colors = cycle('rgbcmykw') target_ids = range(len(target_names)) for i, c, label in zip(target_ids, colors, target_names): ax.scatter(data[target == i, 0], data[target == i, 1], c=c, label=label) plt.legend() plt.savefig('Reduced_PCA_Graph') def my_model(self): ''' plot some random stuff ''' #Loading and pruning the data dataset = genfromtxt('cleveland_data.csv',dtype = float, delimiter=',') #print dataset X = dataset[:,0:12] #Feature Set y = dataset[:,13] #Label Set # Classifying the data using a Linear SVM and predicting the probability of disease belonging to a particular class modelSVM = LinearSVC(C=0.001) pca = PCA(n_components=5, whiten=True).fit(X) X_new = pca.transform(X) # calling plot_2D target_names = ['0','1','2','3','4'] # create an axis ax = self.figure.add_subplot(111) # discards the old graph ax.clear() self.plot_2D(X_new, y, target_names,ax) # refresh canvas self.canvas.draw() #Applying cross validation on the training and test set for validating our Linear SVM Model X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_new, y, test_size=0.4, train_size=0.6, random_state=0) modelSVM = modelSVM.fit(X_train, y_train) print("Testing Linear SVC values using Split") print(modelSVM.score(X_test, y_test)) # printing the Likelihood of disease belonging to a particular class # predicting the outcome count0 = 0 count1 = 0 count2 = 0 count3 = 0 count4 = 0 for i in modelSVM.predict(X_new): if i == 0: count0 = count0+1; elif i == 1: count1 = count1+1; elif i == 2: count2 = count2+1; elif i == 3: count3 = count3+1; elif modelSVM.predict(i) ==4: count4 = count4+1 total = count0+count1+count2+count3+count4 #Predicting the Likelihood #Applying the Principal Component Analysis on the data features modelSVM2 = SVC(C=0.001,kernel='rbf') #Applying cross validation on the training and test set for validating our Linear SVM Model X_train1, X_test1, y_train1, y_test1 = cross_validation.train_test_split(X_new, y, test_size=0.4, train_size=0.6, random_state=0) modelSVM2 = modelSVM2.fit(X_train1, y_train1) print("Testing with RBF using split") print(modelSVM2.score(X_test1, y_test1)) #Using Stratified K Fold skf = cross_validation.StratifiedKFold(y, n_folds=5) for train_index, test_index in skf: # print("TRAIN:", train_index, "TEST:", test_index) X_train3, X_test3 = X[train_index], X[test_index] y_train3, y_test3 = y[train_index], y[test_index] modelSVM3 = SVC(C=0.001,kernel='rbf') modelSVM3 = modelSVM3.fit(X_train3, y_train3) print("Testing using stratified with K folds") print(modelSVM3.score(X_test3, y_test3)) if __name__ == '__main__': app = QApplication(sys.argv) main = Window() main.show() sys.exit(app.exec_())