Update app.py

app.py

@@ -8,25 +8,25 @@ heart=pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp',
                                                             'oldpeak', 'slope', 'ca', 'thal', 'heart disease'])
 
 # load the saved models
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/Tree.pkl', 'rb') as f:
+with open('Tree.pkl', 'rb') as f:
     tree_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/svm.pkl', 'rb') as f:
+with open('svm.pkl', 'rb') as f:
     svm_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/QDA.pkl', 'rb') as f:
+with open('QDA.pkl', 'rb') as f:
     qda_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/MLP.pkl', 'rb') as f:
+with open('MLP.pkl', 'rb') as f:
     mlp_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/Log.pkl', 'rb') as f:
+with open('Log.pkl', 'rb') as f:
     log_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/LDA.pkl', 'rb') as f:
+with open('LDA.pkl', 'rb') as f:
     lda_model = pickle.load(f)
 
-with open('/content/drive/MyDrive/Colab Notebooks/Homework04/For.pkl', 'rb') as f:
+with open('For.pkl', 'rb') as f:
     for_model = pickle.load(f)
 
 # Define the function to make predictions