Update app.py

app.py

@@ -4,7 +4,7 @@ import pandas as pd
 import numpy as np
 
 # Load your dataset here
-file_path_gr = '/content/drive/MyDrive/Colab Notebooks/Homework_04/heart.dat'
+file_path_gr = 'heart.dat'
 df = pd.read_csv(file_path_gr, header=None, sep='\s+')
 
 # Define the X variable with the feature columns
@@ -12,17 +12,17 @@ X = df.drop(df.columns[-1], axis=1)
 
 # Load the saved models
 best_models = [
-    ("Logistic Regression", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Logistic Regression_best_model.pkl')),
-    ("Support Vector Machine", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Support Vector Machine_best_model.pkl')),
-    ("Decision Trees", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Decision Trees_best_model.pkl')),
-    ("Random Forests", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Random Forests_best_model.pkl')),
-    ("MLPClassifier", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/MLPClassifier_best_model.pkl')),
-    ("K-Nearest Neighbors", joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/K-Nearest Neighbors_best_model.pkl')),
-    ("Linear Discriminant Analysis",joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/Linear Discriminant Analysis_best_model.pkl'))
+    ("Logistic Regression", joblib.load('Logistic Regression_best_model.pkl')),
+    ("Support Vector Machine", joblib.load('Support Vector Machine_best_model.pkl')),
+    ("Decision Trees", joblib.load('Decision Trees_best_model.pkl')),
+    ("Random Forests", joblib.load('Random Forests_best_model.pkl')),
+    ("MLPClassifier", joblib.load('MLPClassifier_best_model.pkl')),
+    ("K-Nearest Neighbors", joblib.load('K-Nearest Neighbors_best_model.pkl')),
+    ("Linear Discriminant Analysis",joblib.load('Linear Discriminant Analysis_best_model.pkl'))
 ]
 
 # Load the saved scaler
-scaler = joblib.load('/content/drive/MyDrive/Colab Notebooks/Homework_04/scaler.pkl')
+scaler = joblib.load('scaler.pkl')
 
 attribute_names = [
     "age", "sex", "chest pain type", "resting blood pressure",