edit the format of information

app.py

@@ -48,14 +48,15 @@ def load_interface():
         """
         # Ensemble Classifier for Predicting Smoker or Non-Smoker
         
-        **Contributors**: Matt Soria, Jake Leniart, Francisco Lozano
-        **University**: Depaul University
-        **Class**: DSC 478, Programming Machine Learning
+        **Contributors**: Matt Soria, Jake Leniart, Francisco Lozano\n
+        **University**: Depaul University\n
+        **Class**: DSC 478, Programming Machine Learning\n
 
         ## Overview
         Our project focused on creating a classifier for a Kaggle dataset containing bio-signals and information on individuals' smoking status. The classifier aims to identify whether a patient is a smoker based on 22 provided features. You can find the dataset [here](https://www.kaggle.com/datasets/gauravduttakiit/smoker-status-prediction-using-biosignals?resource=download&select=train_dataset.csv). 
         We developed an Ensemble Classifier with Soft Voting, which combines KNN, SVM, and XGBoost classifiers.
 
+        ## Labels
         - **non-smoker** = 0 
         - **smoker** = 1
 
@@ -63,6 +64,7 @@ def load_interface():
 
         ### Classification Report
 
+        ```
         Train Accuracy:  0.7833977837414656
         Test Accuracy:   0.7885084006669232
 
@@ -74,10 +76,11 @@ def load_interface():
             accuracy                           0.79      7797
            macro avg       0.77      0.77      0.77      7797
         weighted avg       0.79      0.79      0.79      7797
+        ```
 
         ## Confusion Matrix
 
-        ![](file/smoker_cm.png)
+        ![](smoker_cm.png)
 
         ## Final Report
         For more details about our Ensemble Classifier and the individual models, please refer to our Jupyter notebooks in our project repository.