Update app.py

app.py

@@ -9,7 +9,6 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
-from tabulate import tabulate
 
 # File uploader
 st.title("Model Training with Metrics")
@@ -81,20 +80,9 @@ if uploaded_file is not None:
             # Create a metrics DataFrame
             metrics_df = pd.DataFrame(metrics)
 
-            # Format table with tabulate
-            table = tabulate(
-                metrics_df, 
-                headers=['\033[1m' + header + '\033[0m' for header in metrics_df.columns],  # Bold headers
-                tablefmt="grid",  # Set table format to grid for borders
-                showindex=False, 
-                numalign="center", 
-                stralign="center"
-            )
-
-            # Display results in Streamlit
+            # Display results in a table using st.dataframe
             st.subheader("Model Performance Metrics")
-            st.markdown(f"**Model Performance Metrics**")
-            st.text(table)
+            st.dataframe(metrics_df)
 
             # Option to download the model performance metrics (Results Table)
             st.download_button(
@@ -104,10 +92,4 @@ if uploaded_file is not None:
                 mime="text/csv"
             )
 
-            # Option to download the dataset
-            st.download_button(
-                label="Download Dataset",
-                data=df.to_csv(index=False),
-                file_name="dataset.csv",
-                mime="text/csv"
-            )
+