Update app.py

app.py

@@ -61,7 +61,7 @@ def process_and_evaluate(df):
     plt.savefig('roc_curve.png')  # Save the ROC curve as an image
     plt.close()  # Close the plot to free memory
 
-    return classification_rep, 'correlation_matrix.png', 'roc_curve.png'
+    return classification_rep, conf_matrix, 'correlation_matrix.png', 'roc_curve.png'
 
 # Create the Streamlit app
 st.set_page_config(page_title="心臟病預測系統")
@@ -72,14 +72,28 @@ st.write("上傳包含心臟病數據的 CSV 文件以獲取分類報告、相
 uploaded_file = st.file_uploader("上傳 CSV 文件", type="csv")
 
 if uploaded_file is not None:
-    # Load the dataset
+    # Load the dataset directly from the uploaded file
     df = pd.read_csv(uploaded_file)
 
-    classification_report, correlation_matrix_path, roc_curve_path = process_and_evaluate(df)
+    # Process the data and generate reports
+    classification_report, conf_matrix, correlation_matrix_path, roc_curve_path = process_and_evaluate(df)
 
     st.subheader("分類報告")
     st.text_area("分類報告", classification_report, height=400)
 
+    st.subheader("混淆矩陣")
+    st.write(conf_matrix)
+    
+    # Plot and display the confusion matrix
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'])
+    plt.ylabel('實際值')
+    plt.xlabel('預測值')
+    plt.title('混淆矩陣')
+    plt.savefig('confusion_matrix.png')  # Save the confusion matrix as an image
+    plt.close()  # Close the plot to free memory
+    st.image('confusion_matrix.png')
+
     st.subheader("相依矩陣")
     st.image(correlation_matrix_path)