Update app.py

app.py

@@ -106,7 +106,8 @@ if uploaded_file is not None:
     st.subheader("Highly Correlated Features")
     high_corr = corr.abs().unstack().sort_values(ascending=False).drop_duplicates()
     high_corr = high_corr[high_corr.index.get_level_values(0) != high_corr.index.get_level_values(1)]
-    st.write(high_corr)
+    high_corr_df = pd.DataFrame(high_corr)
+    st.write(high_corr_df)
 
     target = st.selectbox("Select Target Variable", df.columns)
     features = [col for col in df.columns if col != target]
@@ -164,12 +165,23 @@ if uploaded_file is not None:
         # Visualization (Bar Graphs for Classification)
         st.subheader("Classification Model Performance Metrics Graph")
         metrics_df.set_index('Model', inplace=True)
-        metrics_df.plot(kind='bar', figsize=(10, 6), colormap='viridis', rot=45)
+        ax = metrics_df.plot(kind='bar', figsize=(10, 6), colormap='coolwarm', rot=45)
         plt.title("Classification Models - Performance Metrics")
         plt.ylabel("Scores")
         plt.xlabel("Models")
         st.pyplot(plt)
 
+        # Download button for the bar graph
+        buf = BytesIO()
+        ax.figure.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Classification Performance Graph as PNG",
+            data=buf,
+            file_name="classification_performance_graph.png",
+            mime="image/png"
+        )
+
     else:  # Continuous target (regression)
         st.subheader("Regression Model Training")
         regressors = {
@@ -215,7 +227,7 @@ if uploaded_file is not None:
             file_name="regression_metrics_table.png",
             mime="image/png"
         )
-        
+
         # Visualization (Bar Graphs for Regression)
         st.subheader("Regression Model Performance Metrics Graph")
         regression_metrics_df.set_index('Model', inplace=True)
@@ -224,3 +236,14 @@ if uploaded_file is not None:
         plt.ylabel("Scores")
         plt.xlabel("Models")
         st.pyplot(plt)
+
+        # Download button for the bar graph
+        buf = BytesIO()
+        plt.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Regression Performance Graph as PNG",
+            data=buf,
+            file_name="regression_performance_graph.png",
+            mime="image/png"
+        )