Update app.py

app.py

@@ -83,6 +83,17 @@ if uploaded_file is not None:
         mime="text/csv"
     )
 
+    # Correlation Heatmap
+    st.subheader("Correlation Heatmap")
+    corr = df.corr()
+    plt.figure(figsize=(10, 6))
+    sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')
+    st.pyplot(plt)
+
+    # Correlation Metrics
+    st.subheader("Correlation Metrics")
+    st.dataframe(corr)
+
     target = st.selectbox("Select Target Variable", df.columns)
     features = [col for col in df.columns if col != target]
     X = df[features]
@@ -118,12 +129,27 @@ if uploaded_file is not None:
         st.subheader("Classification Model Performance Metrics")
         st.dataframe(metrics_df)
 
+        # Download as CSV
         st.download_button(
             label="Download Classification Report as CSV",
             data=metrics_df.to_csv(index=False),
             file_name="classification_report.csv",
             mime="text/csv"
         )
+
+        # Download as PNG
+        fig, ax = plt.subplots()
+        ax.axis('off')
+        table = ax.table(cellText=metrics_df.values, colLabels=metrics_df.columns, loc='center', cellLoc='center')
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        plt.savefig("classification_report.png")
+        st.download_button(
+            label="Download Classification Report as PNG",
+            data=open("classification_report.png", "rb"),
+            file_name="classification_report.png",
+            mime="image/png"
+        )
     
     else:  # Continuous target (regression)
         st.subheader("Regression Model Training")
@@ -153,9 +179,15 @@ if uploaded_file is not None:
         st.subheader("Regression Model Performance Metrics")
         st.dataframe(regression_metrics_df)
 
+        # Download as CSV
         st.download_button(
             label="Download Regression Report as CSV",
             data=regression_metrics_df.to_csv(index=False),
             file_name="regression_report.csv",
             mime="text/csv"
         )
+
+        # Download as PNG
+        fig, ax = plt.subplots()
+        ax.axis('off')
+        table = ax.table(cellText=regression_metrics_df.values