Update app.py

app.py

@@ -105,9 +105,8 @@ if uploaded_file is not None:
     # Highlight highly correlated pairs
     st.subheader("Highly Correlated Features")
     high_corr = corr.abs().unstack().sort_values(ascending=False).drop_duplicates()
-    high_corr = high_corr[high_corr >= 0.8]
-    high_corr_df = high_corr[high_corr.index.get_level_values(0) != high_corr.index.get_level_values(1)]
-    st.write(high_corr_df)
+    high_corr = high_corr[high_corr.index.get_level_values(0) != high_corr.index.get_level_values(1)]
+    st.write(high_corr)
 
     target = st.selectbox("Select Target Variable", df.columns)
     features = [col for col in df.columns if col != target]
@@ -144,7 +143,7 @@ if uploaded_file is not None:
         st.subheader("Classification Model Performance Metrics")
         st.dataframe(metrics_df)
 
-                # Save metrics as PNG (table form)
+        # Save metrics as PNG (table form)
         fig, ax = plt.subplots(figsize=(8, 4))
         ax.axis('tight')
         ax.axis('off')
@@ -161,7 +160,16 @@ if uploaded_file is not None:
             file_name="classification_metrics_table.png",
             mime="image/png"
         )
-    
+        
+        # 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)
+        plt.title("Classification Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)
+
     else:  # Continuous target (regression)
         st.subheader("Regression Model Training")
         regressors = {
@@ -207,5 +215,12 @@ 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)
+        regression_metrics_df.plot(kind='bar', figsize=(10, 6), colormap='coolwarm', rot=45)
+        plt.title("Regression Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)