Update app.py

app.py

@@ -1,3 +1,7 @@
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+You said:
 import streamlit as st
 import pandas as pd
 from sklearn.model_selection import train_test_split
@@ -105,7 +109,8 @@ 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.index.get_level_values(0) != high_corr.index.get_level_values(1)]
+    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)
 
     target = st.selectbox("Select Target Variable", df.columns)
@@ -144,18 +149,15 @@ if uploaded_file is not None:
         st.dataframe(metrics_df)
 
         # Save metrics as PNG
-        def generate_classification_report_image():
-            fig, ax = plt.subplots()
-            sns.barplot(data=metrics_df, x="Model", y="Accuracy", ax=ax)
-            ax.set_title("Classification Model Performance")
-            buf = BytesIO()
-            fig.savefig(buf, format="png")
-            buf.seek(0)
-            return buf
-
+        fig, ax = plt.subplots()
+        sns.barplot(data=metrics_df, x="Model", y="Accuracy", ax=ax)
+        ax.set_title("Classification Model Performance")
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
         st.download_button(
             label="Download Classification Report as PNG",
-            data=generate_classification_report_image(),
+            data=buf,
             file_name="classification_report.png",
             mime="image/png"
         )
@@ -189,18 +191,15 @@ if uploaded_file is not None:
         st.dataframe(regression_metrics_df)
 
         # Save metrics as PNG
-        def generate_regression_report_image():
-            fig, ax = plt.subplots()
-            sns.barplot(data=regression_metrics_df, x="Model", y="R² Score", ax=ax)
-            ax.set_title("Regression Model Performance")
-            buf = BytesIO()
-            fig.savefig(buf, format="png")
-            buf.seek(0)
-            return buf
-
+        fig, ax = plt.subplots()
+        sns.barplot(data=regression_metrics_df, x="Model", y="R² Score", ax=ax)
+        ax.set_title("Regression Model Performance")
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
         st.download_button(
             label="Download Regression Report as PNG",
-            data=generate_regression_report_image(),
+            data=buf,
             file_name="regression_report.png",
             mime="image/png"
-        )
+        )