Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import seaborn as sns
+import plotly.graph_objects as go
 from sklearn.ensemble import RandomForestClassifier
 from xgboost import XGBClassifier
 from sklearn.tree import DecisionTreeClassifier
@@ -57,11 +57,10 @@ if uploaded_file is not None:
     # Process the file and get results
     rf_importance, xgb_importance, cart_importance, corr_matrix = calculate_importances(uploaded_file)
     
-    # Display the correlation matrix as a heatmap (static for now)
+    # Display the correlation matrix as an interactive heatmap with Plotly
     st.write("Correlation Matrix:")
-    plt.figure(figsize=(10, 8))
-    sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", cbar=True)
-    st.pyplot(plt)
+    fig_corr = px.imshow(corr_matrix, text_auto=True, aspect="auto", title="Correlation Matrix", color_continuous_scale="coolwarm")
+    st.plotly_chart(fig_corr)
     
     # Plot and display Random Forest Feature Importances with Plotly
     st.write("Random Forest Feature Importance:")