Update app.py

app.py

@@ -430,7 +430,7 @@ elif app_mode == "Data Cleaning":
                     new_df[cols] = new_df[cols].bfill()
 
                 update_cleaned_data(new_df)
-                st.experimental_rerun()  # Force re-run after apply
+                st.run()  # Force re-run after apply
 
     # Data Type Conversion
     enhance_section_title("Data Type Conversion", "🔄")
@@ -465,7 +465,7 @@ elif app_mode == "Data Cleaning":
                     new_df[col_to_convert] = pd.to_datetime(new_df[col_to_convert], format=date_format, errors='coerce')
 
                 update_cleaned_data(new_df)
-                st.experimental_rerun()  # Force re-run after apply
+                st.run()  # Force re-run after apply
             except Exception as e:
                 st.error(f"Error: {str(e)}")
 
@@ -478,7 +478,7 @@ elif app_mode == "Data Cleaning":
             if st.button("Confirm Drop (Columns)"):
                 new_df = df.drop(columns=columns_to_drop)
                 update_cleaned_data(new_df)
-                st.experimental_rerun()  # Force re-run after apply
+                st.run()  # Force re-run after apply
 
     # Label Encoding
     enhance_section_title("Label Encoding", "🔢")
@@ -491,7 +491,7 @@ elif app_mode == "Data Cleaning":
                     le = LabelEncoder()
                     new_df[col] = le.fit_transform(new_df[col].astype(str))
                 update_cleaned_data(new_df)
-                st.experimental_rerun()  # Force re-run after apply
+                st.run()  # Force re-run after apply
 
     # StandardScaler
     enhance_section_title("StandardScaler", "📏")
@@ -503,7 +503,7 @@ elif app_mode == "Data Cleaning":
                 scaler = StandardScaler()
                 new_df[scale_cols] = scaler.fit_transform(new_df[scale_cols])
                 update_cleaned_data(new_df)
-                st.experimental_rerun()  # Force re-run after apply
+                st.run()  # Force re-run after apply
 
     # Pattern-Based Cleaning
     enhance_section_title("Pattern-Based Cleaning", "🕵️")
@@ -516,7 +516,7 @@ elif app_mode == "Data Cleaning":
             new_df = df.copy()
             new_df[selected_col] = new_df[selected_col].str.replace(pattern, replacement, regex=True)
             update_cleaned_data(new_df)
-            st.experimental_rerun()  # Force re-run after apply
+            st.run()  # Force re-run after apply
 
     enhance_section_title("✨ Cleaned Data Preview", "✨")
     with st.expander("✨ Cleaned Data Preview"):