Update app.py

app.py

@@ -17,6 +17,7 @@ import scipy.stats as stats
 import matplotlib.pyplot as plt #For SHAP charts
 from scipy.stats import pearsonr, spearmanr
 from sklearn.inspection import permutation_importance
+from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
@@ -516,14 +517,14 @@ elif app_mode == "Data Cleaning":
         if columns_to_drop:
             st.warning(f"Will drop: {', '.join(columns_to_drop)}")
             if st.button("Confirm Drop (Columns)"):
-                new_df = df.copy()
-                new_df = new_df.drop(columns=columns_to_drop)
-                update_cleaned_data(new_df)
-                st.rerun() #Force re-run after apply
+                try:
+                    new_df = df.copy()
+                    new_df = new_df.drop(columns=columns_to_drop)
+                    update_cleaned_data(new_df)
+                    st.rerun()  # Force re-run after apply
+                except Exception as e:
+                    st.error(f"Error: {str(e)}")
 
-    # --------------------------
-    # Label Encoding
-    # --------------------------
     # --------------------------
     # Label/One-Hot Encoding
     # --------------------------
@@ -550,8 +551,6 @@ elif app_mode == "Data Cleaning":
                 except Exception as e:
                     st.error(f"Error: {str(e)}")
 
-            
-
     # --------------------------
     # StandardScaler
     # --------------------------
@@ -565,7 +564,7 @@ elif app_mode == "Data Cleaning":
                     scaler = StandardScaler()
                     new_df[scale_cols] = scaler.fit_transform(new_df[scale_cols])
                     update_cleaned_data(new_df)
-                    st.rerun()#Force re-run after apply
+                    st.rerun()  # Force re-run after apply
                 except Exception as e:
                     st.error(f"Error: {str(e)}")