Update app.py

app.py

@@ -249,7 +249,7 @@ def process_dataframe(df):
         df_class = df[required_columns_2].fillna("NA").copy()
         
         # Transform categorical columns for prediction DataFrame using the label encoders.
-        for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly']:
+        for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
             try:
                 df_pred[col] = loaded_label_encoder[col].transform(df_pred[col])
             except ValueError as e:
@@ -261,12 +261,12 @@ def process_dataframe(df):
             df_class[col] = df_pred[col]
         
         # Transform the extra columns in the classification DataFrame.
-        for col in ['EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
-            try:
-                df_class[col] = loaded_label_encoder[col].transform(df_class[col])
-            except ValueError as e:
-                print(f'Invalid value in column {col}: {e}', 'error')
-                return pd.DataFrame(), pd.DataFrame()
+        #for col in ['EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
+        #    try:
+        #        df_class[col] = loaded_label_encoder[col].transform(df_class[col])
+        #    except ValueError as e:
+        #        print(f'Invalid value in column {col}: {e}', 'error')
+        #        return pd.DataFrame(), pd.DataFrame()
         
         # Convert both DataFrames to float.
         df_pred = df_pred.astype(float)