Update app.py

app.py

@@ -252,6 +252,8 @@ def predict():
 
 def process_dataframe(df):
     try:
+        df[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']]=df[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']].fillna("NA")
+        
         # Define the columns needed for two parts.
         required_columns = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngBlk', 'EngWht', 'EngOpen',
@@ -261,9 +263,7 @@ def process_dataframe(df):
         
         # Create two DataFrames: one for prediction and one for classification.
         df_pred = df[required_columns].copy()
-        df_pred = df_pred[(df_pred[['EngCts']] > 0.00).all(axis=1) & (df_pred[['EngCts']] <= 0.99).all(axis=1)]
-        df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']]=df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']].fillna("NA")
-        df_pred = df_pred[(df_pred[['MkblAmt', 'GrdAmt', 'ByGrdAmt', 'GiaAmt', 'EngCts']] != 0).all(axis=1)]
+        df_pred = df_pred[(df_pred[['EngCts']] > 0.00).all(axis=1) & (df_pred[['EngCts']] <= 0.99).all(axis=1)] 
         df_class = df[required_columns_2].fillna("NA").copy()
         
         # Transform categorical columns for prediction DataFrame using the label encoders.