Update app.py

app.py

@@ -241,7 +241,8 @@ def process_dataframe(df):
         required_columns = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngBlk', 'EngWht', 'EngOpen',
                            'EngPav', 'EngAmt']
-        required_columns_2 = required_columns + ['EngBlk', 'EngWht', 'EngOpen', 'EngPav']
+        required_columns_2 = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
+                           'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngAmt']
         
         # Create two DataFrames: one for prediction and one for classification.
         df_pred = df[required_columns].copy()
@@ -294,22 +295,22 @@ def process_dataframe(df):
         dx['cut_change'] = cut_model.predict(x)
         dx['qua_change'] = qua_model.predict(x)
         dx['shp_change'] = shp_model.predict(x)
-        dx['Change_Blk_Eng_to_Mkbl_value'] = blk_eng_to_mkbl_model.predict(x2)
-        dx['Change_Wht_Eng_to_Mkbl_value'] = wht_eng_to_mkbl_model.predict(x2)
-        dx['Change_Open_Eng_to_Mkbl_value'] = open_eng_to_mkbl_model.predict(x2)
-        dx['Change_Pav_Eng_to_Mkbl_value'] = pav_eng_to_mkbl_model.predict(x2)
-        dx['Change_Blk_Eng_to_Grd_value'] = blk_eng_to_grade_model.predict(x2)
-        dx['Change_Wht_Eng_to_Grd_value'] = wht_eng_to_grade_model.predict(x2)
-        dx['Change_Open_Eng_to_Grd_value'] = open_eng_to_grade_model.predict(x2)
-        dx['Change_Pav_Eng_to_Grd_value'] = pav_eng_to_grade_model.predict(x2)
-        dx['Change_Blk_Eng_to_ByGrd_value'] = blk_eng_to_bygrade_model.predict(x2)
-        dx['Change_Wht_Eng_to_ByGrd_value'] = wht_eng_to_bygrade_model.predict(x2)
-        dx['Change_Open_Eng_to_ByGrd_value'] = open_eng_to_bygrade_model.predict(x2)
-        dx['Change_Pav_Eng_to_ByGrd_value'] = pav_eng_to_bygrade_model.predict(x2)
-        dx['Change_Blk_Eng_to_Gia_value'] = blk_eng_to_gia_model.predict(x2)
-        dx['Change_Wht_Eng_to_Gia_value'] = wht_eng_to_gia_model.predict(x2)
-        dx['Change_Open_Eng_to_Gia_value'] = open_eng_to_gia_model.predict(x2)
-        dx['Change_Pav_Eng_to_Gia_value'] = pav_eng_to_gia_model.predict(x2)
+        dx['Change_Blk_Eng_to_Mkbl_value'] = blk_eng_to_mkbl_model.predict(x)
+        dx['Change_Wht_Eng_to_Mkbl_value'] = wht_eng_to_mkbl_model.predict(x)
+        dx['Change_Open_Eng_to_Mkbl_value'] = open_eng_to_mkbl_model.predict(x)
+        dx['Change_Pav_Eng_to_Mkbl_value'] = pav_eng_to_mkbl_model.predict(x)
+        dx['Change_Blk_Eng_to_Grd_value'] = blk_eng_to_grade_model.predict(x)
+        dx['Change_Wht_Eng_to_Grd_value'] = wht_eng_to_grade_model.predict(x)
+        dx['Change_Open_Eng_to_Grd_value'] = open_eng_to_grade_model.predict(x)
+        dx['Change_Pav_Eng_to_Grd_value'] = pav_eng_to_grade_model.predict(x)
+        dx['Change_Blk_Eng_to_ByGrd_value'] = blk_eng_to_bygrade_model.predict(x)
+        dx['Change_Wht_Eng_to_ByGrd_value'] = wht_eng_to_bygrade_model.predict(x)
+        dx['Change_Open_Eng_to_ByGrd_value'] = open_eng_to_bygrade_model.predict(x)
+        dx['Change_Pav_Eng_to_ByGrd_value'] = pav_eng_to_bygrade_model.predict(x)
+        dx['Change_Blk_Eng_to_Gia_value'] = blk_eng_to_gia_model.predict(x)
+        dx['Change_Wht_Eng_to_Gia_value'] = wht_eng_to_gia_model.predict(x)
+        dx['Change_Open_Eng_to_Gia_value'] = open_eng_to_gia_model.predict(x)
+        dx['Change_Pav_Eng_to_Gia_value'] = pav_eng_to_gia_model.predict(x)
         
         # Inverse transform classification predictions.
         dx['col_change'] = loaded_label_encoder['Change_color_value'].inverse_transform(dx['col_change'])