Update app.py

app.py

@@ -275,42 +275,52 @@ def process_dataframe(df):
         # -------------------------
         # Prediction Report Section
         # -------------------------
-        x = df_pred.copy()
-        df_pred['GIA_Predicted'] = gia_model.predict(x)
-        df_pred['Grade_Predicted'] = grade_model.predict(x)
-        df_pred['ByGrade_Predicted'] = bygrade_model.predict(x)
-        df_pred['Makable_Predicted'] = makable_model.predict(x)
-        df_pred['GIA_Diff'] = df_pred['EngAmt'] - df_pred['GIA_Predicted']
-        df_pred['Grade_Diff'] = df_pred['EngAmt'] - df_pred['Grade_Predicted']
-        df_pred['ByGrade_Diff'] = df_pred['EngAmt'] - df_pred['ByGrade_Predicted']
-        df_pred['Makable_Diff'] = df_pred['EngAmt'] - df_pred['Makable_Predicted']
+        try:
+            x = df_pred.copy()
+            df_pred['GIA_Predicted'] = gia_model.predict(x)
+            df_pred['Grade_Predicted'] = grade_model.predict(x)
+            df_pred['ByGrade_Predicted'] = bygrade_model.predict(x)
+            df_pred['Makable_Predicted'] = makable_model.predict(x)
+            df_pred['GIA_Diff'] = df_pred['EngAmt'] - df_pred['GIA_Predicted']
+            df_pred['Grade_Diff'] = df_pred['EngAmt'] - df_pred['Grade_Predicted']
+            df_pred['ByGrade_Diff'] = df_pred['EngAmt'] - df_pred['ByGrade_Predicted']
+            df_pred['Makable_Diff'] = df_pred['EngAmt'] - df_pred['Makable_Predicted']
+        except ValueError as e:
+            print(f'pred model error----->: {e}', 'error')
         
         # -------------------------
         # Classification Report Section
         # -------------------------
-        x2 = df_class.copy()
-        dx = df_pred.copy()  # Start with the prediction data.
-        dx['col_change'] = col_model.predict(x2)
-        dx['cts_change'] = cts_model.predict(x2)
-        dx['cut_change'] = cut_model.predict(x2)
-        dx['qua_change'] = qua_model.predict(x2)
-        dx['shp_change'] = shp_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)
+        try:
+            x2 = df_class.copy()
+            dx = df_pred.copy()  # Start with the prediction data.
+            dx['col_change'] = col_model.predict(x2)
+            dx['cts_change'] = cts_model.predict(x2)
+            dx['cut_change'] = cut_model.predict(x2)
+            dx['qua_change'] = qua_model.predict(x2)
+            dx['shp_change'] = shp_model.predict(x2)
+        except ValueError as e:
+            print(f'class model error----->: {e}', 'error')
+
+        try:
+            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)
+        except ValueError as e:
+            print(f'grade_code model error----->: {e}', 'error')
         
         # Inverse transform classification predictions.
         dx['col_change'] = loaded_label_encoder['Change_color_value'].inverse_transform(dx['col_change'])