Update app.py

app.py

@@ -146,6 +146,7 @@ print("mkble_amt_class_model type:", type(mkble_amt_class_model))
     
 
 # Classification models loaded using joblib.
+'''
 col_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_col.joblib'))
 cts_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cts.joblib'))
 cut_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_cut.joblib'))
@@ -168,6 +169,8 @@ blk_eng_to_gia_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegr
 wht_eng_to_gia_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_gia_wht.joblib'))
 open_eng_to_gia_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_gia_open.joblib'))
 pav_eng_to_gia_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_gia_pav.joblib'))
+'''
+
 
 # List of label encoder names.
 encoder_list = [
@@ -322,6 +325,7 @@ def process_dataframe(df):
         # -------------------------
         # Classification Report Section
         # -------------------------
+        '''
         try:
             x2 = df_class.copy()
             dx = df_pred.copy()  # Start with the prediction data.
@@ -375,9 +379,11 @@ def process_dataframe(df):
         dx['Change_Wht_Eng_to_Gia_value'] = loaded_label_encoder['Change_Wht_Eng_to_Gia_value'].inverse_transform(dx['Change_Wht_Eng_to_Gia_value'])
         dx['Change_Open_Eng_to_Gia_value'] = loaded_label_encoder['Change_Open_Eng_to_Gia_value'].inverse_transform(dx['Change_Open_Eng_to_Gia_value'])
         dx['Change_Pav_Eng_to_Gia_value'] = loaded_label_encoder['Change_Pav_Eng_to_Gia_value'].inverse_transform(dx['Change_Pav_Eng_to_Gia_value'])
-        
+     
+        '''
+           
         # Final return with full data for pagination.
-        return df_pred, dx.head(len(df_pred))
+        return df_pred, df_pred
     except Exception as e:
         print(f'Error processing file: {e}', 'error')
         return pd.DataFrame(), pd.DataFrame()