Update app.py

app.py

@@ -77,11 +77,22 @@ if (should_train_model=='1'): #train model
 
     # Read the CSV files into pandas DataFrames they will later by converted to DataTables and used to train and evaluate the model
     
+    
+    # Check if the result is a non-empty DataFrame
+    file_train_df = fetch_and_update_training_data(file_path_train)
+
+    if file_train_df is not None and not file_train_df.empty:
+        file_train_df = fetch_and_update_training_data(file_path_train)
+        file_test_df = pd.read_csv(file_path_test)
+    else:
+        file_train_df = pd.read_csv(file_path_train)
+        file_test_df = pd.read_csv(file_path_test)
+    
     #file_train_df = fetch_and_update_training_data(file_path_train)
     #file_test_df = pd.read_csv(file_path_test)
 
-    file_train_df = pd.read_csv(file_path_train)
-    file_test_df = pd.read_csv(file_path_test)
+    #file_train_df = pd.read_csv(file_path_train)
+    #file_test_df = pd.read_csv(file_path_test)
     
     #combine dataframes to get all possible labels/classifications for both training and evaluating - to get all possible labels (intents)
     df = pd.concat([file_train_df, file_test_df], ignore_index=True)