Update app.py

app.py

@@ -45,6 +45,37 @@ arg5 = config.get('arg5', '1')
 arg6 = config.get('arg6', 'saved_fleet_model')
 arg7 = config.get('arg7', 'Model')
 
+# create function for fetch data
+def fetch_and_update_training_data():
+    model_name = f"{arg7}"
+    export_type = "sentence"
+
+    print(arg2)
+    print(arg7)
+    
+    request_url = f"http://20.247.235.135/AiBot/BE/api/PromptEnhancer/ExportPEList?modelName={model_name}&exportType={export_type}"
+
+    response = requests.get(request_url)
+
+    if response.status_code == 200:
+        try:
+            # Save response as CSV
+            with open(arg2 + ".csv", "w", encoding="utf-8") as file:
+                file.write(response.text)
+
+            print("Training file updated successfully.")
+
+            # Read the updated CSV into a DataFrame
+            return pd.read_csv(arg2 + ".csv")
+
+        except Exception as e:
+            print(f"Error processing the response: {e}")
+            return None
+    else:
+        print(f"Error fetching data: {response.status_code}")
+        return None
+        
+
 if num_args == 7:    
     # cmd args
     # sys.argv[0] is the script name, sys.argv[1] is the first argument, etc.
@@ -81,11 +112,9 @@ if (should_train_model=='1'): #train model
     file_path_train = train_file + ".csv"
     file_path_test = test_file + ".csv"
 
-
-    file_train_df = fetch_and_update_training_data()
-
     # Read the CSV files into pandas DataFrames they will later by converted to DataTables and used to train and evaluate the model
     #file_train_df = pd.read_csv(file_path_train)
+    
     file_train_df = fetch_and_update_training_data()
     file_test_df = pd.read_csv(file_path_test)
 
@@ -338,38 +367,7 @@ label_mapping_reverse = {value: key for key, value in label_mapping.items()}
 
 
 
-# create function for fetch data
-def fetch_and_update_training_data():
-    model_name = f"{arg7}"
-    export_type = "sentence"
-
-    print(arg2)
-    print(arg7)
-    
-    request_url = f"http://20.247.235.135/AiBot/BE/api/PromptEnhancer/ExportPEList?modelName={model_name}&exportType={export_type}"
-
-    response = requests.get(request_url)
-
-    if response.status_code == 200:
-        try:
-            # Save response as CSV
-            with open(arg2 + ".csv", "w", encoding="utf-8") as file:
-                file.write(response.text)
-
-            print("Training file updated successfully.")
-
-            # Read the updated CSV into a DataFrame
-            return pd.read_csv(arg2 + ".csv")
 
-        except Exception as e:
-            print(f"Error processing the response: {e}")
-            return None
-    else:
-        print(f"Error fetching data: {response.status_code}")
-        return None
-        
-# run the function     
-fetch_and_update_training_data()
 
 #Function to classify user input
 def classify_user_input():