Update app.py

app.py

@@ -28,7 +28,7 @@ from huggingface_hub import HfApi, login, upload_folder, create_repo
 import os
 from flask import Flask, jsonify, request
 import requests
-
+from fetch_data import fetch_and_update_training_data
 
 # Load configuration file 
 with open('config.json', 'r') as config_file:
@@ -45,37 +45,6 @@ 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.
@@ -115,7 +84,7 @@ 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
     #file_train_df = pd.read_csv(file_path_train)
     
-    file_train_df = fetch_and_update_training_data()
+    file_train_df = fetch_and_update_training_data(file_path_train)
     file_test_df = pd.read_csv(file_path_test)