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:
@@ -43,8 +43,9 @@ arg3 = config.get('arg3', 'test_fleet')
 arg4 = config.get('arg4', '1') 
 arg5 = config.get('arg5', '1')
 arg6 = config.get('arg6', 'saved_fleet_model')
+arg7 = config.get('arg7', 'Model')
 
-if num_args == 6:    
+if num_args == 7:    
     # cmd args
     # sys.argv[0] is the script name, sys.argv[1] is the first argument, etc.
     should_train_model = arg1  # should train model?
@@ -352,6 +353,35 @@ label_mapping = model.config.label_mapping
 label_mapping_reverse = {value: key for key, value in label_mapping.items()}
 
 
+
+# fetch data
+def fetch_and_update_training_data(arg2):
+    model_name = f"{arg7}"
+    export_type = "sentence"
+    
+    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(train_file + ".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(train_file + ".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
+
+
 #Function to classify user input
 def classify_user_input():
     while True: