Update app.py

app.py

@@ -1,4 +1,8 @@
 import os
+huggingface_token = os.getenv("HF_TOKEN")
+if not huggingface_token:
+    print("경고: Hugging Face 토큰이 설정되지 않았습니다.")
+    
 import gradio as gr
 import json
 import logging
@@ -20,12 +24,16 @@ import numpy as np
 import warnings
 
 
-huggingface_token = os.getenv("HF_TOKEN")
-
 
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")
 
 
+try:
+    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu", token=huggingface_token)
+except Exception as e:
+    print(f"번역기 로드 실패: {str(e)}")
+    # 번역 실패시 원본 텍스트를 그대로 반환하는 대체 함수
+    def translator(text, max_length=512):
+        return [{'translation_text': text}]
         
 #Load prompts for randomization
 df = pd.read_csv('prompts.csv', header=None)
@@ -366,12 +374,18 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
 
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
     try:
-        # 한글 감지 및 번역 (이 부분은 그대로 유지)
+        # 한글 감지 및 번역 부분
         if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
-            translated = translator(prompt, max_length=512)[0]['translation_text']
-            print(f"Original prompt: {prompt}")
-            print(f"Translated prompt: {translated}")
-            prompt = translated
+            try:
+                translated = translator(prompt, max_length=512)[0]['translation_text']
+                print(f"원본 프롬프트: {prompt}")
+                print(f"번역된 프롬프트: {translated}")
+                prompt = translated
+            except Exception as e:
+                print(f"번역 실패: {str(e)}")
+                # 번역 실패시 원본 프롬프트 사용
+
+
 
         if not selected_indices:
             raise gr.Error("You must select at least one LoRA before proceeding.")