Update app.py

app.py

@@ -4,14 +4,15 @@ import torch
 from PIL import Image
 from diffusers import DiffusionPipeline
 import random
-from transformers import pipeline
+import re
+import googletrans  # Use googletrans instead of transformers pipeline
 
 torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 torch.backends.cuda.matmul.allow_tf32 = True
 
-# 번역 모델 초기화
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
+# Initialize translator with googletrans (more reliable than the failing model)
+translator = googletrans.Translator()
 
 base_model = "black-forest-labs/FLUX.1-dev"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
@@ -26,14 +27,22 @@ MAX_SEED = 2**32-1
 
 @spaces.GPU()
 def translate_and_generate(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-    # 한글 감지 및 번역
+    # Korean detection and translation
     def contains_korean(text):
-        return any(ord('가') <= ord(char) <= ord('힣') for char in text)
+        # Korean Unicode range check
+        return bool(re.search(r'[가-힣]', text))
     
     if contains_korean(prompt):
-        # 한글을 영어로 번역
-        translated = translator(prompt)[0]['translation_text']
-        actual_prompt = translated
+        try:
+            # Try to translate using googletrans
+            translated = translator.translate(prompt, src='ko', dest='en').text
+            actual_prompt = translated
+        except Exception as e:
+            # Fallback if translation fails
+            print(f"Translation error: {e}")
+            actual_prompt = prompt
+            # Add a note that we're proceeding with original text
+            print("Proceeding with original text without translation")
     else:
         actual_prompt = prompt