Update app.py

app.py

@@ -4,6 +4,10 @@ import random
 import spaces
 import torch
 from diffusers import DiffusionPipeline
+from transformers import pipeline
+
+# 번역 파이프라인 초기화
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -18,6 +22,14 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
+
+    # 한글 입력 감지 및 번역
+    if any('\uAC00' <= char <= '\uD7A3' for char in prompt):
+        print("Translating Korean prompt...")
+        translated_prompt = translator(prompt, max_length=512)[0]['translation_text']
+        print("Translated prompt:", translated_prompt)
+        prompt = translated_prompt
+
     image = pipe(
             prompt = prompt,
             width = width,
@@ -45,12 +57,8 @@ footer {
 }
 """
 
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css
-) as demo:
-
+with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
     with gr.Column(elem_id="col-container"):
-
-
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -103,20 +111,18 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css
                 )
 
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
-        gr.HTML(footer)
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs = [result, seed]
-    )
+        gr.on(
+            triggers=[run_button.click, prompt.submit],
+            fn=infer,
+            inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
+            outputs=[result, seed]
+        )
 
 demo.launch()