Update app.py

app.py

@@ -8,7 +8,6 @@ import requests
 import re
 import asyncio
 from PIL import Image
-from glob import glob
 
 translator = Translator()
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
@@ -29,7 +28,7 @@ JS = """function () {
   }
 }"""
 
-client = AsyncInferenceClient()
+
 
 def enable_lora(lora_in, lora_add):
     if not lora_in and not lora_add:
@@ -39,12 +38,6 @@ def enable_lora(lora_in, lora_add):
             lora_in = lora_add
         return lora_in
 
-def imagename():
-    os.makedirs("output", exist_ok=True)
-    base_count = len(glob(os.path.join("output", "*.webp")))
-    image_path = os.path.join("output", f"{base_count:06d}.webp")
-    return image_path
-
 async def generate_image(
     prompt:str,
     model:str,
@@ -61,9 +54,9 @@ async def generate_image(
     
     text = str(translator.translate(prompt, 'English'))
 
-    #generator = torch.Generator().manual_seed(seed)
- 
-    image1 = await client.text_to_image(
+    client1 = AsyncInferenceClient(basemodel)
+    
+    image1 = await client1.text_to_image(
         prompt=text,
         height=height,
         width=width,
@@ -71,10 +64,11 @@ async def generate_image(
         num_inference_steps=steps,
         model=basemodel,
     )
-    image1=image1.save(imagename())
     print(image1)
-    
-    image2 = await client.text_to_image(
+
+    client2 = AsyncInferenceClient(model)
+
+    image2 = await client2.text_to_image(
         prompt=text,
         height=height,
         width=width,
@@ -82,8 +76,8 @@ async def generate_image(
         num_inference_steps=steps,
         model=model,
     )
-    image2=image2.save(imagename())
     print(image2)
+    
     return image1, image2, seed
 
 async def gen(