Update app.py

app.py

@@ -2,11 +2,10 @@ import os
 import gradio as gr
 import numpy as np
 import random
-from huggingface_hub import AsyncInferenceClient
+from huggingface_hub import InferenceClient  # Replaced AsyncInferenceClient with InferenceClient
 from translatepy import Translator
 import requests
 import re
-import asyncio
 from PIL import Image
 from gradio_client import Client, handle_file
 from huggingface_hub import login
@@ -18,19 +17,19 @@ MAX_SEED = np.iinfo(np.int32).max
 def enable_lora(lora_add, basemodel):
     return basemodel if not lora_add else lora_add
 
-async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
+def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
     try:
         if seed == -1:
             seed = random.randint(0, MAX_SEED)
             print(seed)
         seed = int(seed)
-      
+        
         text = str(Translator().translate(prompt, 'English')) + "," + lora_word
-        client = AsyncInferenceClient()
-        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
+        client = InferenceClient()  # Using synchronous client instead of async
+        image = client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
         return image, seed
     except Exception as e:
-        print(f"Error generando imagen: {e}")
+        print(f"Error generating image: {e}")
         return None, None
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
@@ -39,13 +38,13 @@ def get_upscale_finegrain(prompt, img_path, upscale_factor):
         result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
         return result[1]
     except Exception as e:
-        print(f"Error escalando imagen: {e}")
+        print(f"Error upscaling image: {e}")
         return None
 
-async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
 
-    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
+    image, seed = generate_image(prompt, model, "", width, height, scales, steps, seed)
     if image is None:
         return [None, None]
     
@@ -90,4 +89,4 @@ with gr.Blocks(css=css) as demo:
     
                 btn = gr.Button("Generate")
                 btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
-    demo.launch()
+    demo.launch()