Update app.py

app.py

@@ -12,34 +12,27 @@ from gradio_client import Client, handle_file
 from huggingface_hub import login
 from gradio_imageslider import ImageSlider
 
-
 MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
-
+client = AsyncInferenceClient()
 
 def enable_lora(lora_add, basemodel):
-    """Habilita o deshabilita LoRA según la opción seleccionada"""
     return basemodel if not lora_add else lora_add
 
-
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    """Genera una imagen utilizando el modelo seleccionado"""
     try:
         if seed == -1:
             seed = random.randint(0, MAX_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)
         return image, seed
     except Exception as e:
         print(f"Error generando imagen: {e}")
-        return None, None
-
+        return f"Error al generar imagen: {e}", None
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
-    """Escala una imagen utilizando FineGrain"""
     try:
         client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
         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")
@@ -48,13 +41,12 @@ def get_upscale_finegrain(prompt, img_path, upscale_factor):
         print(f"Error escalando imagen: {e}")
         return None
 
-
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
-    """Función principal que genera y escala la imagen"""
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
+    
     if image is None:
-        return [None, None]
+        return [f"Error generando imagen con el modelo {model}", None]
     
     image_path = "temp_image.jpg"
     image.save(image_path, format="JPEG")
@@ -71,12 +63,10 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     else:
         return [image_path, image_path]
 
-
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
 
-
 with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
     with gr.Column(elem_id="col-container"):
         with gr.Row():
@@ -98,5 +88,5 @@ with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
                     seed = gr.Number(label="Semilla", value=-1)
     
                 btn = gr.Button("Generar")
-                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,)
+                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()