Update app.py

app.py

@@ -12,43 +12,58 @@ from gradio_client import Client, handle_file
 from huggingface_hub import login
 from gradio_imageslider import ImageSlider
 
-translator = Translator()
+# Constantes
+MAX_SEED = np.iinfo(np.int32).max
 HF_TOKEN = os.environ.get("HF_TOKEN")
 HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")
-MAX_SEED = np.iinfo(np.int32).max
-CSS = "footer { visibility: hidden; }"
-JS = "function () { gradioURL = window.location.href; if (!gradioURL.endsWith('?__theme=dark')) { window.location.replace(gradioURL + '?__theme=dark'); } }"
 
-def enable_lora(lora_add, basemodel): 
+# Funciones
+def enable_lora(lora_add, basemodel):
+    """Habilita o deshabilita el modelo LORA"""
     return basemodel if not lora_add else lora_add
 
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    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
+    """Genera una imagen a partir de un texto"""
+    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 generating image: {e}")
+        return None, None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
+    """Genera una imagen y la ajusta"""
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
-    image_path = "temp_image.jpg"  
-    image.save(image_path, format="JPEG")  
+    if image is None:
+        return [None, None]
+    
+    image_path = "temp_image.jpg"
+    image.save(image_path, format="JPEG")
     
     if process_upscale:
         upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
     else:
         upscale_image = image_path
-
+    
     return [image_path, upscale_image]
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
-    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")
-    return result[1]
+    """Ajusta la imagen"""
+    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")
+        return result[1]
+    except Exception as e:
+        print(f"Error upscale image: {e}")
+        return None
 
+# Interfaz gráfica
 css = """
 #col-container{
     margin: 0 auto;
@@ -56,13 +71,13 @@ css = """
 }
 """
 
-with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
+with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown("Flux Upscaled +LORA")
         with gr.Row():
-            with gr.Column(scale=1.5):
+            with gr.Column(scale=3):
                 output_res = ImageSlider(label="Flux / Upscaled")
-            with gr.Column(scale=0.8):
+            with gr.Column(scale=2):
                 prompt = gr.Textbox(label="Prompt")
                 basemodel_choice = gr.Dropdown(label="Base Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
                 lora_model_choice = gr.Dropdown(label="LORA Model", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
@@ -76,7 +91,7 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                     scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5)
                     steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24)
                     seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
-
+            
                 submit_btn = gr.Button("Submit", scale=1)
                 submit_btn.click(
                     fn=lambda: None,
@@ -87,4 +102,6 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
                     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()