Update app.py

app.py

@@ -42,7 +42,6 @@ def get_upscale_finegrain(prompt, img_path, upscale_factor):
     except Exception as e:
         return None
 
-# Función para guardar el prompt
 def save_prompt(prompt_text, seed):
     try:
         prompt_file_path = DATA_PATH / f"prompt_{seed}.txt"
@@ -53,7 +52,6 @@ def save_prompt(prompt_text, seed):
         st.error(f"Error al guardar el prompt: {e}")
         return None
 
-# Función principal de generación de imágenes
 async 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
     improved_prompt = await improve_prompt(prompt)
@@ -90,7 +88,7 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
 
 async def improve_prompt(prompt):
     try:
-        instruction = ("With this idea, describe in English a detailed txt2img prompt...")
+        instruction = ("With this idea, describe in English a detailed txt2img prompt in 200 character at most...")
         formatted_prompt = f"{prompt}: {instruction}"
         response = llm_client.text_generation(formatted_prompt, max_new_tokens=200)
         improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip()
@@ -98,6 +96,15 @@ async def improve_prompt(prompt):
     except Exception as e:
         return f"Error mejorando el prompt: {e}"
 
+def save_image(image, seed):
+    try:
+        image_path = DATA_PATH / f"image_{seed}.jpg"
+        image.save(image_path, format="JPEG")
+        return image_path
+    except Exception as e:
+        st.error(f"Error al guardar la imagen: {e}")
+        return None
+
 def get_storage():
     files = [{"name": str(file.resolve()), "size": file.stat().st_size} for file in DATA_PATH.glob("*.jpg") if file.is_file()]
     usage = sum([f['size'] for f in files])
@@ -123,12 +130,10 @@ def run_gen():
     prompt_to_use = st.session_state.get('improved_prompt', prompt)
     return loop.run_until_complete(gen(prompt_to_use, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora))
 
-# Configuración de la página y sidebar
 st.set_page_config(layout="wide")
 st.title("Generador de Imágenes FLUX")
 prompt = st.sidebar.text_input("Descripción de la imagen", max_chars=200)
 
-# Opciones avanzadas
 with st.sidebar.expander("Opciones avanzadas", expanded=False):
     basemodel = st.selectbox("Modelo Base", ["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"])
     lora_model = st.selectbox("LORA Realismo", ["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"])