Update app.py

app.py

@@ -2,20 +2,18 @@ import os
 import gradio as gr
 import numpy as np
 import random
-from huggingface_hub import AsyncInferenceClient
-from translatepy import Translator
-import requests
-import re
+from huggingface_hub import AsyncInferenceClient, InferenceClient
 import asyncio
 from PIL import Image
 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()
+llm_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
 def enable_lora(lora_add, basemodel):
     return basemodel if not lora_add else lora_add
@@ -25,11 +23,10 @@ async def generate_image(prompt, model, lora_word, width, height, scales, steps,
         if seed == -1:
             seed = random.randint(0, MAX_SEED)
         seed = int(seed)
-        text = str(Translator().translate(prompt, 'English')) + "," + lora_word
+        text = prompt + "," + lora_word
         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 f"Error al generar imagen: {e}", None
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
@@ -38,7 +35,6 @@ 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}")
         return None
 
 async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
@@ -58,11 +54,19 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
             upscale_image.save("upscale_image.jpg", format="JPEG")
             return [image_path, "upscale_image.jpg"]
         else:
-            print("Error: La ruta de la imagen escalada es None")
             return [image_path, image_path]
     else:
         return [image_path, image_path]
 
+def improve_prompt(prompt):
+    try:
+        instruction = "Mejora mi prompt para texto a imagen en inglés con estilo, cinematografía, cámaras, atmósfera e iluminación para la mejor calidad, de máximo 200 palabras."
+        formatted_prompt = f"{instruction}: {prompt}"
+        response = llm_client.text_generation(formatted_prompt, max_new_tokens=200)
+        return response.generated_text.strip()
+    except Exception as e:
+        return f"Error mejorando el prompt: {e}"
+
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
@@ -79,6 +83,11 @@ with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
                 process_lora = gr.Checkbox(label="Procesar LORA")
                 process_upscale = gr.Checkbox(label="Procesar Escalador")
                 upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)
+
+                improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False)
+
+                improve_btn = gr.Button("Mejora mi prompt")
+                improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
                 
                 with gr.Accordion(label="Opciones Avanzadas", open=False):
                     width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
@@ -88,5 +97,5 @@ 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)
-    demo.launch()
+                btn.click(fn=gen, inputs=[improved_prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res)
+    demo.launch()