# 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
# 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


# 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):
#     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)
#         return image, seed
#     except Exception as e:
#         print(f"Error generando imagen: {e}")
#         return None, None

# def get_upscale_finegrain(prompt, img_path, upscale_factor):
#     try:
#         client = Client("finegrain/finegrain-image-enhancer") 
#         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):
#     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]
    
#     image_path = "temp_image.jpg"
#     image.save(image_path, format="JPEG")
    
#     if process_upscale:
#         upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
#         if upscale_image_path is not None:
#             upscale_image = Image.open(upscale_image_path)
#             upscale_image.save("upscale_image.jpg", format="JPEG")
#             return [image_path, "upscale_image.jpg"]
#         else:
#             print("Error: The scaled image path is None")
#             return [image_path, image_path]
#     else:
#         return [image_path, image_path]

# css = """
# #col-container{ margin: 0 auto; max-width: 1024px;}
# """

# with gr.Blocks(css=css) as demo:
#     with gr.Column(elem_id="col-container"):
#         with gr.Row():
#             with gr.Column(scale=3):
#                 output_res = ImageSlider(label="Flux / Upscaled")
#             with gr.Column(scale=2):
#                 prompt = gr.Textbox(label="Image Description")
#                 basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
#                 lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
#                 process_lora = gr.Checkbox(label="LoRA Process")
#                 process_upscale = gr.Checkbox(label="Scale Process")
#                 upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)
                
#                 with gr.Accordion(label="Advanced Options", open=False):
#                     width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280)
#                     height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768)
#                     scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
#                     steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
#                     seed = gr.Number(label="Seed", value=-1)
    
#                 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()