app.py

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)
        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, 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", seed]
        else:
            print("Error: The scaled image path is None")
            return [image_path, image_path, seed]
    else:
        return [image_path, image_path, seed]

# Update the interface
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", "Shakker-Labs/FLUX.1-dev-LoRA-add-details"], value="black-forest-labs/FLUX.1-schnell")
                lora_model_choice = gr.Dropdown(label="LoRA", choices=["XLabs-AI/flux-RealismLora"], 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=1280)
                    scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=4)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=4)
                    seed = gr.Number(label="Seed", value=-1)
    
                btn = gr.Button("Generate")
                seed_output = gr.Textbox(label="Seed Value")  # Added Textbox for Seed Output
                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, seed_output],  # Updated outputs to include seed
                )
    demo.launch()