app.py

import gradio as gr
import requests
import io
import random
import os
from PIL import Image
from deep_translator import GoogleTranslator

# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
    if prompt == "" or prompt is None:
        return None

    key = random.randint(0, 999)
    
    # Detectar el idioma del prompt y traducirlo al inglés
    translator = GoogleTranslator(target='en')
    try:
        prompt = translator.translate(prompt)
    except Exception as e:
        print(f"Error during translation: {e}")
        return None

    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
    
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength,
        "width": width,
        "height": height
    }

    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Error: Failed to get image. Response status: {response.status_code}")
        print(f"Response content: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
        raise gr.Error(f"{response.status_code}")
    
    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
        return image
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None

css = """
#app-container {
    max-width: 600px;
    margin-left: auto;
    margin-right: auto;
    background-color: #ffffff; /* Fondo blanco */
    color: #333333; /* Texto gris oscuro */
}

input, textarea, select {
    background-color: #f5f5f5; /* Fondo gris claro para inputs */
    color: #333333; /* Texto gris oscuro en inputs */
    border: 1px solid #333333; /* Borde negro en inputs */
}

button {
    background-color: #28a745; /* Fondo verde */
    color: #ffffff; /* Texto blanco en botones */
    border: 1px solid #333333; /* Borde negro en botones */
    border-radius: 4px; /* Bordes redondeados */
}

button:hover {
    background-color: #218838; /* Verde oscuro en hover */
}

h1 {
    color: #333333; /* Texto gris oscuro en h1 */
}

h2 {
    color: #333333; /* Texto gris oscuro en h2 */
}
"""

with gr.Blocks(css=css) as app:
    gr.HTML("<center><h1>Generador de Sueños con Flux</h1></center>")
    gr.HTML("<center><h2>Transforma tus sueños en imágenes vibrantes con un solo clic.</h2></center>")
    with gr.Column(elem_id="app-container"):
        with gr.Row():
            with gr.Column():
                text_prompt = gr.Textbox(label="Prompt", placeholder="Introduce un prompt aquí", lines=2, elem_id="prompt-text-input")
                with gr.Accordion("Opciones avanzadas", open=False):
                    negative_prompt = gr.Textbox(label="Prompt Negativo", placeholder="Qué no debería aparecer en la imagen", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
                    steps = gr.Slider(label="Pasos de muestreo", value=35, minimum=1, maximum=100, step=1)
                    cfg = gr.Slider(label="Escala CFG", value=7, minimum=1, maximum=20, step=1)
                    method = gr.Radio(label="Método de muestreo", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
                    strength = gr.Slider(label="Fuerza", value=0.7, minimum=0, maximum=1, step=0.001)
                    seed = gr.Slider(label="Semilla", value=-1, minimum=-1, maximum=1000000000, step=1)
                    
                    image_size = gr.Dropdown(
                        label="Tamaño de Imagen",
                        choices=[
                            "4:3 (1024x768 px)",
                            "16:9 (1920x1080 px)",
                            "1:1 (1080x1080 px)",
                            "1:1 (500x500 px)",
                            "9:16 (720x1280 px)",
                            "9:16 (1080x1920 px)"
                        ],
                        value="16:9 (1920x1080 px)"
                    )

            with gr.Column():
                with gr.Row():
                    generate_button = gr.Button("Generar", elem_id="generate-button", variant="primary")
                with gr.Row():
                    image_output = gr.Image(type="pil", label="Imagen de Salida", elem_id="gallery")
        
        generate_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, image_size], outputs=image_output)

app.launch(show_api=False, share=False)