import gradio as gr
import requests
import io
import random
import os
import time
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):
    if not prompt:
        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
    }

    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;
}

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

button {
    background-color: #f5f5f5; /* Fondo gris claro */
    color: #333333; /* Texto gris oscuro */
    border: 2px solid black; /* Borde negro en botones */
}

button.primary {
    background-color: green; /* Fondo verde para el botón 'Generate' */
    color: white; /* Texto blanco en el botón 'Generate' */
}

button.secondary {
    background-color: #f5f5f5; /* Fondo gris claro para el botón 'Clear' */
    color: #333333; /* Texto gris oscuro en el botón 'Clear' */
}

button:hover {
    background-color: #e0e0e0; /* Fondo gris más oscuro en hover */
}

h1, h2, h3, h4, h5, h6 {
    color: #333333; /* Texto gris oscuro en encabezados */
}

@media (max-width: 768px) {
    .button-row {
        display: flex;
        flex-direction: column;
        gap: 10px;
    }
}
"""

with gr.Blocks(css=css) as app:
    gr.HTML("""
    <center>
        <h1>Dream Generator with Flux</h1>
        <h2>Transforma tus sueños en imágenes vibrantes con un solo clic.</h2>
    </center>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", 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="Sampling steps", value=35, minimum=1, maximum=100, step=1)
            cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)

            with gr.Row(elem_id="button-row"):
                clear_button = gr.Button("Clear", elem_id="clear-button", variant="secondary")
                generate_button = gr.Button("Generate", elem_id="generate-button", variant="primary")

        with gr.Column(scale=1):
            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")

    generate_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
    
    def clear_prompt():
        return gr.Textbox.update(value="")
    
    clear_button.click(clear_prompt, inputs=[], outputs=text_prompt)

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