app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
from transformers import pipeline

# Translation pipeline and hardware settings
device = "cuda" if torch.cuda.is_available() else "cpu"
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=device)
dtype = torch.bfloat16
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    # Korean input detection and translation
    if any('\uAC00' <= char <= '\uD7A3' for char in prompt):
        print("Translating Korean prompt...")
        translated_prompt = translator(prompt, max_length=512)[0]['translation_text']
        print("Translated prompt:", translated_prompt)
        prompt = translated_prompt
        
    image = pipe(
            prompt = prompt,
            width = width,
            height = height,
            num_inference_steps = num_inference_steps,
            generator = generator,
            guidance_scale=0.0
    ).images[0]
    
    return image, seed

examples = [
    ["[한글] [스타일: 모던] [색상: 빨강과 검정] [컨셉: 식당] [텍스트: '맛있는집'] [배경: 심플]"],
    ["[Style: Corporate] [Color: Navy and Silver] [Concept: Finance] [Text: 'TRUST'] [Background: Professional]"],
    ["[Style: Dynamic] [Color: Purple and Orange] [Concept: Creative Agency] [Text: 'SPARK'] [Background: Abstract]"],
    ["[Style: Minimalist] [Color: Red and White] [Concept: Sports] [Text: 'POWER'] [Background: Clean]"]
]

css = """
footer {visibility: hidden}
.container {max-width: 850px; margin: auto; padding: 20px}
.title {text-align: center; margin-bottom: 20px}
#prompt {min-height: 50px}
#result {min-height: 400px}
.gr-box {border-radius: 10px; border: 1px solid #ddd}
"""


def create_snow_effect():
    # CSS 스타일 정의
    snow_css = """
    @keyframes snowfall {
        0% {
            transform: translateY(-10vh) translateX(0);
            opacity: 1;
        }
        100% {
            transform: translateY(100vh) translateX(100px);
            opacity: 0.3;
        }
    }
    .snowflake {
        position: fixed;
        color: white;
        font-size: 1.5em;
        user-select: none;
        z-index: 1000;
        pointer-events: none;
        animation: snowfall linear infinite;
    }
    """

    # JavaScript 코드 정의
    snow_js = """
    function createSnowflake() {
        const snowflake = document.createElement('div');
        snowflake.innerHTML = '❄';
        snowflake.className = 'snowflake';
        snowflake.style.left = Math.random() * 100 + 'vw';
        snowflake.style.animationDuration = Math.random() * 3 + 2 + 's';
        snowflake.style.opacity = Math.random();
        document.body.appendChild(snowflake);
        
        setTimeout(() => {
            snowflake.remove();
        }, 5000);
    }
    setInterval(createSnowflake, 200);
    """

    # CSS와 JavaScript를 결합한 HTML 
    snow_html = f"""
    <style>
        {snow_css}
    </style>
    <script>
        {snow_js}
    </script>
    """
    
    return gr.HTML(snow_html)


with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
    create_snow_effect()        
    gr.HTML("<h1 class='title'>LOGO Generator AI</h1>")
    
    with gr.Column(elem_id="container"):
        with gr.Group():
            prompt = gr.Text(
                label="PROMPT",
                placeholder="Text input Prompt (Korean input supported)",
                lines=2
            )
            run_button = gr.Button("Generate Logo", variant="primary")
        
        with gr.Row():
            result = gr.Image(label="Generated Logo", show_label=True)
        
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Row():
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                randomize_seed = gr.Checkbox(label="Random Seed", value=True)
            
            with gr.Row():
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
                num_inference_steps = gr.Slider(label="Quality", minimum=1, maximum=50, step=1, value=4)
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result, seed],
            cache_examples="lazy"
        )
        
        gr.on(
            triggers=[run_button.click, prompt.submit],
            fn=infer,
            inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
            outputs=[result, seed]
        )

demo.launch()