Create app-backup.py

app-backup.py

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+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+import os
+from diffusers import DiffusionPipeline
+from transformers import pipeline
+from huggingface_hub import login
+
+# Login to Hugging Face Hub with token
+# You need to set the HF_TOKEN environment variable
+hf_token = os.getenv("HF_TOKEN")
+if hf_token:
+    login(token=hf_token)
+else:
+    print("Warning: HF_TOKEN environment variable not found. Authentication may fail.")
+
+# 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
+
+# Load the model with token authentication
+pipe = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell", 
+    torch_dtype=dtype,
+    use_auth_token=hf_token  # Use the token for authentication
+).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="soft", 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()