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

# 번역 파이프라인 초기화
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

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)

    # 한글 입력 감지 및 번역
    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 = [
    "Create a new logo for a tech startup",
    "Design an engaging Instagram post for a fashion brand",
    "Create a new character for a social media campaign",
    "Generate a marketing advertisement for a new product launch",
    "Design a social media banner for a charity event",
    "Create a new branding concept for a luxury hotel",
    "Design a promotional video thumbnail for a movie premiere",
    "Generate a marketing campaign for a sustainable lifestyle brand"
]

css = """
footer {
    visibility: hidden;
}
"""

with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
                elem_id="prompt"
            )

            run_button = gr.Button("Run", scale=0)

        result = gr.Image(label="Result", show_label=False, elem_id="result")

        with gr.Accordion("Advanced Settings", open=False, elem_id="advanced-settings"):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize 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,
                )

            with gr.Row():
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    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()