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

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)
    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
 

def create_flux_tab(image_input):
    examples = [
        "a tiny astronaut hatching from an egg on the moon",
        "a cat holding a sign that says hello world",
        "an anime illustration of a wiener schnitzel",
    ]
    
    css="""
    #col-container {
        margin: 0 auto;
        max-width: 520px;
    }
    """
    
    with gr.Blocks(css=css) as demo:
        
        with gr.Column(elem_id="col-container"):
            gr.Markdown(f"""# FLUX.1 [schnell]""")
            
            with gr.Row():
                
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt",
                    container=False,
                )
                
                run_button = gr.Button("Run", scale=0)
            
            result = gr.Image(label="Result", show_label=False

             with gr.Row():
                use_in_text2lipsync_button = gr.Button("이 이미지를 Txt to Lipsync 탭에서 사용하기")  # 새로운 버튼 추가
            
            
            with gr.Accordion("Advanced Settings", open=False):
                
                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=1024,
                    )
                    
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=1024,
                    )
                
                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]
        )
        # 새로운 버튼 클릭 이벤트 정의
        use_in_text2lipsync_button.click(
            fn=lambda img: img,  # 간단한 람다 함수를 사용하여 이미지를 그대로 전달
            inputs=[result],  # 생성된 이미지를 입력으로 사용
            outputs=[image_input]  # Text to LipSync 탭의 image_input을 업데이트
        
    return flux_demo

# demo.launch()