import gradio as gr
import torch
import spaces
from diffusers import FluxPipeline, FluxTransformer2DModel
from PIL import Image
from diffusers.utils import export_to_gif
import uuid

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

if torch.cuda.is_available():
    torch_dtype = torch.bfloat16
else:
    torch_dtype = torch.float32

def split_image(input_image, num_splits=4):
    # Create a list to store the output images
    output_images = []

    # Split the image into four 256x256 sections
    for i in range(num_splits):
        left = i * 256
        right = (i + 1) * 256
        box = (left, 0, right, 256)
        output_images.append(input_image.crop(box))

    return output_images

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch_dtype
)
pipe.to(device)

@spaces.GPU 
def infer(prompt, seed, randomize_seed, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
    prompt_template = f"A side by side 4 frame image showing consecutive stills from a looped gif moving from left to right. The gif is {prompt}"
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt=prompt,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=1,
        generator=torch.Generator(device).manual_seed(seed),
        height=height,
        width=width
    ).images[0]
    gif_name = f"{uuid.uuid4().hex}-flux.gif"
    export_to_gif(split_image(image, 4), gif_name, fps=4)
    return gif_name, seed

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # FLUX.1 Schnell Animations
        Generate gifs with 
        """)
        
        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.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)

            num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=12,
                    step=1,
                    value=4, 
            )    
                
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )
    gr.on(
        trigger=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.queue().launch()