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

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

# Initialize the AuraFlow v0.3 pipeline
pipe = AuraFlowPipeline.from_pretrained(
    "fal/AuraFlow-v0.3",
    torch_dtype=torch.float16
).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

@spaces.GPU
def infer(prompt,
          negative_prompt="",
          seed=42,
          randomize_seed=False,
          width=1024,
          height=1024,
          guidance_scale=5.0,
          num_inference_steps=28,
          progress=gr.Progress(track_tqdm=True)):
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device=device).manual_seed(seed)
    
    image = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        generator=generator
    ).images[0]
        
    return image, seed

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

with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:

    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Text(label="Prompt", placeholder="Enter your prompt")
            negative_prompt = gr.Text(label="Negative prompt", placeholder="Enter a negative prompt")
            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            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)
            guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0)
            num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
            run_button = gr.Button("Generate")

        with gr.Column(scale=1):
            result = gr.Image(label="Generated Image")
            seed_output = gr.Number(label="Seed used")

    run_button.click(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed_output]
    )

    gr.Examples(
        examples=[
            "A photo of a lavender cat",
            "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
            "An astronaut riding a green horse",
            "A delicious ceviche cheesecake slice",
        ],
        inputs=prompt,
    )

demo.queue().launch(server_name="0.0.0.0", share=False)