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

# Set the device based on availability
device = "cuda" if torch.cuda.is_available() else "cpu"

# Use the ByteDance/AnimateDiff-Lightning model
model_repo_id = "ByteDance/AnimateDiff-Lightning" 

# Set the torch dtype based on available hardware
if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

# Load the pipeline from the pretrained model repository
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

# Maximum values for seed and image size
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# Define the inference function
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):

    # Randomize seed if the checkbox is selected
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device=device).manual_seed(seed)

    # Generate the animation using the pipeline
    animation = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height,
        generator=generator
    ).images[0]  # Assuming the model generates images in the `.images` property
    
    return animation, seed

# Sample prompts for the UI
examples = [
    "A cat playing with a ball in a garden",
    "A dancing astronaut in space",
    "A flying dragon in the sky at sunset",
]

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

# Build the Gradio UI
with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # AnimateDiff Lightning Model Text-to-Animation
        """)
        
        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="Generated Animation", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )
            
            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():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=30,
                )

        # Example prompts for user selection
        gr.Examples(
            examples=examples,
            inputs=[prompt]
        )

    # Create an API endpoint for the model
    demo.api(fn=infer, inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs=[result, seed])
    demo.launch()