from diffusers import DDPMPipeline
import torch
import PIL.Image
import gradio as gr
import random
import numpy as np

ddpm_pipeline = DDPMPipeline.from_pretrained("nateraw/my-aurora")

def predict(seed=42):
    generator = torch.manual_seed(seed)
    images = ddpm_pipeline(generator=generator)["sample"]
    return images[0]

random_seed = random.randint(0, 2147483647)
gr.Interface(
    predict,
    inputs=[
        gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1),
    ],
    outputs=gr.Image(shape=[32,32], type="pil", elem_id="output_image"),
    title="Generate aurora with diffusers!",
    description="This demo the <a href=\"https://huggingface.co/nateraw/my-aurora\">my-aurora</a> model to generate aurora created by <a href=\"https://huggingface.co/nateraw\">nateraw</a> using the <a href=\"https://github.com/osanseviero/diffuse-it\">Diffuse It! tool</a>. Inference might take around a minute.",
).launch(debug=True, enable_queue=True)