import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline

#model_id = "lunarfish/furrydiffusion"
pipe = StableDiffusionPipeline.from_pretrained("lunarfish/furrydiffusion", torch_type=torch.float16, revision="main")

num_samples = 2

def infer(prompt):
    images = pipe([prompt] * num_samples, guidance_scale=7.5)["sample"]
    return images


block = gr.Blocks()

examples = [
    [
        'fox'
    ],
    [
        'rabbit'
    ],
    [
        'wolf'
    ],
]

with block as demo:
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):

                text = gr.Textbox(
                    label="Enter your prompt", show_label=False, max_lines=1
                ).style(
                    border=(True, False, True, True),
                    rounded=(True, False, False, True),
                    container=False,
                )
                btn = gr.Button("Run").style(
                    margin=False,
                    rounded=(False, True, True, False),
                )
               
        gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="generated_id").style(
            grid=[2], height="auto"
        )
        
        ex = gr.Examples(examples=examples, fn=infer, inputs=[text], outputs=gallery, cache_examples=True)
        ex.dataset.headers = [""]
        
        text.submit(infer, inputs=[text], outputs=gallery)
        btn.click(infer, inputs=[text], outputs=gallery)
demo.queue(max_size=25).launch()