import gradio as gr
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from PIL import Image

from model import *

# SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"

# Constants
# base = "stabilityai/stable-diffusion-xl-base-1.0"
# repo = "ByteDance/SDXL-Lightning"
# checkpoints = {
#     "1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
#     "2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
#     "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
#     "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
# }
# loaded = None


# Ensure model and scheduler are initialized in GPU-enabled function
# if torch.cuda.is_available():
#     pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")

    
# Function 
# @spaces.GPU(enable_queue=True)

def generate_image(prompt):
    
    return prompt_to_img(prompt)[0]



# Gradio Interface
description = """
This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-fast text-to-image generative model capable of producing high-quality images in 4 steps.
As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
"""

with gr.Blocks(css="style.css") as demo:
    gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
    gr.Markdown(description)
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
            submit = gr.Button(scale=1, variant='primary')
    img = gr.Image(label='SDXL-Lightning Generated Image')

    prompt.submit(fn=generate_image,
                 inputs=[prompt],
                 outputs=img,
                 )
    submit.click(fn=generate_image,
                 inputs=[prompt],
                 outputs=img,
                 )
    
demo.queue().launch()