# import gradio as gr
# import numpy as np
# import random
# #import spaces #[uncomment to use ZeroGPU]
# from diffusers import DiffusionPipeline
# import torch
#
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
#
# if torch.cuda.is_available():
#     torch_dtype = torch.float16
# else:
#     torch_dtype = torch.float32
#
# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)
#
# MAX_SEED = np.iinfo(np.int32).max
# MAX_IMAGE_SIZE = 1024
#
# #@spaces.GPU #[uncomment to use ZeroGPU]
# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
#
#     if randomize_seed:
#         seed = random.randint(0, MAX_SEED)
#
#     generator = torch.Generator().manual_seed(seed)
#
#     image = 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]
#
#     return image, seed
#
# examples = [
#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
#     "An astronaut riding a green horse",
#     "A delicious ceviche cheesecake slice",
# ]
#
# css="""
# #col-container {
#     margin: 0 auto;
#     max-width: 640px;
# }
# """
#
# with gr.Blocks(css=css) as demo:
#
#     with gr.Column(elem_id="col-container"):
#         gr.Markdown(f"""
#         # Text-to-Image Gradio Template
#         """)
#
#         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="Result", 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=False,
#             )
#
#             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, #Replace with defaults that work for your model
#                 )
#
#                 height = gr.Slider(
#                     label="Height",
#                     minimum=256,
#                     maximum=MAX_IMAGE_SIZE,
#                     step=32,
#                     value=1024, #Replace with defaults that work for your model
#                 )
#
#             with gr.Row():
#
#                 guidance_scale = gr.Slider(
#                     label="Guidance scale",
#                     minimum=0.0,
#                     maximum=10.0,
#                     step=0.1,
#                     value=0.0, #Replace with defaults that work for your model
#                 )
#
#                 num_inference_steps = gr.Slider(
#                     label="Number of inference steps",
#                     minimum=1,
#                     maximum=50,
#                     step=1,
#                     value=2, #Replace with defaults that work for your model
#                 )
#
#         gr.Examples(
#             examples = examples,
#             inputs = [prompt]
#         )
#     gr.on(
#         triggers=[run_button.click, prompt.submit],
#         fn = infer,
#         inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
#         outputs = [result, seed]
#     )
#
# demo.queue().launch()

import gradio as gr
gr.load("models/nerijs/dark-fantasy-illustration-flux").launch()