import gradio as gr

gr.load("models/black-forest-labs/FLUX.1-schnell").launch(share=True)

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

# from transformers import pipeline

# pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")

# def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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, 
#             width = width,
#             height = height,
#             num_inference_steps = num_inference_steps, 
#             generator = generator,
#             guidance_scale=0.0
#     ).images[0] 
#     return image, seed

# with gr.Blocks(css=css) as demo:
    
#     with gr.Column(elem_id="col-container"):
#         gr.Markdown(f"""# FLUX.1 [schnell]
# 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
# [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
#         """)
        
#         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):
            
#             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():
                
  
#                 num_inference_steps = gr.Slider(
#                     label="Number of inference steps",
#                     minimum=1,
#                     maximum=50,
#                     step=1,
#                     value=4,
#                 )
        
#         gr.Examples(
#             examples = examples,
#             fn = infer,
#             inputs = [prompt],
#             outputs = [result, seed],
#             cache_examples="lazy"
#         )

#     gr.on(
#         triggers=[run_button.click, prompt.submit],
#         fn = infer,
#         inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
#         outputs = [result, seed]
#     )

# demo.launch()