import spaces
import torch
from diffusers import FluxPipeline
import gradio as gr
import random
import numpy as np
import os

#from huggingface_hub import login


if torch.cuda.is_available():
    device = "cuda"
    print("Using GPU")
else:
    device = "cpu"
    print("Using CPU")


# login hf token
HF_TOKEN = os.getenv("HF_TOKEN")
#login(token=HF_TOKEN)


MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"

# Initialize the pipeline and download the model
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.to(device)

# Enable memory optimizations
pipe.enable_attention_slicing()


# Define the image generation function
@spaces.GPU(duration=180)
def generate_image(promptx, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
    if seed == 0:
        seed = random.randint(1, MAX_SEED)

    generato = torch.Generator().manual_seed(seed)
    
    
    with torch.inference_mode():
        out = pipe(
            prompt=promptx,
            num_inference_steps=num_inference_steps,
            height=height,
            width=width,
            guidance_scale=guidance_scale,
            generator=generato,
            num_images_per_prompt=num_images_per_prompt
        ).images
    
    return out



# Create the Gradio interface

examples = [
    ["Full-body, realistic photo of a network engineer in a data center, conducting an experiment"]
]

css = '''
.gradio-container{max-width: 100% !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as fluxobj:
    with gr.Row():
        with gr.Column():
            gr.Markdown(
            """ # FLUX.1-dev
            """
        )
            gr.Markdown(
                """
                Made by csit.udru.ac.th for non-commercial license
                """
        )
    with gr.Group():
        with gr.Row():
            promptx = gr.Textbox(label="", show_label=False, info="", placeholder="Describe the image you want")
            run_button = gr.Button("Generate", scale=0)
        resultf = gr.Gallery(label="Generated AI Images", elem_id="gallery")
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1)
            guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=7.0, value=3.5, step=0.1)
        with gr.Row():
            width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1024, step=32, value=1024)
            height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1024, step=32, value=1024)
        with gr.Row():
            seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
            num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=1)

    # gr.Examples(
    #     examples=examples,
    #     fn=generate_image,
    #     inputs=[promptx, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
    #     outputs=[resultf],
    #     cache_examples=CACHE_EXAMPLES
    # )

    gr.on(
        triggers=[
            promptx.submit,
            run_button.click,
        ],
        fn=generate_image,
        inputs=[promptx, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
        outputs=[resultf],
    )
if __name__ == "__main__":
   fluxobj.queue(max_size=20).launch()