import gradio as gr
import numpy as np
import random
import spaces
import torch
import os
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Include your Hugging Face access token
hf_token = os.getenv("waffles")

# Load the diffusion pipeline with the access token
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=hf_token).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU(duration=190)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, num_images=1, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    images = []
    for _ in range(num_images):
        image = pipe(
            prompt=prompt, 
            width=width,
            height=height,
            num_inference_steps=num_inference_steps, 
            generator=generator,
            guidance_scale=guidance_scale
        ).images[0]
        images.append(image)
    
    return images, seed
 
examples = [
    "a white husky knocking everything down in a living room",
    "a tuxedo cat with a waffle in her mouth",
    "an anime Chiweenie Dog wearing a hoodie",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)
        
        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.Gallery(label="Result", show_label=False)

        # Display all settings directly without the Accordion
        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():

            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=1,
                maximum=15,
                step=0.1,
                value=5.0,
            )

            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=50,
                step=1,
                value=28,
            )
        
        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, guidance_scale, num_inference_steps, num_images],
        outputs=[result, seed]
    )

demo.launch()