import os
import gradio as gr
import numpy as np
import random
import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
from huggingface_hub import login

login(token=os.getenv('HF_TOKEN'))

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/stable-diffusion-xl-base-1.0" #Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

base = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
base = base.to(device)

base.load_lora_weights("kishlaykumar1995/blinky-flux-lora-32")

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1920

@spaces.GPU(duration=120) #[uncomment to use ZeroGPU]
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device=device).manual_seed(seed)

    # Generate the output image
    out = base(
        prompt=prompt,
        guidance_scale=guidance_scale,
        height=height,
        width=width,
        num_inference_steps=num_inference_steps,
        generator=generator,
        joint_attention_kwargs={"lora_scale": lora_scale}
    )

    
    return out.images[0], seed

examples = [
    "A photo of sks cartoon character driving a car",
    "A photo of sks cartoon character holding a banner titled Reliance Industries",
    "A photo of sks cartoon character eating at a restaurant",
]

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=16312,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1320, #Replace with defaults that work for your model
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=768, #Replace with defaults that work for your model
                )
            
            with gr.Row():
                
                lora_scale = gr.Slider(
                    label="Lora Scale",
                    minimum=0,
                    maximum=1,
                    step=0.1,
                    value=0.8, #Replace with defaults that work for your model
                )

            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0,
                    step=0.1,
                    value=13.5, #Replace with defaults that work for your model
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=50, #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, lora_scale],
        outputs = [result, seed]
    )

demo.queue().launch()