import gradio as gr
import numpy as np
import random
from peft import PeftModel, LoraConfig

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"  # 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.unet.load_adapter("um235/cartoon_cat_stickers")
pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    lscale,
    controlnet_enabled,
    control_strength,
    control_mode,
    control_image,
    ip_adapter_enabled,
    ip_adapter_scale,
    ip_adapter_image,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)
    
    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
    if (model_repo_id=="stable-diffusion-v1-5/stable-diffusion-v1-5"): 
        pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
        pipe.scale_lora(lscale)
    pipe = pipe.to(device)

    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 = [
    "Sticker, cartoon-style cat character with soft yellow fur. A gentle cat with expressive eyes that shine with a sad, emotional look. The cat, with a small pink nose and a flower on its head, appears to be crying, with blue teardrops around its eyes, giving the sticker a simple yet poignant design.",
    "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(" # UM235 DIFFUSION Space")
        
        model_id_input = gr.Dropdown(
                label="Choose Model",
                choices=[
                    "stable-diffusion-v1-5/stable-diffusion-v1-5",  
                    "CompVis/stable-diffusion-v1-4", 
                    "stable-diffusion-v1-5/stable-diffusion-v1-5 with lora",  
                ],
                value="stable-diffusion-v1-5/stable-diffusion-v1-5 with lora",  
                show_label=True,
                type="value",  
        )
        with gr.Row():
                lscale = gr.Slider(
                    label="Lora scale",
                    minimum=0,
                    maximum=2,
                    step=0.05,
                    value=1,  # Replace with defaults that work for your model
                )
        
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
        with gr.Accordion("ControlNet Settings", open=False):
            controlnet_enabled = gr.Checkbox(label="Enable ControlNet", value=False)

        with gr.Row():
                control_strength = gr.Slider(
                    label="ControlNet scale",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.75,
                    visible=False,
                )

                control_mode = gr.Dropdown(
                    label="ControlNet Mode",
                    choices=["edge_detection", "pose_estimation", "depth_estimation"],
                    value="edge_detection",
                    visible=False,
            )

        control_image = gr.Image(label="ControlNet Image", type="pil", visible=False)

        with gr.Accordion("IP-Adapter Settings", open=False):
            ip_adapter_enabled = gr.Checkbox(label="Enable IP-Adapter", value=False)

            with gr.Row():
                ip_adapter_scale = gr.Slider(
                    label="IP-Adapter Scale",
                    minimum=0.0,
                    maximum=2.0,
                    step=0.05,
                    value=1.0,
                    visible=False,
                )

            ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil", visible=False)

        with gr.Row():
            
            run_button = gr.Button("Run", scale=0, variant="primary")

        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=True,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            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=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # 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=7.0,  # 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=20,  # 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=[
            model_id_input,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            lscale,
            controlnet_enabled,
            control_strength,
            control_mode,
            control_image,
            ip_adapter_enabled,
            ip_adapter_scale,
            ip_adapter_image,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()