import gradio as gr
import numpy as np
import random

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

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id_default = "CompVis/stable-diffusion-v1-4"

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

pipe = DiffusionPipeline.from_pretrained(model_repo_id_default, torch_dtype=torch_dtype)
pipe = pipe.to(device)

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


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt: str,
    negative_prompt: str,
    width: int,
    height: int,
    num_inference_steps: int,
    progress=gr.Progress(track_tqdm=True),
    model_id: Optional[str] = 'CompVis/stable-diffusion-v1-4',
    seed: Optional[int] = 42,
    guidance_scale: Optional[float] = 7.0,
):
    generator = torch.Generator().manual_seed(seed)

    if model_id != model_repo_id_default:
        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
        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, pipe.name_or_path

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(" # DEMO Text-to-Image")

        with gr.Row():
            model_id = gr.Textbox(
                label="Model ID",
                max_lines=1,
                placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
                value="CompVis/stable-diffusion-v1-4"
            )

        prompt = gr.Textbox(
            label="Prompt",
            max_lines=1,
            placeholder="Enter your prompt",
        )

        negative_prompt = gr.Textbox(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
        )

        with gr.Row():
            seed = gr.Number(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=0.0,
                maximum=10.0,
                step=0.1,
                value=7.0,
            )

        with gr.Accordion("Optional Settings", open=False):
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                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=2,
                )

        run_button = gr.Button("Run", scale=1, variant="primary")
        result = gr.Image(label="Result", show_label=False)
        inferenced_model_name = gr.Label(show_label=True, value=model_id.value)
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[
            result, 
            inferenced_model_name,
        ],
    )

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