import gradio as gr
import numpy as np
import random
import spaces  # Uncomment if using ZeroGPU

from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/stable-diffusion-2-1-base"

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

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

backgrounds_list = ["forest", "city street", "beach", "office", "bus", "laboratory", "factory", "construction site", "hospital", "night club", ""]
poses_list = ["portrait", "side-portrait"]
id_list = ["ID_0", "ID_1", "ID_2", "ID_3", "ID_4", "ID_5"]

gender_dict = {"ID_0": "male"}
MAX_SEED = 10000
image_size = 512

@spaces.GPU  # Uncomment if using ZeroGPU
def infer(
    background,
    pose,
    negative_prompt,
    seed,
    randomize_seed,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
    num_images=1
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    id = "ID_0"
    gender = gender_dict[id]

    # Construct prompt from dropdown selections
    prompt = f"face {pose.lower()} photo of {gender} {id} person, {background.lower()} background"

    print(prompt)
    print(negative_prompt)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=image_size,
        height=image_size,
        generator=generator,
        num_images_per_prompt=num_images,
    ).images[0]

    return image, seed

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(" # ID-Booth Demo")

        with gr.Row():
            which_id = gr.Dropdown(
                label="Identity",
                choices=id_list,
                value=id_list[0],
            )

            background = gr.Dropdown(
                label="Background",
                choices=backgrounds_list,
                value=backgrounds_list[0],
            )

            pose = gr.Dropdown(
                label="Pose",
                choices=poses_list,
                value=poses_list[0],
            )

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

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion(open=False, label="Advanced Options"):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="cartoon, cgi, render, illustration, painting, drawing, black and white, bad body proportions, landscape",
            )
            num_inference_steps = gr.Slider( 
                label="Number of sample steps",
                minimum=1,
                maximum=100,
                step=1,
                value=25,
            )
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=0.1,
                maximum=10.0,
                step=0.1,
                value=3.0,
            )
            num_images = gr.Slider(
                label="Number of output images",
                minimum=1,
                maximum=4,
                step=1,
                value=2,
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)


        gr.Examples(
            examples=[
                [id_list[0], backgrounds_list[0], poses_list[0], "A beautiful photo of a person", 0, False, 512, 512, 7.5, 50],
            ],
            inputs=[which_id, background, pose],
        )

    gr.on(
        triggers=[run_button.click],
        fn=infer,
        inputs=[
            background,
            pose,
            negative_prompt,
            seed,
            randomize_seed,
            guidance_scale,
            num_inference_steps,
            num_images
        ],
        outputs=[result, seed],
    )

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