import gradio as gr
import numpy as np
import random
import spaces  # Uncomment if using ZeroGPU
import os 
from diffusers import StableDiffusionPipeline, DDPMScheduler
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)

pipe = StableDiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch.float16).to(device)
pipe.scheduler = DDPMScheduler.from_pretrained(model_repo_id, subfolder="scheduler")

folder_of_lora_weights = "./ID-Booth_LoRA_weights"
which_checkpoint = "checkpoint-31-6400"
lora_name = "pytorch_lora_weights.safetensors"

folder_of_identity_images = "./assets/example_images/"

backgrounds_list = ["Forest", "City street", "Beach", "Office", "Bus", "Laboratory", "Factory", "Construction site", "Hospital", "Night club", ""]
poses_list = ["Portrait", "Side-portrait"]
id_list = ["ID_1", "ID_5", "ID_16", "ID_20"]

gender_dict = {"ID_1": "male", "ID_5": "male", "ID_16": "female", "ID_20": "male"}
MAX_SEED = 10000
image_size = 512


@spaces.GPU  # Uncomment if using ZeroGPU
def infer(
    identity,
    background,
    pose,
    negative_prompt,
    seed,
    randomize_seed,
    guidance_scale,
    num_inference_steps,
    num_images=1
):
    
    full_lora_weights_path = f"{folder_of_lora_weights}/{identity}/{which_checkpoint}/{lora_name}"
    pipe.load_lora_weights(full_lora_weights_path)
        
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    gender = gender_dict[identity]

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

    images = 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

    return images


### Description
header = " # ID-Booth: Identity-consistent Face Generation with Diffusion Models"
description = "This is an official Gradio demo for the paper <a href='https://dariant.github.io/publications/ID-Booth' target='_blank'>ID-Booth: Identity-consistent Face Generation with Diffusion Models</a>"

footer = r"""
**Citation**
<br>
If you find ID-Booth helpful, please consider citing our paper:
```bibtex
@article{tomasevic2025IDBooth,
  title={{ID-Booth}: Identity-consistent Face Generation with Diffusion Models},
  author={Toma{\v{s}}evi{\'c}, Darian and Boutros, Fadi and Lin, Chenhao and Damer, Naser and {\v{S}}truc, Vitomir and Peer, Peter},
  journal={arXiv preprint arXiv:2504.07392},
  year={2025}
}
```
"""

css = '''
.gradio-container {
    width: 75%;
    margin: auto;
}
'''

with gr.Blocks(css=css) as demo:

    # description
    gr.Markdown(header)
    gr.Markdown(description)
    with gr.Column():
        # with gr.Row():
        #     gr.Markdown("### Choose an identity, background, and pose:")

        with gr.Row():
            for id in id_list:
                image_path = os.path.join(folder_of_identity_images, id + ".jpg")
                img = gr.Image(value=image_path, label=id, 
                               width=256, height=256,
                               show_label=True, interactive=False,
                               show_download_button=False, 
                               show_fullscreen_button=False,
                               show_share_button=False,
                               )
                   
            
        with gr.Row():
            identity = gr.Dropdown(
                label="Identity:",
                choices=id_list,
                value=id_list[2],
            )

            background = gr.Dropdown(
                label="Background:",
                choices=backgrounds_list,
                value=backgrounds_list[1],
            )

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

        run_button = gr.Button("Generate in-the-wild images", scale=0, variant="primary")
        #result = gr.Image(label="Result", show_label=False)
        result = gr.Gallery(label="Generated Images", 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 sampling steps",
                minimum=1,
                maximum=100,
                step=1,
                value=30,
            )
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=0.1,
                maximum=10.0,
                step=0.1,
                value=5.0,
            )
            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=[selected_identity, background, pose],
        # )

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

    gr.Markdown(footer)

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