""""This has been duplicated to show the new duplication feature demo"""
import gradio as gr
import torch
import spaces
from PIL import Image, ImageDraw, ImageFont
from src.condition import Condition
from diffusers.pipelines import FluxPipeline
import numpy as np

from src.generate import seed_everything, generate

pipe = None
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
pipe.load_lora_weights(
    "Yuanshi/OminiControl",
    weight_name=f"omini/subject_512.safetensors",
    adapter_name="subject_512",
)
pipe.load_lora_weights(
    "Yuanshi/OminiControl",
    weight_name=f"omini/subject_1024_beta.safetensors",
    adapter_name="subject_1024",
)


@spaces.GPU
def process_image_and_text(image, resolution, text):
    w, h, min_size = image.size[0], image.size[1], min(image.size)
    image = image.crop(
        (
            (w - min_size) // 2,
            (h - min_size) // 2,
            (w + min_size) // 2,
            (h + min_size) // 2,
        )
    )
    image = image.resize((512, 512))

    condition = Condition("subject", image)

    result_img = generate(
        pipe,
        prompt=text.strip(),
        conditions=[condition],
        num_inference_steps=8,
        height=resolution,
        width=resolution,
    ).images[0]

    return result_img


def get_samples():
    sample_list = [
        {
            "image": "assets/oranges.jpg",
            "resolution": 512,
            "text": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show. With text on the screen that reads 'Omini Control!'",
        },
        {
            "image": "assets/penguin.jpg",
            "resolution": 512,
            "text": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat, holding a sign that reads 'Omini Control!'",
        },
        {
            "image": "assets/rc_car.jpg",
            "resolution": 1024,
            "text": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.",
        },
        {
            "image": "assets/clock.jpg",
            "resolution": 1024,
            "text": "In a Bauhaus style room, this item is placed on a shiny glass table, with a vase of flowers next to it. In the afternoon sun, the shadows of the blinds are cast on the wall.",
        },
    ]
    return [
        [
            Image.open(sample["image"]).resize((512, 512)),
            sample["resolution"],
            sample["text"],
        ]
        for sample in sample_list
    ]


header = """
# 🌍 OminiControl / FLUX

<div style="text-align: center; display: flex; justify-content: left; gap: 5px;">
<a href="https://arxiv.org/abs/2411.15098"><img src="https://img.shields.io/badge/ariXv-Paper-A42C25.svg" alt="arXiv"></a>
<a href="https://huggingface.co/Yuanshi/OminiControl"><img src="https://img.shields.io/badge/🤗-Model-ffbd45.svg" alt="HuggingFace"></a>
<a href="https://github.com/Yuanshi9815/OminiControl"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a>
</div>
"""


def create_app():
    with gr.Blocks() as app:
        gr.Markdown(header)
        with gr.Tabs():
            with gr.Tab("Subject-driven"):
                gr.Interface(
                    fn=process_image_and_text,
                    inputs=[
                        gr.Image(type="pil", label="Condition Image", width=300),
                        gr.Radio(
                            [("512", 512), ("1024(beta)", 1024)],
                            label="Resolution",
                            value=512,
                        ),
                        # gr.Slider(4, 16, 4, step=4, label="Inference Steps"),
                        gr.Textbox(lines=2, label="Text Prompt"),
                    ],
                    outputs=gr.Image(type="pil"),
                    examples=get_samples(),
                )
            with gr.Tab("Fill"):
                gr.Markdown("Coming soon")
            with gr.Tab("Canny"):
                gr.Markdown("Coming soon")
            with gr.Tab("Depth"):
                gr.Markdown("Coming soon")
    return app


if __name__ == "__main__":
    create_app().launch(debug=True, ssr_mode=False)