import gradio as gr
import numpy as np
import torch, random, json, spaces, time
from ulid import ULID
from diffsynth.pipelines.qwen_image import (
    QwenImagePipeline, ModelConfig,
    QwenImageUnit_Image2LoRAEncode, QwenImageUnit_Image2LoRADecode
)
from safetensors.torch import save_file
import torch
from PIL import Image
from utils import repo_utils, image_utils, prompt_utils


# repo_utils.clone_repo_if_not_exists("git clone https://huggingface.co/DiffSynth-Studio/General-Image-Encoders", "app/repos")
# repo_utils.clone_repo_if_not_exists("https://huggingface.co/apple/starflow", "app/models")

URL_PUBLIC = "https://huggingface.co/spaces/AiSudo/Qwen-Image-to-LoRA/blob/main"
DTYPE = torch.bfloat16
MAX_SEED = np.iinfo(np.int32).max

vram_config_disk_offload = {
    "offload_dtype": "disk",
    "offload_device": "disk",
    "onload_dtype": "disk",
    "onload_device": "disk",
    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}

# Load models
pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(
            download_source="huggingface", 
            model_id="DiffSynth-Studio/General-Image-Encoders", 
            origin_file_pattern="SigLIP2-G384/model.safetensors", 
            **vram_config_disk_offload
        ),
        ModelConfig(
            download_source="huggingface", 
            model_id="DiffSynth-Studio/General-Image-Encoders", 
            origin_file_pattern="DINOv3-7B/model.safetensors", 
            **vram_config_disk_offload
        ),
        ModelConfig(
            download_source="huggingface", 
            model_id="DiffSynth-Studio/Qwen-Image-i2L", 
            origin_file_pattern="Qwen-Image-i2L-Style.safetensors", 
            **vram_config_disk_offload
        ),
    ],
    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)


@spaces.GPU
def generate_lora(
    images,
    progress=gr.Progress(track_tqdm=True),
):

    ulid = str(ULID()).lower()
    print(f"ulid: {ulid}")

    # Load images 
    images = [
        Image.open("examples/style/1/0.jpg"),
        Image.open("examples/style/1/1.jpg"),
        Image.open("examples/style/1/2.jpg"),
        Image.open("examples/style/1/3.jpg"),
        Image.open("examples/style/1/4.jpg"),
    ]


    # Model inference
    with torch.no_grad():
        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
        lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]

    lora_path = f"loras/{ulid}.safetensors"
    lora_url = f"{URL_PUBLIC}/{lora_path}"

    save_file(lora, lora_path)

    return True

@spaces.GPU
def generate_image(
    prompt,
    negative_prompt,
    seed=42,
    randomize_seed=True,
    guidance_scale=1.5,
    num_inference_steps=8,
    progress=gr.Progress(track_tqdm=True),
):
    
    return True


def read_file(path: str) -> str:
    with open(path, 'r', encoding='utf-8') as f:
        content = f.read()
    return content


css = """
#col-container {
    margin: 0 auto;
    max-width: 960px;
}
"""



with open('examples/0_examples.json', 'r') as file: examples = json.load(file)
with gr.Blocks() as demo:
    has_lora = True
    with gr.Column(elem_id="col-container"):
        with gr.Column():
            gr.HTML(read_file("static/header.html"))
        with gr.Row():
            with gr.Column():
                input_images = gr.Gallery(
                    label="Generated images", 
                    show_label=False, 
                    elem_id="gallery", 
                    columns=2, 
                    object_fit="cover", 
                    height=300)
                
                lora_button = gr.Button("Generate LoRA", variant="primary")

            with gr.Column():
                lora_path = gr.Textbox(label="Generated LoRA path",lines=2, interactive=False)
                lora_download = gr.DownloadButton(label=f"Download LoRA", visible=has_lora)
        
        with gr.Row(visible=has_lora):
            gr.Markdown("Your LoRA is ready! Now, try generating some images.")
            with gr.Column():
                prompt = gr.Textbox(
                    label="Prompt",
                    show_label=False,
                    lines=2,
                    placeholder="Enter your prompt",
                    value="a man in a fishing boat. high quality, detailed"
                    # container=False,
                )
                
                imagen_button = gr.Button("Generate Image", variant="primary")

                
                with gr.Accordion("Advanced Settings", open=False):
                    
                    negative_prompt = gr.Textbox(
                        label="Negative prompt",
                        lines=2,
                        container=False,
                        placeholder="Enter your negative prompt",
                        value="blurry, ugly, bad"
                    )
                    with gr.Row():
                        num_inference_steps = gr.Slider(
                            label="Steps",
                            minimum=1,
                            maximum=30,
                            step=1,
                            value=9,
                        )
                        control_context_scale = gr.Slider(
                            label="Context scale",
                            minimum=0.0,
                            maximum=1.0,
                            step=0.01,
                            value=0.75,
                        )

                    with gr.Row():
                        guidance_scale = gr.Slider(
                            label="Guidance scale",
                            minimum=0.0,
                            maximum=10.0,
                            step=0.1,
                            value=1.0,
                        )

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

            with gr.Column():
                output_image = gr.Image(label="Generated image", show_label=False)
                    
        # gr.Examples(examples=examples, inputs=[input_image])
        gr.Markdown(read_file("static/footer.md"))

    lora_button.click(
        fn=generate_lora,
        inputs=[
            input_images
        ],
        outputs=[lora_path, lora_download, has_lora],
    )


if __name__ == "__main__":
    demo.launch(mcp_server=True, css=css)