import gradio as gr
import numpy as np
import random
import torch
import spaces

from huggingface_hub import hf_hub_download
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from safetensors import safe_open

from PIL import Image
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
from torchao.quantization import quantize_
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3

import time


def update_history(new_images, history):
    time.sleep(0.5)
    if history is None:
        history = []
    if new_images is not None and len(new_images) > 0:
        if not isinstance(history, list):
            history = list(history) if history else []
        for img in new_images:
            history.insert(0, img)
    history = history[:20]
    return history

def use_history_as_input(evt: gr.SelectData):
    if evt.value is not None:
        return gr.update(value=evt.value)
    return gr.update()

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
BASE_MODEL_ID = "Qwen/Qwen-Image-Edit-2511"
BASE_MODEL_REVISION = "6f3ccc0b56e431dc6a0c2b2039706d7d26f22cb9"
SFW_REPO = "Phr00t/Qwen-Image-Edit-Rapid-AIO"
SFW_FILE = "v19/Qwen-Rapid-AIO-SFW-v19.safetensors"
_sfw_path = hf_hub_download(SFW_REPO, SFW_FILE)

_PREFIX = "model.diffusion_model."

_config = QwenImageTransformer2DModel.load_config(
    BASE_MODEL_ID, subfolder="transformer", revision=BASE_MODEL_REVISION
)
with init_empty_weights():
    transformer = QwenImageTransformer2DModel.from_config(_config)

_placed = set()
with safe_open(_sfw_path, framework="pt", device="cpu") as _f:
    for _key in _f.keys():
        if not _key.startswith(_PREFIX):
            continue
        _name = _key[len(_PREFIX):]
        if _name.startswith("__"):
            continue
        set_module_tensor_to_device(
            transformer, _name, "cpu", value=_f.get_tensor(_key), dtype=dtype
        )
        _placed.add(_name)

_expected = set(transformer.state_dict().keys())
_absent = _expected - _placed
if _absent:
    raise RuntimeError(
        f"{SFW_FILE} is missing {len(_absent)} of {len(_expected)} tensors, "
        f"starting with {sorted(_absent)[:5]}"
    )
transformer.eval()
print(f"Loaded {SFW_FILE}: {len(_placed)} tensors", flush=True)

pipe = QwenImageEditPlusPipeline.from_pretrained(
    BASE_MODEL_ID,
    revision=BASE_MODEL_REVISION,
    transformer=transformer,
    torch_dtype=dtype,
)

del transformer
if torch.cuda.is_available():
    torch.cuda.empty_cache()

if torch.cuda.is_available():
    torch.cuda.empty_cache()

fp8_config = Float8DynamicActivationFloat8WeightConfig()
quantize_(pipe.transformer, fp8_config)
try:
    quantize_(pipe.text_encoder, fp8_config)
except Exception as exc:
    print(
        f"Text-encoder FP8 quantization unavailable ({type(exc).__name__}); "
        "continuing with the pinned text encoder.",
        flush=True,
    )

pipe = pipe.to(device)

try:
    pipe.transformer.__class__ = QwenImageTransformer2DModel
    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
except Exception as exc:
    print(f"FA3 attention processor unavailable; continuing with default attention: {exc}")


MAX_SEED = np.iinfo(np.int32).max
MIN_INPUT_SIDE = 256

def use_output_as_input(output_images):
    if not output_images:
        return gr.update()
    first = output_images[0]
    path = first[0] if isinstance(first, (list, tuple)) else first
    return gr.update(value=path)


@spaces.GPU(duration=45)
def infer(
    image_1,
    image_2,
    prompt,
    seed=42,
    randomize_seed=False,
    true_guidance_scale=1.0,
    num_inference_steps=4,
    height=None,
    width=None,
    num_images_per_prompt=1,
    progress=gr.Progress(track_tqdm=True),
):
    negative_prompt = " "

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    pil_images = []
    for img in (image_1, image_2):
        if img is None:
            continue
        try:
            if isinstance(img, str):
                pil_images.append(Image.open(img).convert("RGB"))
            elif isinstance(img, Image.Image):
                pil_images.append(img.convert("RGB"))
            elif hasattr(img, "name"):
                pil_images.append(Image.open(img.name).convert("RGB"))
        except Exception:
            continue

    if not pil_images:
        raise gr.Error("Upload at least one valid input image before editing.")

    for img in pil_images:
        if min(img.size) < MIN_INPUT_SIDE:
            raise gr.Error(
                f"Input images must be at least {MIN_INPUT_SIDE}px on each side. "
                "Very small source images can produce tiled or grid-like artifacts."
            )

    print(
        f"Starting generation: seed={seed}, steps={num_inference_steps}, "
        f"guidance={true_guidance_scale}, size={width}x{height}, inputs={len(pil_images)}",
        flush=True,
    )

    images_pil = pipe(
        image=pil_images if len(pil_images) > 0 else None,
        prompt=prompt,
        height=height,
        width=width,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        generator=generator,
        true_cfg_scale=true_guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
    ).images

    return images_pil, seed, gr.update(visible=True)


css = """
#col-container {
    margin: 0 auto;
    max-width: 1024px;
}
#logo-title {
    text-align: center;
}
#logo-title img {
    width: 400px;
}
#edit_text{margin-top: -62px !important}
"""

with gr.Blocks(css=css, delete_cache=(3600, 86400)) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""
        <div style="text-align:center;padding:4rem 1rem;
                    font-family:system-ui,sans-serif;color:#999;font-size:.9rem">
            This Space has no public interface.
        </div>
        """)
        with gr.Row(visible=False):
            with gr.Column():
                with gr.Row():
                    image_1 = gr.Image(label="Image 1", type="filepath", interactive=True)
                    image_2 = gr.Image(label="Image 2 (optional)", type="filepath", interactive=True)

                prompt = gr.Text(
                    label="Prompt 🪄",
                    show_label=True,
                    placeholder="Enter your prompt here...",
            )
                run_button = gr.Button("Edit!", variant="primary")
                
                with gr.Accordion("Advanced Settings", open=False):
                    
        
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
        
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        
                    with gr.Row():
        
                        true_guidance_scale = gr.Slider(
                            label="True guidance scale",
                            minimum=1.0,
                            maximum=10.0,
                            step=0.1,
                            value=1.0
                        )

                        num_inference_steps = gr.Slider(
                            label="Number of inference steps",
                            minimum=1,
                            maximum=40,
                            step=1,
                            value=4,
                        )
                        
            with gr.Column():
                result = gr.Gallery(label="Result", show_label=False, type="filepath")
                with gr.Row():
                    use_output_btn = gr.Button("↗️ Use as input", variant="secondary", size="sm", visible=False)

                with gr.Row(visible=False):
                    gr.Markdown("### 📜 History")
                    clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop")
                
                history_gallery = gr.Gallery(
                    label="Click any image to use as input", 
                    interactive=False,
                    show_label=True,
                    visible=False
                )


        

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            image_1,
            image_2,
            prompt,
            seed,
            randomize_seed,
            true_guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed, use_output_btn],

    ).then(
    fn=update_history,
    inputs=[result, history_gallery],
    outputs=history_gallery,

    )

    use_output_btn.click(
        fn=use_output_as_input,
        inputs=[result],
        outputs=[image_1]
    )

    history_gallery.select(
        fn=use_history_as_input,
        inputs=None,
        outputs=[image_1],

    )
    
    clear_history_button.click(
        fn=lambda: [],
        inputs=None,
        outputs=history_gallery,

    )


def _dressfit_load(src):
    import base64 as _b64, urllib.request as _url
    from io import BytesIO as _BytesIO
    if src.startswith("http://") or src.startswith("https://"):
        req = _url.Request(src, headers={"User-Agent": "dressfit/1.0"})
        with _url.urlopen(req, timeout=30) as resp:
            raw = resp.read(25 * 1024 * 1024)
    else:
        if src.startswith("data:"):
            src = src.split(",", 1)[1]
        raw = _b64.b64decode(src)
    return Image.open(_BytesIO(raw)).convert("RGB")


def dressfit_tryon(person: str, garment: str, prompt: str,
                   steps: float = 4, guidance: float = 1.0,
                   seed: float = 0, randomize: bool = True) -> dict:
    import base64 as _b64, traceback as _tb
    from io import BytesIO as _BytesIO
    try:
        result, used_seed = infer(
            _dressfit_load(person), _dressfit_load(garment), prompt,
            int(seed), bool(randomize), float(guidance), int(steps),
        )[:2]
        if isinstance(result, (list, tuple)):
            result = result[0] if result else None
        if result is None:
            return {"status": "error", "message": "the pipeline returned nothing"}
        buf = _BytesIO()
        result.save(buf, format="PNG")
        return {"status": "success", "seed": int(used_seed),
                "image": "data:image/png;base64," + _b64.b64encode(buf.getvalue()).decode()}
    except Exception as exc:
        return {"status": "error", "message": "".join(_tb.format_exception(exc))[-2500:]}


with demo:
    gr.api(dressfit_tryon, api_name="dressfit_tryon")

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