import torch
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
from transformers import CLIPVisionModel
import gradio as gr
import tempfile
import spaces
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import random

MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
LORA_REPO_ID = "Kijai/WanVideo_comfy"
LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"

image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32)
vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(
    MODEL_ID, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
pipe.to("cuda")

causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
pipe.fuse_lora()

MOD_VALUE = 32
DEFAULT_H_SLIDER_VALUE = 320
DEFAULT_W_SLIDER_VALUE = 560
NEW_FORMULA_MAX_AREA = 480.0 * 832.0 

SLIDER_MIN_H, SLIDER_MAX_H = 128, 896
SLIDER_MIN_W, SLIDER_MAX_W = 128, 896
MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 24
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 120

default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
default_negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards, watermark, text, signature"

# CSS 스타일 정의
custom_css = """
/* 전체 배경 그라디언트 */
.gradio-container {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #f5576c 75%, #fa709a 100%) !important;
    background-size: 400% 400% !important;
    animation: gradientShift 15s ease infinite !important;
}

@keyframes gradientShift {
    0% { background-position: 0% 50%; }
    50% { background-position: 100% 50%; }
    100% { background-position: 0% 50%; }
}

/* 메인 컨테이너 스타일 */
.main-container {
    backdrop-filter: blur(10px);
    background: rgba(255, 255, 255, 0.1) !important;
    border-radius: 20px !important;
    padding: 30px !important;
    box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37) !important;
    border: 1px solid rgba(255, 255, 255, 0.18) !important;
}

/* 헤더 스타일 */
h1 {
    background: linear-gradient(45deg, #ffffff, #f0f0f0) !important;
    -webkit-background-clip: text !important;
    -webkit-text-fill-color: transparent !important;
    background-clip: text !important;
    font-weight: 800 !important;
    font-size: 2.5rem !important;
    text-align: center !important;
    margin-bottom: 2rem !important;
    text-shadow: 2px 2px 4px rgba(0,0,0,0.1) !important;
}

/* 컴포넌트 컨테이너 스타일 */
.input-container, .output-container {
    background: rgba(255, 255, 255, 0.08) !important;
    border-radius: 15px !important;
    padding: 20px !important;
    margin: 10px 0 !important;
    backdrop-filter: blur(5px) !important;
    border: 1px solid rgba(255, 255, 255, 0.1) !important;
}

/* 입력 필드 스타일 */
input, textarea, .gr-box {
    background: rgba(255, 255, 255, 0.9) !important;
    border: 1px solid rgba(255, 255, 255, 0.3) !important;
    border-radius: 10px !important;
    color: #333 !important;
    transition: all 0.3s ease !important;
}

input:focus, textarea:focus {
    background: rgba(255, 255, 255, 1) !important;
    border-color: #667eea !important;
    box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1) !important;
}

/* 버튼 스타일 */
.generate-btn {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    color: white !important;
    font-weight: 600 !important;
    font-size: 1.1rem !important;
    padding: 12px 30px !important;
    border-radius: 50px !important;
    border: none !important;
    cursor: pointer !important;
    transition: all 0.3s ease !important;
    box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4) !important;
}

.generate-btn:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 6px 20px rgba(102, 126, 234, 0.6) !important;
}

/* 슬라이더 스타일 */
input[type="range"] {
    background: transparent !important;
}

input[type="range"]::-webkit-slider-track {
    background: rgba(255, 255, 255, 0.3) !important;
    border-radius: 5px !important;
    height: 6px !important;
}

input[type="range"]::-webkit-slider-thumb {
    background: linear-gradient(135deg, #667eea, #764ba2) !important;
    border: 2px solid white !important;
    border-radius: 50% !important;
    cursor: pointer !important;
    width: 18px !important;
    height: 18px !important;
    -webkit-appearance: none !important;
}

/* Accordion 스타일 */
.gr-accordion {
    background: rgba(255, 255, 255, 0.05) !important;
    border-radius: 10px !important;
    border: 1px solid rgba(255, 255, 255, 0.1) !important;
    margin: 15px 0 !important;
}

/* 라벨 스타일 */
label {
    color: #ffffff !important;
    font-weight: 500 !important;
    font-size: 0.95rem !important;
    margin-bottom: 5px !important;
}

/* 이미지 업로드 영역 */
.image-upload {
    border: 2px dashed rgba(255, 255, 255, 0.3) !important;
    border-radius: 15px !important;
    background: rgba(255, 255, 255, 0.05) !important;
    transition: all 0.3s ease !important;
}

.image-upload:hover {
    border-color: rgba(255, 255, 255, 0.5) !important;
    background: rgba(255, 255, 255, 0.1) !important;
}

/* 비디오 출력 영역 */
video {
    border-radius: 15px !important;
    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3) !important;
}

/* Examples 섹션 스타일 */
.gr-examples {
    background: rgba(255, 255, 255, 0.05) !important;
    border-radius: 15px !important;
    padding: 20px !important;
    margin-top: 20px !important;
}

/* Checkbox 스타일 */
input[type="checkbox"] {
    accent-color: #667eea !important;
}

/* 반응형 애니메이션 */
@media (max-width: 768px) {
    h1 { font-size: 2rem !important; }
    .main-container { padding: 20px !important; }
}
"""

def _calculate_new_dimensions_wan(pil_image, mod_val, calculation_max_area,
                                 min_slider_h, max_slider_h,
                                 min_slider_w, max_slider_w,
                                 default_h, default_w):
    orig_w, orig_h = pil_image.size
    if orig_w <= 0 or orig_h <= 0:
        return default_h, default_w

    aspect_ratio = orig_h / orig_w
    
    calc_h = round(np.sqrt(calculation_max_area * aspect_ratio))
    calc_w = round(np.sqrt(calculation_max_area / aspect_ratio))

    calc_h = max(mod_val, (calc_h // mod_val) * mod_val)
    calc_w = max(mod_val, (calc_w // mod_val) * mod_val)
    
    new_h = int(np.clip(calc_h, min_slider_h, (max_slider_h // mod_val) * mod_val))
    new_w = int(np.clip(calc_w, min_slider_w, (max_slider_w // mod_val) * mod_val))
    
    return new_h, new_w

def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_w_val):
    if uploaded_pil_image is None:
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
    try:
        new_h, new_w = _calculate_new_dimensions_wan(
            uploaded_pil_image, MOD_VALUE, NEW_FORMULA_MAX_AREA,
            SLIDER_MIN_H, SLIDER_MAX_H, SLIDER_MIN_W, SLIDER_MAX_W,
            DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE
        )
        return gr.update(value=new_h), gr.update(value=new_w)
    except Exception as e:
        gr.Warning("Error attempting to calculate new dimensions")
        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)

def get_duration(input_image, prompt, height, width, 
                   negative_prompt, duration_seconds,
                   guidance_scale, steps,
                   seed, randomize_seed, 
                   progress):
    if steps > 4 and duration_seconds > 2:
        return 90
    elif steps > 4 or duration_seconds > 2:
        return 75
    else:
        return 60

@spaces.GPU(duration=get_duration)
def generate_video(input_image, prompt, height, width, 
                   negative_prompt=default_negative_prompt, duration_seconds = 2,
                   guidance_scale = 1, steps = 4,
                   seed = 42, randomize_seed = False, 
                   progress=gr.Progress(track_tqdm=True)):
    
    if input_image is None:
        raise gr.Error("Please upload an input image.")

    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
    
    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
    
    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)

    resized_image = input_image.resize((target_w, target_h))

    with torch.inference_mode():
        output_frames_list = pipe(
            image=resized_image, prompt=prompt, negative_prompt=negative_prompt,
            height=target_h, width=target_w, num_frames=num_frames,
            guidance_scale=float(guidance_scale), num_inference_steps=int(steps),
            generator=torch.Generator(device="cuda").manual_seed(current_seed)
        ).frames[0]

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
        video_path = tmpfile.name
    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
    return video_path, current_seed

with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    with gr.Column(elem_classes=["main-container"]):
        gr.Markdown("# ✨ Fast 4 steps Wan 2.1 I2V (14B) with CausVid LoRA")

        # Add badges side by side
        gr.HTML("""
        <div class="badge-container">
            <a href="https://huggingface.co/spaces/Heartsync/wan2-1-fast-security" target="_blank">
                <img src="https://img.shields.io/static/v1?label=WAN%202.1&message=FAST%20%26%20Furios&color=%23008080&labelColor=%230000ff&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
            </a>
            <a href="https://huggingface.co/spaces/Heartsync/WAN-VIDEO-AUDIO" target="_blank">
                <img src="https://img.shields.io/static/v1?label=WAN%202.1&message=VIDEO%20%26%20AUDIO&color=%23008080&labelColor=%230000ff&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
            </a>
        </div>
        """)
        
        with gr.Row():
            with gr.Column(elem_classes=["input-container"]):
                input_image_component = gr.Image(
                    type="pil", 
                    label="🖼️ Input Image (auto-resized to target H/W)",
                    elem_classes=["image-upload"]
                )
                prompt_input = gr.Textbox(
                    label="✏️ Prompt", 
                    value=default_prompt_i2v,
                    lines=2
                )
                duration_seconds_input = gr.Slider(
                    minimum=round(MIN_FRAMES_MODEL/FIXED_FPS,1), 
                    maximum=round(MAX_FRAMES_MODEL/FIXED_FPS,1), 
                    step=0.1, 
                    value=2, 
                    label="⏱️ Duration (seconds)", 
                    info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps."
                )
                
                with gr.Accordion("⚙️ Advanced Settings", open=False):
                    negative_prompt_input = gr.Textbox(
                        label="❌ Negative Prompt", 
                        value=default_negative_prompt, 
                        lines=3
                    )
                    seed_input = gr.Slider(
                        label="🎲 Seed", 
                        minimum=0, 
                        maximum=MAX_SEED, 
                        step=1, 
                        value=42, 
                        interactive=True
                    )
                    randomize_seed_checkbox = gr.Checkbox(
                        label="🔀 Randomize seed", 
                        value=True, 
                        interactive=True
                    )
                    with gr.Row():
                        height_input = gr.Slider(
                            minimum=SLIDER_MIN_H, 
                            maximum=SLIDER_MAX_H, 
                            step=MOD_VALUE, 
                            value=DEFAULT_H_SLIDER_VALUE, 
                            label=f"📏 Output Height (multiple of {MOD_VALUE})"
                        )
                        width_input = gr.Slider(
                            minimum=SLIDER_MIN_W, 
                            maximum=SLIDER_MAX_W, 
                            step=MOD_VALUE, 
                            value=DEFAULT_W_SLIDER_VALUE, 
                            label=f"📐 Output Width (multiple of {MOD_VALUE})"
                        )
                    steps_slider = gr.Slider(
                        minimum=1, 
                        maximum=30, 
                        step=1, 
                        value=4, 
                        label="🚀 Inference Steps"
                    ) 
                    guidance_scale_input = gr.Slider(
                        minimum=0.0, 
                        maximum=20.0, 
                        step=0.5, 
                        value=1.0, 
                        label="🎯 Guidance Scale", 
                        visible=False
                    )

                generate_button = gr.Button(
                    "🎬 Generate Video", 
                    variant="primary",
                    elem_classes=["generate-btn"]
                )
                
            with gr.Column(elem_classes=["output-container"]):
                video_output = gr.Video(
                    label="🎥 Generated Video", 
                    autoplay=True, 
                    interactive=False
                )

        input_image_component.upload(
            fn=handle_image_upload_for_dims_wan,
            inputs=[input_image_component, height_input, width_input],
            outputs=[height_input, width_input]
        )
        
        input_image_component.clear( 
            fn=handle_image_upload_for_dims_wan,
            inputs=[input_image_component, height_input, width_input],
            outputs=[height_input, width_input]
        )
        
        ui_inputs = [
            input_image_component, prompt_input, height_input, width_input,
            negative_prompt_input, duration_seconds_input,
            guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
        ]
        generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])

        with gr.Column():
            gr.Examples(
                examples=[ 
                    ["peng.png", "a penguin playfully dancing in the snow, Antarctica", 896, 512],
                    ["forg.jpg", "the frog jumps around", 448, 832],
                ],
                inputs=[input_image_component, prompt_input, height_input, width_input], 
                outputs=[video_output, seed_input], 
                fn=generate_video, 
                cache_examples="lazy",
                label="🌟 Example Gallery"
            )

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