import os
import math
import gradio as gr
import numpy as np
import torch
import safetensors.torch as sf
import db_examples
import datetime
from pathlib import Path

from PIL import Image
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import CLIPTextModel, CLIPTokenizer
from briarmbg import BriaRMBG
from enum import Enum
from torch.hub import download_url_to_file
import cv2

from typing import Optional

from Depth.depth_anything_v2.dpt import DepthAnythingV2



# from FLORENCE
import spaces
import supervision as sv
import torch
from PIL import Image


from utils.florence import load_florence_model, run_florence_inference, \
    FLORENCE_OPEN_VOCABULARY_DETECTION_TASK
from utils.sam import load_sam_image_model, run_sam_inference


import torch
DEVICE = torch.device("cuda")

torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
if torch.cuda.get_device_properties(0).major >= 8:
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True


FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=DEVICE)
SAM_IMAGE_MODEL = load_sam_image_model(device=DEVICE)

@spaces.GPU(duration=20)
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def process_image(image_input, text_input) -> Optional[Image.Image]:
    # if not image_input:
    #     gr.Info("Please upload an image.")
    #     return None

    # if not text_input:
    #     gr.Info("Please enter a text prompt.")
    #     return None

    _, result = run_florence_inference(
        model=FLORENCE_MODEL,
        processor=FLORENCE_PROCESSOR,
        device=DEVICE,
        image=image_input,
        task=FLORENCE_OPEN_VOCABULARY_DETECTION_TASK,
        text=text_input
    )
    detections = sv.Detections.from_lmm(
        lmm=sv.LMM.FLORENCE_2,
        result=result,
        resolution_wh=image_input.size
    )
    detections = run_sam_inference(SAM_IMAGE_MODEL, image_input, detections)
    if len(detections) == 0:
        gr.Info("No objects detected.")
        return None
    return Image.fromarray(detections.mask[0].astype("uint8") * 255)


try:
    import xformers
    import xformers.ops
    XFORMERS_AVAILABLE = True
    print("xformers is available - Using memory efficient attention")
except ImportError:
    XFORMERS_AVAILABLE = False
    print("xformers not available - Using default attention")

# 'stablediffusionapi/realistic-vision-v51'
# 'runwayml/stable-diffusion-v1-5'
sd15_name = 'stablediffusionapi/realistic-vision-v51'
tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4")

# Change UNet

with torch.no_grad():
    new_conv_in = torch.nn.Conv2d(12, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
    new_conv_in.weight.zero_()
    new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
    new_conv_in.bias = unet.conv_in.bias
    unet.conv_in = new_conv_in

unet_original_forward = unet.forward


def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
    c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
    new_sample = torch.cat([sample, c_concat], dim=1)
    kwargs['cross_attention_kwargs'] = {}
    return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)


unet.forward = hooked_unet_forward

# Load

model_path = './models/iclight_sd15_fbc.safetensors'

if not os.path.exists(model_path):
    download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fbc.safetensors', dst=model_path)

# Device and dtype setup
device = torch.device('cuda')
dtype = torch.float16  # RTX 2070 works well with float16

# Memory optimizations for RTX 2070
torch.backends.cudnn.benchmark = True
if torch.cuda.is_available():
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
    # Set a smaller attention slice size for RTX 2070
    torch.backends.cuda.max_split_size_mb = 512

# Move models to device with consistent dtype
text_encoder = text_encoder.to(device=device, dtype=dtype)
vae = vae.to(device=device, dtype=dtype)  # Changed from bfloat16 to float16
unet = unet.to(device=device, dtype=dtype)
rmbg = rmbg.to(device=device, dtype=torch.float32)  # Keep this as float32

model = DepthAnythingV2(encoder='vits', features=64, out_channels=[48, 96, 192, 384])
model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vits.pth', map_location=device))
model.eval()


from utils.florence import load_florence_model, run_florence_inference, \
    FLORENCE_OPEN_VOCABULARY_DETECTION_TASK
from utils.sam import load_sam_image_model, run_sam_inference


torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
if torch.cuda.get_device_properties(0).major >= 8:
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True


#FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=device)
SAM_IMAGE_MODEL = load_sam_image_model(device=device)

# Update the state dict merging to use correct dtype
sd_offset = sf.load_file(model_path)
sd_origin = unet.state_dict()
sd_merged = {k: sd_origin[k] + sd_offset[k].to(device=device, dtype=dtype) for k in sd_origin.keys()}
unet.load_state_dict(sd_merged, strict=True)
del sd_offset, sd_origin, sd_merged

def enable_efficient_attention():
    if XFORMERS_AVAILABLE:
        try:
            # RTX 2070 specific settings
            unet.set_use_memory_efficient_attention_xformers(True)
            vae.set_use_memory_efficient_attention_xformers(True)
            print("Enabled xformers memory efficient attention")
        except Exception as e:
            print(f"Xformers error: {e}")
            print("Falling back to sliced attention")
            # Use sliced attention for RTX 2070
            unet.set_attention_slice_size(4)
            vae.set_attention_slice_size(4)
            unet.set_attn_processor(AttnProcessor2_0())
            vae.set_attn_processor(AttnProcessor2_0())
    else:
        # Fallback for when xformers is not available
        print("Using sliced attention")
        unet.set_attention_slice_size(4)
        vae.set_attention_slice_size(4)
        unet.set_attn_processor(AttnProcessor2_0())
        vae.set_attn_processor(AttnProcessor2_0())

# Add memory clearing function
def clear_memory():
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

# Enable efficient attention
enable_efficient_attention()

# Samplers

ddim_scheduler = DDIMScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    clip_sample=False,
    set_alpha_to_one=False,
    steps_offset=1,
)

euler_a_scheduler = EulerAncestralDiscreteScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    steps_offset=1
)

dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
    num_train_timesteps=1000,
    beta_start=0.00085,
    beta_end=0.012,
    algorithm_type="sde-dpmsolver++",
    use_karras_sigmas=True,
    steps_offset=1
)

# Pipelines

t2i_pipe = StableDiffusionPipeline(
    vae=vae,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=dpmpp_2m_sde_karras_scheduler,
    safety_checker=None,
    requires_safety_checker=False,
    feature_extractor=None,
    image_encoder=None
)

i2i_pipe = StableDiffusionImg2ImgPipeline(
    vae=vae,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    unet=unet,
    scheduler=dpmpp_2m_sde_karras_scheduler,
    safety_checker=None,
    requires_safety_checker=False,
    feature_extractor=None,
    image_encoder=None
)


@torch.inference_mode()
def encode_prompt_inner(txt: str):
    max_length = tokenizer.model_max_length
    chunk_length = tokenizer.model_max_length - 2
    id_start = tokenizer.bos_token_id
    id_end = tokenizer.eos_token_id
    id_pad = id_end

    def pad(x, p, i):
        return x[:i] if len(x) >= i else x + [p] * (i - len(x))

    tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
    chunks = [pad(ck, id_pad, max_length) for ck in chunks]

    token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
    conds = text_encoder(token_ids).last_hidden_state

    return conds


@torch.inference_mode()
def encode_prompt_pair(positive_prompt, negative_prompt):
    c = encode_prompt_inner(positive_prompt)
    uc = encode_prompt_inner(negative_prompt)

    c_len = float(len(c))
    uc_len = float(len(uc))
    max_count = max(c_len, uc_len)
    c_repeat = int(math.ceil(max_count / c_len))
    uc_repeat = int(math.ceil(max_count / uc_len))
    max_chunk = max(len(c), len(uc))

    c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
    uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]

    c = torch.cat([p[None, ...] for p in c], dim=1)
    uc = torch.cat([p[None, ...] for p in uc], dim=1)

    return c, uc


@torch.inference_mode()
def pytorch2numpy(imgs, quant=True):
    results = []
    for x in imgs:
        y = x.movedim(0, -1)

        if quant:
            y = y * 127.5 + 127.5
            y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8)
        else:
            y = y * 0.5 + 0.5
            y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32)

        results.append(y)
    return results


@torch.inference_mode()
def numpy2pytorch(imgs):
    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
    h = h.movedim(-1, 1)
    return h


def resize_and_center_crop(image, target_width, target_height):
    pil_image = Image.fromarray(image)
    original_width, original_height = pil_image.size
    scale_factor = max(target_width / original_width, target_height / original_height)
    resized_width = int(round(original_width * scale_factor))
    resized_height = int(round(original_height * scale_factor))
    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
    left = (resized_width - target_width) / 2
    top = (resized_height - target_height) / 2
    right = (resized_width + target_width) / 2
    bottom = (resized_height + target_height) / 2
    cropped_image = resized_image.crop((left, top, right, bottom))
    return np.array(cropped_image)


def resize_without_crop(image, target_width, target_height):
    pil_image = Image.fromarray(image)
    resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
    return np.array(resized_image)


@torch.inference_mode()
def run_rmbg(img, sigma=0.0):
    # Convert RGBA to RGB if needed
    if img.shape[-1] == 4:
        # Use white background for alpha composition
        alpha = img[..., 3:] / 255.0
        rgb = img[..., :3]
        white_bg = np.ones_like(rgb) * 255
        img = (rgb * alpha + white_bg * (1 - alpha)).astype(np.uint8)
    
    H, W, C = img.shape
    assert C == 3
    k = (256.0 / float(H * W)) ** 0.5
    feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k)))
    feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32)
    alpha = rmbg(feed)[0][0]
    alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear")
    alpha = alpha.movedim(1, -1)[0]
    alpha = alpha.detach().float().cpu().numpy().clip(0, 1)
    
    # Create RGBA image
    rgba = np.dstack((img, alpha * 255)).astype(np.uint8)
    result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha
    return result.clip(0, 255).astype(np.uint8), rgba


@torch.inference_mode()
def process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
    clear_memory()
    bg_source = BGSource(bg_source)

    if bg_source == BGSource.UPLOAD:
        pass
    elif bg_source == BGSource.UPLOAD_FLIP:
        input_bg = np.fliplr(input_bg)
    elif bg_source == BGSource.GREY:
        input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
    elif bg_source == BGSource.LEFT:
        gradient = np.linspace(224, 32, image_width)
        image = np.tile(gradient, (image_height, 1))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == BGSource.RIGHT:
        gradient = np.linspace(32, 224, image_width)
        image = np.tile(gradient, (image_height, 1))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == BGSource.TOP:
        gradient = np.linspace(224, 32, image_height)[:, None]
        image = np.tile(gradient, (1, image_width))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    elif bg_source == BGSource.BOTTOM:
        gradient = np.linspace(32, 224, image_height)[:, None]
        image = np.tile(gradient, (1, image_width))
        input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
    else:
        raise 'Wrong background source!'

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

    fg = resize_and_center_crop(input_fg, image_width, image_height)
    bg = resize_and_center_crop(input_bg, image_width, image_height)
    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)

    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)

    latents = t2i_pipe(
        prompt_embeds=conds,
        negative_prompt_embeds=unconds,
        width=image_width,
        height=image_height,
        num_inference_steps=steps,
        num_images_per_prompt=num_samples,
        generator=rng,
        output_type='latent',
        guidance_scale=cfg,
        cross_attention_kwargs={'concat_conds': concat_conds},
    ).images.to(vae.dtype) / vae.config.scaling_factor

    pixels = vae.decode(latents).sample
    pixels = pytorch2numpy(pixels)
    pixels = [resize_without_crop(
        image=p,
        target_width=int(round(image_width * highres_scale / 64.0) * 64),
        target_height=int(round(image_height * highres_scale / 64.0) * 64))
    for p in pixels]

    pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
    latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
    latents = latents.to(device=unet.device, dtype=unet.dtype)

    image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8
    fg = resize_and_center_crop(input_fg, image_width, image_height)
    bg = resize_and_center_crop(input_bg, image_width, image_height)
    concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype)
    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
    concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)

    latents = i2i_pipe(
        image=latents,
        strength=highres_denoise,
        prompt_embeds=conds,
        negative_prompt_embeds=unconds,
        width=image_width,
        height=image_height,
        num_inference_steps=int(round(steps / highres_denoise)),
        num_images_per_prompt=num_samples,
        generator=rng,
        output_type='latent',
        guidance_scale=cfg,
        cross_attention_kwargs={'concat_conds': concat_conds},
    ).images.to(vae.dtype) / vae.config.scaling_factor

    pixels = vae.decode(latents).sample
    pixels = pytorch2numpy(pixels, quant=False)

    clear_memory()
    return pixels, [fg, bg]


# Add save function
def save_images(images, prefix="relight"):
    # Create output directory if it doesn't exist
    output_dir = Path("outputs")
    output_dir.mkdir(exist_ok=True)
    
    # Create timestamp for unique filenames
    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
    
    saved_paths = []
    for i, img in enumerate(images):
        if isinstance(img, np.ndarray):
            # Convert to PIL Image if numpy array
            img = Image.fromarray(img)
        
        # Create filename with timestamp
        filename = f"{prefix}_{timestamp}_{i+1}.png"
        filepath = output_dir / filename
        
        # Save image
        img.save(filepath)


    # print(f"Saved {len(saved_paths)} images to {output_dir}")
    return saved_paths


# Modify process_relight to save images
@torch.inference_mode()
def process_relight(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
    input_fg, matting = run_rmbg(input_fg)
    # show input_fg in a new image 
    input_fg_img = Image.fromarray(input_fg)
    results, extra_images = process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source)
    results = [(x * 255.0).clip(0, 255).astype(np.uint8) for x in results]
    final_results = results + extra_images
    
    # Save the generated images
    save_images(results, prefix="relight")
    
    return results


# Modify process_normal to save images
@torch.inference_mode()
def process_normal(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source):
    input_fg, matting = run_rmbg(input_fg, sigma=16)

    print('left ...')
    left = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.LEFT.value)[0][0]

    print('right ...')
    right = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.RIGHT.value)[0][0]

    print('bottom ...')
    bottom = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.BOTTOM.value)[0][0]

    print('top ...')
    top = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.TOP.value)[0][0]

    inner_results = [left * 2.0 - 1.0, right * 2.0 - 1.0, bottom * 2.0 - 1.0, top * 2.0 - 1.0]

    ambient = (left + right + bottom + top) / 4.0
    h, w, _ = ambient.shape
    matting = resize_and_center_crop((matting[..., 0] * 255.0).clip(0, 255).astype(np.uint8), w, h).astype(np.float32)[..., None] / 255.0

    def safa_divide(a, b):
        e = 1e-5
        return ((a + e) / (b + e)) - 1.0

    left = safa_divide(left, ambient)
    right = safa_divide(right, ambient)
    bottom = safa_divide(bottom, ambient)
    top = safa_divide(top, ambient)

    u = (right - left) * 0.5
    v = (top - bottom) * 0.5

    sigma = 10.0
    u = np.mean(u, axis=2)
    v = np.mean(v, axis=2)
    h = (1.0 - u ** 2.0 - v ** 2.0).clip(0, 1e5) ** (0.5 * sigma)
    z = np.zeros_like(h)

    normal = np.stack([u, v, h], axis=2)
    normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
    normal = normal * matting + np.stack([z, z, 1 - z], axis=2) * (1 - matting)

    results = [normal, left, right, bottom, top] + inner_results
    results = [(x * 127.5 + 127.5).clip(0, 255).astype(np.uint8) for x in results]


    # Save the generated images
    save_images(results, prefix="normal")
    
    return results




quick_prompts = [
    'modern sofa in living room',
    'elegant dining table with chairs',
    'luxurious bed in bedroom, cinematic lighting',
    'minimalist office desk, natural lighting',
    'vintage wooden cabinet, warm lighting',
    'contemporary bookshelf, ambient lighting',
    'designer armchair, dramatic lighting',
    'modern kitchen island, bright lighting',
]
quick_prompts = [[x] for x in quick_prompts]


class BGSource(Enum):
    UPLOAD = "Use Background Image"
    UPLOAD_FLIP = "Use Flipped Background Image"
    LEFT = "Left Light"
    RIGHT = "Right Light"
    TOP = "Top Light"
    BOTTOM = "Bottom Light"
    GREY = "Ambient"


class MaskMover:
    def __init__(self):
        self.extracted_fg = None
        self.original_fg = None  # Store original foreground
        
    def set_extracted_fg(self, fg_image):
        """Store the extracted foreground with alpha channel"""
        self.extracted_fg = fg_image.copy()
        self.original_fg = fg_image.copy()  # Keep original
        return fg_image
    
    def create_composite(self, background, x_pos, y_pos, scale=1.0):
        """Create composite with foreground at specified position"""
        if self.original_fg is None or background is None:
            return background
        
        # Convert inputs to PIL Images
        if isinstance(background, np.ndarray):
            bg = Image.fromarray(background)
        else:
            bg = background
        
        if isinstance(self.original_fg, np.ndarray):
            fg = Image.fromarray(self.original_fg)
        else:
            fg = self.original_fg
        
        # Scale the foreground size
        new_width = int(fg.width * scale)
        new_height = int(fg.height * scale)
        fg = fg.resize((new_width, new_height), Image.LANCZOS)
        
        # Center the scaled foreground at the position
        x = int(x_pos - new_width / 2)
        y = int(y_pos - new_height / 2)
        
        # Create composite
        result = bg.copy()
        if fg.mode == 'RGBA':  # If foreground has alpha channel
            result.paste(fg, (x, y), fg.split()[3])  # Use alpha channel as mask
        else:
            result.paste(fg, (x, y))
        
        return np.array(result)
    
def get_depth(image):
    if image is None:
        return None
    # Convert from PIL/gradio format to cv2
    raw_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    # Get depth map
    depth = model.infer_image(raw_img) # HxW raw depth map
    # Normalize depth for visualization
    depth = ((depth - depth.min()) / (depth.max() - depth.min()) * 255).astype(np.uint8)
    # Convert to RGB for display
    depth_colored = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
    depth_colored = cv2.cvtColor(depth_colored, cv2.COLOR_BGR2RGB)
    return Image.fromarray(depth_colored)

# def find_objects(image_input):
#     detections = run_sam_inference(SAM_IMAGE_MODEL, image_input, detections)
#     if len(detections) == 0:
#         gr.Info("No objects detected.")
#         return None
#     return Image.fromarray(detections.mask[0].astype("uint8") * 255)



block = gr.Blocks().queue()
with block:
    mask_mover = MaskMover()
    
    with gr.Row():
        gr.Markdown("## IC-Light (Relighting with Foreground and Background Condition)")
        gr.Markdown("💾 Generated images are automatically saved to 'outputs' folder")
    
    with gr.Row():
        with gr.Column():
            # Step 1: Input and Extract
            with gr.Group():
                gr.Markdown("### Step 1: Extract Foreground")
                input_image = gr.Image(type="numpy", label="Input Image", height=480)
                input_text = gr.Textbox(label="Describe target object")

                find_objects_button = gr.Button(value="Find Objects")
                extract_button = gr.Button(value="Remove Background")
                extracted_fg = gr.Image(type="numpy", label="Extracted Foreground", height=480)

            
            # Step 2: Background and Position
            with gr.Group():
                gr.Markdown("### Step 2: Position on Background")
                input_bg = gr.Image(type="numpy", label="Background Image", height=480)
                
                with gr.Row():
                    x_slider = gr.Slider(
                        minimum=0,
                        maximum=1000,
                        label="X Position",
                        value=500,
                        visible=False
                    )
                    y_slider = gr.Slider(
                        minimum=0,
                        maximum=1000,
                        label="Y Position",
                        value=500,
                        visible=False
                    )
                    fg_scale_slider = gr.Slider(
                        label="Foreground Scale", 
                        minimum=0.01, 
                        maximum=3.0, 
                        value=1.0, 
                        step=0.01
                    )

                    get_depth_button = gr.Button(value="Get Depth")

                    depth_image = gr.Image(type="numpy", label="Depth Image", height=480)

                
                editor = gr.ImageEditor(
                    type="numpy",
                    label="Position Foreground",
                    height=480,
                    visible=False
                )
            
            # Step 3: Relighting Options
            with gr.Group():
                gr.Markdown("### Step 3: Relighting Settings")
                prompt = gr.Textbox(label="Prompt")
                bg_source = gr.Radio(
                    choices=[e.value for e in BGSource],
                    value=BGSource.UPLOAD.value,
                    label="Background Source", 
                    type='value'
                )

                example_prompts = gr.Dataset(
                    samples=quick_prompts, 
                    label='Prompt Quick List', 
                    components=[prompt]
                )
                # bg_gallery = gr.Gallery(
                #     height=450, 
                #     label='Background Quick List', 
                #     value=db_examples.bg_samples, 
                #     columns=5, 
                #     allow_preview=False
                # )
            relight_button = gr.Button(value="Relight")

            # Additional settings
            with gr.Group():
                with gr.Row():
                    num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                    seed = gr.Number(label="Seed", value=12345, precision=0)
                with gr.Row():
                    image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64)
                    image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64)

            with gr.Accordion("Advanced options", open=False):
                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01)
                highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=2.0, value=1.2, step=0.01)
                highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01)
                a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
                n_prompt = gr.Textbox(
                    label="Negative Prompt",
                    value='lowres, bad anatomy, bad hands, cropped, worst quality'
                )
                normal_button = gr.Button(value="Compute Normal (4x Slower)")
        
        with gr.Column():
            result_gallery = gr.Image(height=832, label='Outputs')

    # Event handlers
    def extract_foreground(image):
            if image is None:
                return None, gr.update(visible=True), gr.update(visible=True)
            result, rgba = run_rmbg(image)
            mask_mover.set_extracted_fg(rgba)
            
            return result, gr.update(visible=True), gr.update(visible=True)

    original_bg = None

    extract_button.click(
            fn=extract_foreground,
            inputs=[input_image],
            outputs=[extracted_fg, x_slider, y_slider]
        )
    
    find_objects_button.click(
        fn=process_image,
        inputs=[input_image, input_text],
        outputs=[extracted_fg]
    )

    get_depth_button.click(
            fn=get_depth,
            inputs=[input_bg],
            outputs=[depth_image]
        )

    def update_position(background, x_pos, y_pos, scale):
        """Update composite when position changes"""
        global original_bg
        if background is None:
            return None
        
        if original_bg is None:
            original_bg = background.copy()
        
        # Convert string values to float
        x_pos = float(x_pos)
        y_pos = float(y_pos)
        scale = float(scale)
        
        return mask_mover.create_composite(original_bg, x_pos, y_pos, scale)

    x_slider.change(
        fn=update_position,
        inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
        outputs=[input_bg]
    )

    y_slider.change(
        fn=update_position,
        inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
        outputs=[input_bg]
    )

    fg_scale_slider.change(
        fn=update_position,
        inputs=[input_bg, x_slider, y_slider, fg_scale_slider],
        outputs=[input_bg]
    )

    # Update inputs list to include fg_scale_slider
    ips = [input_bg, input_bg, prompt, image_width, image_height, num_samples, 
           seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, 
           bg_source, x_slider, y_slider, fg_scale_slider]  # Added fg_scale_slider

    def process_relight_with_position(*args):
        if mask_mover.extracted_fg is None:
            gr.Warning("Please extract foreground first")
            return None
            
        background = args[1]  # Get background image
        x_pos = float(args[-3])  # x_slider value
        y_pos = float(args[-2])  # y_slider value
        scale = float(args[-1])  # fg_scale_slider value
        
        # Get original foreground size after scaling
        fg = Image.fromarray(mask_mover.original_fg)
        new_width = int(fg.width * scale)
        new_height = int(fg.height * scale)
        
        # Calculate crop region around foreground position
        crop_x = int(x_pos - new_width/2)
        crop_y = int(y_pos - new_height/2)
        crop_width = new_width
        crop_height = new_height
        
        # Add padding for context (20% extra on each side)
        padding = 0.2
        crop_x = int(crop_x - crop_width * padding)
        crop_y = int(crop_y - crop_height * padding)
        crop_width = int(crop_width * (1 + 2 * padding))
        crop_height = int(crop_height * (1 + 2 * padding))
        
        # Ensure crop dimensions are multiples of 8
        crop_width = ((crop_width + 7) // 8) * 8
        crop_height = ((crop_height + 7) // 8) * 8
        
        # Ensure crop region is within image bounds
        bg_height, bg_width = background.shape[:2]
        crop_x = max(0, min(crop_x, bg_width - crop_width))
        crop_y = max(0, min(crop_y, bg_height - crop_height))
        
        # Get actual crop dimensions after boundary check
        crop_width = min(crop_width, bg_width - crop_x)
        crop_height = min(crop_height, bg_height - crop_y)
        
        # Ensure dimensions are multiples of 8 again
        crop_width = (crop_width // 8) * 8
        crop_height = (crop_height // 8) * 8
        
        # Crop region from background
        crop_region = background[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width]
        
        # Create composite in cropped region
        fg_local_x = int(new_width/2 + crop_width*padding)
        fg_local_y = int(new_height/2 + crop_height*padding)
        cropped_composite = mask_mover.create_composite(crop_region, fg_local_x, fg_local_y, scale)
        
        # Process the cropped region
        crop_args = list(args)
        crop_args[0] = cropped_composite
        crop_args[1] = crop_region
        crop_args[3] = crop_width
        crop_args[4] = crop_height
        crop_args = crop_args[:-3]  # Remove position and scale arguments
        
        # Get relit result
        relit_crop = process_relight(*crop_args)[0]
        
        # Resize relit result to match crop dimensions if needed
        if relit_crop.shape[:2] != (crop_height, crop_width):
            relit_crop = resize_without_crop(relit_crop, crop_width, crop_height)
        
        # Place relit crop back into original background
        result = background.copy()
        result[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width] = relit_crop
        
        return result

    # Update button click events with new inputs list
    relight_button.click(
        fn=process_relight_with_position,
        inputs=ips,
        outputs=[result_gallery]
    )
    
    # Update normal_button to use same argument handling
    def process_normal_with_position(*args):
        if mask_mover.extracted_fg is None:
            gr.Warning("Please extract foreground first")
            return None
            
        background = args[1]
        x_pos = float(args[-3])  # x_slider value
        y_pos = float(args[-2])  # y_slider value
        scale = float(args[-1])  # fg_scale_slider value
        
        # Get original foreground size after scaling
        fg = Image.fromarray(mask_mover.original_fg)
        new_width = int(fg.width * scale)
        new_height = int(fg.height * scale)
        
        # Calculate crop region around foreground position
        crop_x = int(x_pos - new_width/2)
        crop_y = int(y_pos - new_height/2)
        crop_width = new_width
        crop_height = new_height
        
        # Add padding for context (20% extra on each side)
        padding = 0.2
        crop_x = int(crop_x - crop_width * padding)
        crop_y = int(crop_y - crop_height * padding)
        crop_width = int(crop_width * (1 + 2 * padding))
        crop_height = int(crop_height * (1 + 2 * padding))
        
        # Ensure crop dimensions are multiples of 8
        crop_width = ((crop_width + 7) // 8) * 8
        crop_height = ((crop_height + 7) // 8) * 8
        
        # Ensure crop region is within image bounds
        bg_height, bg_width = background.shape[:2]
        crop_x = max(0, min(crop_x, bg_width - crop_width))
        crop_y = max(0, min(crop_y, bg_height - crop_height))
        
        # Crop region from background
        crop_region = background[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width]
        
        # Create composite in cropped region
        fg_local_x = int(new_width/2 + crop_width*padding)
        fg_local_y = int(new_height/2 + crop_height*padding)
        cropped_composite = mask_mover.create_composite(crop_region, fg_local_x, fg_local_y, scale)
        
        # Process the cropped region
        crop_args = list(args)
        crop_args[0] = cropped_composite
        crop_args[1] = crop_region
        crop_args[3] = crop_width
        crop_args[4] = crop_height
        crop_args = crop_args[:-3]
        
        # Get processed result
        processed_crop = process_normal(*crop_args)
        
        # Place processed crop back into original background
        result = background.copy()
        result[crop_y:crop_y+crop_height, crop_x:crop_x+crop_width] = processed_crop
        
        return result[0] if result else None

    normal_button.click(
        fn=process_normal_with_position,
        inputs=ips,
        outputs=[result_gallery]
    )

    example_prompts.click(
        fn=lambda x: x[0],
        inputs=example_prompts,
        outputs=prompt,
        show_progress=False,
        queue=False
    )

    # def bg_gallery_selected(gal, evt: gr.SelectData):
    #     return gal[evt.index]['name']

    # bg_gallery.select(
    #     fn=bg_gallery_selected,
    #     inputs=bg_gallery,
    #     outputs=input_bg
    # )

block.launch(server_name='0.0.0.0')