import spaces

import gradio as gr
import numpy as np
import os
import random
import json
from PIL import Image
import torch
from torchvision import transforms
import zipfile
import cv2  # Added OpenCV import

from diffusers import FluxFillPipeline, AutoencoderKL
from PIL import Image
# from samgeo.text_sam import LangSAM

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# sam = LangSAM(model_type="sam2-hiera-large").to(device)

# Initialize vae model for 16-step encoding
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to("cuda")

pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")

with open("lora_models.json", "r") as f:
    lora_models = json.load(f)

def download_model(model_name, model_path):
    print(f"Downloading model: {model_name} from {model_path}")
    try:
        pipe.load_lora_weights(model_path)
        print(f"Successfully downloaded model: {model_name}")
    except Exception as e:
        print(f"Failed to download model: {model_name}. Error: {e}")

# Iterate through the models and download each one
for model_name, model_path in lora_models.items():
    download_model(model_name, model_path)

lora_models["None"] = None

def calculate_optimal_dimensions(image: Image.Image):
    # Extract the original dimensions
    original_width, original_height = image.size

    # Set constants
    MIN_ASPECT_RATIO = 9 / 16
    MAX_ASPECT_RATIO = 16 / 9
    FIXED_DIMENSION = 1024

    # Calculate the aspect ratio of the original image
    original_aspect_ratio = original_width / original_height

    # Determine which dimension to fix
    if original_aspect_ratio > 1:  # Wider than tall
        width = FIXED_DIMENSION
        height = round(FIXED_DIMENSION / original_aspect_ratio)
    else:  # Taller than wide
        height = FIXED_DIMENSION
        width = round(FIXED_DIMENSION * original_aspect_ratio)

    # Ensure dimensions are multiples of 8
    width = (width // 8) * 8
    height = (height // 8) * 8

    # Enforce aspect ratio limits
    calculated_aspect_ratio = width / height
    if calculated_aspect_ratio > MAX_ASPECT_RATIO:
        width = (height * MAX_ASPECT_RATIO // 8) * 8
    elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
        height = (width / MIN_ASPECT_RATIO // 8) * 8

    # Ensure width and height remain above the minimum dimensions
    width = max(width, 576) if width == FIXED_DIMENSION else width
    height = max(height, 576) if height == FIXED_DIMENSION else height

    return width, height

def process_unmasked_area(image, mask, blur_strength=25):
    """
    Process the unmasked portion of the image to remove context while preserving the masked area
    
    Args:
        image: PIL Image - the original input image
        mask: PIL Image - the mask with white (255) indicating the area to preserve
        blur_strength: int - strength of blur to apply to unmasked regions
    
    Returns:
        PIL Image with unmasked regions processed
    """
    # Convert PIL images to numpy arrays for OpenCV processing
    img_np = np.array(image)
    mask_np = np.array(mask)
    
    # Ensure mask is binary (0 and 255)
    _, mask_binary = cv2.threshold(mask_np, 127, 255, cv2.THRESH_BINARY)
    
    # Create inverted mask (255 in areas we want to process)
    mask_inv = cv2.bitwise_not(mask_binary)
    
    # Apply strong blur to remove context in unmasked areas
    blurred = cv2.GaussianBlur(img_np, (blur_strength, blur_strength), 0)
    
    # Create the processed image
    # Keep original pixels where mask is white (255)
    # Use blurred pixels where mask is black (0)
    processed_np = np.where(mask_binary[:, :, None] == 255, img_np, blurred)
    
    # Convert back to PIL image
    processed_image = Image.fromarray(processed_np)
    
    return processed_image

def vae_encode_16steps(image):
    """
    Encode image using the VAE with 16 steps
    
    Args:
        image: PIL Image to encode
    
    Returns:
        Encoded latent representation
    """
    # Convert PIL image to tensor
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5])
    ])
    
    image_tensor = transform(image).unsqueeze(0).to("cuda")
    
    # Encode with 16 steps
    with torch.no_grad():
        latent = vae.encode(image_tensor, num_inference_steps=16).latent_dist.sample()
        latent = latent * vae.config.scaling_factor
    
    return latent

@spaces.GPU(durations=300)
def infer(edit_images, prompt, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    # pipe.enable_xformers_memory_efficient_attention()
    gr.Info("Infering")

    if lora_model != "None":
        pipe.load_lora_weights(lora_models[lora_model])
        pipe.enable_lora()

    gr.Info("starting checks")

    image = edit_images["background"]
    mask = edit_images["layers"][0]

    if not image:
        gr.Info("Please upload an image.")
        return None, None

    width, height = calculate_optimal_dimensions(image)
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    # Process the unmasked portion to remove context
    processed_image = process_unmasked_area(image, mask)
    
    # Create latent encodings using VAE with 16 steps
    image_latent = vae_encode_16steps(processed_image)
    
    gr.Info("generating image")
    image = pipe(
        # Use the encoded image latent
        mask_image_latent=image_latent,
        prompt=prompt,
        prompt_2=prompt,
        image=processed_image,
        mask_image=mask,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        # strength=strength,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator(device='cuda').manual_seed(seed),
        # generator=torch.Generator().manual_seed(seed),
        # lora_scale=0.75 // not supported in this version
    ).images[0]

    output_image_jpg = image.convert("RGB")
    output_image_jpg.save("output.jpg", "JPEG")

    return output_image_jpg, seed
    # return image, seed

def download_image(image):
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    image.save("output.png", "PNG")
    return "output.png"

def save_details(result, edit_image, prompt, lora_model, strength, seed, guidance_scale, num_inference_steps):
    image = edit_image["background"]
    mask = edit_image["layers"][0]

    if isinstance(result, np.ndarray):
        result = Image.fromarray(result)
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    if isinstance(mask, np.ndarray):
        mask = Image.fromarray(mask)

    result.save("saved_result.png", "PNG")
    image.save("saved_image.png", "PNG")
    mask.save("saved_mask.png", "PNG")

    details = {
        "prompt": prompt,
        "lora_model": lora_model,
        "strength": strength,
        "seed": seed,
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps
    }

    with open("details.json", "w") as f:
        json.dump(details, f)

    # Create a ZIP file
    with zipfile.ZipFile("output.zip", "w") as zipf:
        zipf.write("saved_result.png")
        zipf.write("saved_image.png")
        zipf.write("saved_mask.png")
        zipf.write("details.json")

    return "output.zip"

def set_image_as_inpaint(image):
    return image

# def generate_mask(image, click_x, click_y):
#     text_prompt = "face"
#     mask = sam.predict(image, text_prompt, box_threshold=0.24, text_threshold=0.24)
#     return mask

examples = [
    "photography of a young woman,  accent lighting,  (front view:1.4),  "
    # "a tiny astronaut hatching from an egg on the moon",
    # "a cat holding a sign that says hello world",
    # "an anime illustration of a wiener schnitzel",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 1000px;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [dev]
        """)
        with gr.Row():
            with gr.Column():
                edit_image = gr.ImageEditor(
                    label='Upload and draw mask for inpainting',
                    type='pil',
                    sources=["upload", "webcam"],
                    image_mode='RGB',
                    layers=False,
                    brush=gr.Brush(colors=["#FFFFFF"]),
                    # height=600
                )
                prompt = gr.Text(
                    label="Prompt",
                    show_label=False,
                    max_lines=2,
                    placeholder="Enter your prompt",
                    container=False,
                )

                lora_model = gr.Dropdown(
                    label="Select LoRA Model",
                    choices=list(lora_models.keys()),
                    value="None",
                )

                run_button = gr.Button("Run")

            result = gr.Image(label="Result", show_label=False)

        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():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=30,
                    step=0.5,
                    value=50,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

            with gr.Row():

                strength = gr.Slider(
                    label="Strength",
                    minimum=0,
                    maximum=1,
                    step=0.01,
                    value=0.85,
                )

            #     width = gr.Slider(
            #         label="width",
            #         minimum=512,
            #         maximum=3072,
            #         step=1,
            #         value=1024,
            #     )

            #     height = gr.Slider(
            #         label="height",
            #         minimum=512,
            #         maximum=3072,
            #         step=1,
            #         value=1024,
            #     )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [edit_image, prompt, lora_model, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

    download_button = gr.Button("Download Image as PNG")
    set_inpaint_button = gr.Button("Set Image as Inpaint")
    save_button = gr.Button("Save Details")

    download_button.click(
            fn=download_image,
            inputs=[result],
            outputs=gr.File(label="Download Image")
        )

    set_inpaint_button.click(
            fn=set_image_as_inpaint,
            inputs=[result],
            outputs=[edit_image]
    )

    save_button.click(
            fn=save_details,
            inputs=[result, edit_image, prompt, lora_model, strength, seed, guidance_scale, num_inference_steps],
            outputs=gr.File(label="Download/Save Status")
    )

    # edit_image.select(
    #     fn=generate_mask,
    #     inputs=[edit_image, gr.Number(), gr.Number()],
    #     outputs=[edit_image]
    # )

# demo.launch()
PASSWORD = os.getenv("GRADIO_PASSWORD")
USERNAME = os.getenv("GRADIO_USERNAME")
# Create an authentication object
def authenticate(username, password):
    if username == USERNAME and password == PASSWORD:
        return True

    else:
        return False
# Launch the app with authentication

demo.launch(debug=True, auth=authenticate)