import os
import random
import uuid
from typing import Tuple
import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

# Description for the Gradio interface
DESCRIPTIONz = """## SDXL-LoRA-DLC ⚡
"""

# Function to save generated images
def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

# Function to handle seed randomization
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

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

# Warning if running on CPU
if not torch.cuda.is_available():
    DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU. If it runs for an extended time or if you encounter errors, try running it on a GPU by duplicating the space using @spaces.GPU(). +import spaces.📍</p>"

# Configuration flags (unchanged)
USE_TORCH_COMPILE = 0
ENABLE_CPU_OFFLOAD = 0

# Define available base models
base_models = {
    "RealVisXL V4.0 Lightning": "SG161222/RealVisXL_V4.0_Lightning",
    "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
}

# Global variables to manage the current pipeline
current_base_model = None
current_pipeline = None

# Define LoRA options
LORA_OPTIONS = {
    "Realism (face/character)👦🏻": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"),
    "Pixar (art/toons)🙀": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"),
    "Photoshoot (camera/film)📸": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"),
    "Clothing (hoodies/pant/shirts)👔": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"),
    "Interior Architecture (house/hotel)🏠": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1δ.safetensors", "arch"),
    "Fashion Product (wearing/usable)👜": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"),
    "Minimalistic Image (minimal/detailed)🏞️": ("prithivMLmods/Pegasi-Minimalist-Image-Style", "Pegasi-Minimalist-Image-Style.safetensors", "minimalist"),
    "Modern Clothing (trend/new)👕": ("prithivMLmods/Canopus-Modern-Clothing-Design", "Canopus-Modern-Clothing-Design.safetensors", "mdrnclth"),
    "Animaliea (farm/wild)🫎": ("prithivMLmods/Canopus-Animaliea-Artism", "Canopus-Animaliea-Artism.safetensors", "Animaliea"),
    "Liquid Wallpaper (minimal/illustration)🖼️": ("prithivMLmods/Canopus-Liquid-Wallpaper-Art", "Canopus-Liquid-Wallpaper-Minimalize-LoRA.safetensors", "liquid"),
    "Canes Cars (realistic/futurecars)🚘": ("prithivMLmods/Canes-Cars-Model-LoRA", "Canes-Cars-Model-LoRA.safetensors", "car"),
    "Pencil Art (characteristic/creative)✏️": ("prithivMLmods/Canopus-Pencil-Art-LoRA", "Canopus-Pencil-Art-LoRA.safetensors", "Pencil Art"),
    "Art Minimalistic (paint/semireal)🎨": ("prithivMLmods/Canopus-Art-Medium-LoRA", "Canopus-Art-Medium-LoRA.safetensors", "mdm"),
}

# Define style options
style_list = [
    {
        "name": "3840 x 2160",
        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
    },
    {
        "name": "2560 x 1440",
        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
    },
    {
        "name": "HD+",
        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
    },
    {
        "name": "Style Zero",
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
]

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
DEFAULT_STYLE_NAME = "3840 x 2160"
STYLE_NAMES = list(styles.keys())

# Function to apply selected style
def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
    if style_name in styles:
        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    else:
        p, n = styles[DEFAULT_STYLE_NAME]
    if not negative:
        negative = ""
    return p.replace("{prompt}", positive), n + negative

# Generation function with model selection
@spaces.GPU(duration=180, enable_queue=True)
def generate(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    style_name: str = DEFAULT_STYLE_NAME,
    lora_model: str = "Realism (face/character)👦🏻",
    base_model: str = "RealVisXL V5.0 Lightning",
    progress=gr.Progress(track_tqdm=True),
):
    global current_base_model, current_pipeline

    # Load the pipeline if the base model has changed
    if base_model != current_base_model:
        model_id = base_models[base_model]
        current_pipeline = StableDiffusionXLPipeline.from_pretrained(
            model_id, torch_dtype=torch.float16, use_safetensors=True
        )
        current_pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
            current_pipeline.scheduler.config
        )
        for lora_display_name, (lora_model, lora_weight, adapter_name) in LORA_OPTIONS.items():
            current_pipeline.load_lora_weights(
                lora_model, weight_name=lora_weight, adapter_name=adapter_name
            )
        current_pipeline.to("cuda")
        current_base_model = base_model

    # Handle seed and prompts
    seed = int(randomize_seed_fn(seed, randomize_seed))
    positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt)
    if not use_negative_prompt:
        effective_negative_prompt = ""

    # Set the LoRA adapter
    _, _, adapter_name = LORA_OPTIONS[lora_model]
    current_pipeline.set_adapters(adapter_name)

    # Generate the image
    images = current_pipeline(
        prompt=positive_prompt,
        negative_prompt=effective_negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=20,
        num_images_per_prompt=1,
        cross_attention_kwargs={"scale": 0.65},
        output_type="pil",
    ).images
    image_paths = [save_image(img) for img in images]
    return image_paths, seed

# Example prompts
examples = [
    "Realism: Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational ",
    "Pixar: A young man with light brown wavy hair and light brown eyes sitting in an armchair and looking directly at the camera, pixar style, disney pixar, office background, ultra detailed, 1 man",
    "Hoodie: Front view, capture a urban style, Superman Hoodie, technical materials, fabric small point label on text Blue theory, the design is minimal, with a raised collar, fabric is a Light yellow, low angle to capture the Hoodies form and detailing, f/5.6 to focus on the hoodies craftsmanship, solid grey background, studio light setting, with batman logo in the chest region of the t-shirt",
]

# CSS styling
css = '''
.gradio-container{max-width: 545px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

# Function to load predefined images
def load_predefined_images():
    predefined_images = [
        "assets/1.png",
        "assets/2.png",
        "assets/3.png",
        "assets/4.png",
        "assets/5.png",
        "assets/6.png",
        "assets/7.png",
        "assets/8.png",
        "assets/9.png",
    ]
    return predefined_images

# Gradio interface
with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTIONz)
    with gr.Group():
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt with resp. tag!",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)

    with gr.Accordion("Advanced options", open=False, visible=False):
        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
        negative_prompt = gr.Text(
            label="Negative prompt",
            lines=4,
            max_lines=6,
            value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
            placeholder="Enter a negative prompt",
            visible=True,
        )
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            visible=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row(visible=True):
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=2048,
                step=8,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=2048,
                step=8,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=20.0,
                step=0.1,
                value=3.0,
            )
        style_selection = gr.Radio(
            show_label=True,
            container=True,
            interactive=True,
            choices=STYLE_NAMES,
            value=DEFAULT_STYLE_NAME,
            label="Quality Style",
        )

    # Add base model and LoRA selection dropdowns
    with gr.Row():
        base_model_choice = gr.Dropdown(
            label="Base Model",
            choices=list(base_models.keys()),
            value="RealVisXL V5.0 Lightning"
        )
        model_choice = gr.Dropdown(
            label="LoRA Selection",
            choices=list(LORA_OPTIONS.keys()),
            value="Realism (face/character)👦🏻"
        )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed],
        fn=generate,
        cache_examples=False,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            run_button.click,
        ],
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            randomize_seed,
            style_selection,
            model_choice,
            base_model_choice,
        ],
        outputs=[result, seed],
        api_name="run",
    )

    with gr.Column(scale=3):
        gr.Markdown("### Image Gallery")
        predefined_gallery = gr.Gallery(
            label="Image Gallery",
            columns=3,
            show_label=False,
            value=load_predefined_images()
        )

if __name__ == "__main__":
    demo.queue(max_size=30).launch()