#!/usr/bin/env python
import os
import random
import uuid
import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

css = '''
.gradio-container{max-width: 570px !important}
h1{text-align:center}
'''

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "Chocolate dripping from a donut against a yellow background, 8k",
    "Illustration of A starry night camp in the mountains, 4k, cinematic --ar 85:128 --v 6.0 --style raw",
    "A photo of a lavender cat, hdr, 4k, --ar 85:128 --v 6.0 --style raw",
    "A delicious ceviche cheesecake slice, 4k, octane render, ray tracing, Ultra-High-Definition"
]

MODEL_OPTIONS = {
    "Lightning": "SG161222/RealVisXL_V4.0_Lightning"
}

MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

def load_and_prepare_model(model_id):
    pipe = StableDiffusionXLPipeline.from_pretrained(
        model_id,
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    
    if USE_TORCH_COMPILE:
        pipe.compile()
    
    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    
    return pipe

# Preload and compile both models
models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}

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

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".webp"
    img.save(unique_name, quality=90)
    return unique_name

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

@spaces.GPU(duration=60, enable_queue=True)
def generate(
    model_choice: str,
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 1,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    num_inference_steps: int = 25,
    randomize_seed: bool = False,
    use_resolution_binning: bool = True, 
    num_images: int = 1,  
    progress=gr.Progress(track_tqdm=True),
):
    global models
    pipe = models[model_choice]
    
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator(device=device).manual_seed(seed)

    options = {
        "prompt": [prompt] * num_images,
        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
        "width": width,
        "height": height,
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps,
        "generator": generator,
        "output_type": "pil",
    }

    if use_resolution_binning:
        options["use_resolution_binning"] = True

    images = []
    for i in range(0, num_images, BATCH_SIZE):
        batch_options = options.copy()
        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
        if "negative_prompt" in batch_options:
            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
        images.extend(pipe(**batch_options).images)

    image_paths = [save_image(img) for img in images]
    return image_paths, seed

#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",
     #   "assets/10.png",
   #     "assets/11.png",
 #       "assets/12.png",
 #   ]
  #  return predefined_images


with gr.Blocks(css=css) as demo:
    gr.Markdown(
            f"""
        # Text🥠Image
        Models used in the playground [[Lightning]](https://huggingface.co/SG161222/RealVisXL_V4.0_Lightning), [[Realvision]](https://huggingface.co/) ,[[Turbo]](https://huggingface.co/SG161222/RealVisXL_V3.0_Turbo) for image generation. stable diffusion xl piped (sdxl) model HF. This is the demo space for generating images using the Stable Diffusion XL models, with multi different variants available. ⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.
        """
        )
    with gr.Row():
        prompt = gr.Text(
            label="Prompt",
            show_label=False,
            max_lines=1,
            placeholder="Enter your prompt",
            container=False,
        )
        run_button = gr.Button("Run⚡", scale=0)
    result = gr.Gallery(label="Result", columns=1, show_label=False) 

    with gr.Row():
        model_choice = gr.Dropdown(
            label="Model Selection",
            choices=list(MODEL_OPTIONS.keys()),
            value="Lightning"
        )

    with gr.Accordion("Advanced options", open=True, visible=False):
        num_images = gr.Slider(
            label="Number of Images",
            minimum=1,
            maximum=1,
            step=1,
            value=1,
        )
        with gr.Row():
            with gr.Column(scale=1):
                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=5,
                    lines=4,
                    placeholder="Enter a negative prompt",
                    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",
                    visible=True,
                )
        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():
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=6,
                step=0.01,
                value=3.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=35,
                step=1,
                value=20,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        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=[
            model_choice,
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            randomize_seed,
            num_images
        ],
        outputs=[result, seed],
        api_name="run",
    )
 #   with gr.Column(scale=3):
    #    gr.Markdown("### Image Gallery")
     #   predefined_gallery = gr.Gallery(label="Image Gallery", columns=4, show_label=False, value=load_predefined_images())       
if __name__ == "__main__":
    demo.queue(max_size=20).launch(show_api=True, share=True, server_port=7860)