import gradio as gr
import numpy as np
import random
import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
import torch
from transformers import AutoModelForObjectDetection, AutoImageProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Your diffusion model

# Load your main diffusion pipeline
if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe.scheduler = DPMSolverSDEScheduler.from_config(pipe.scheduler.config, algorithm_type="dpmsolver++", solver_order=2, use_karras_sigmas=True)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# Load ADetailer model (from Hugging Face)
adetailer_model_id = "Bingsu/adetailer"
adetailer_model = AutoModelForObjectDetection.from_pretrained(adetailer_model_id)
adetailer_processor = AutoImageProcessor.from_pretrained(adetailer_model_id)

def fix_eyes_with_adetailer(image):
    # Convert image to format for ADetailer
    pixel_values = adetailer_processor(images=image, return_tensors="pt").pixel_values
    pixel_values = pixel_values.to(device)
    
    # Run ADetailer on the image
    with torch.no_grad():
        outputs = adetailer_model(pixel_values=pixel_values)
    
    # Post-process the outputs and apply the fixes (if any)
    corrected_image = image  # Placeholder for the actual post-processing
    # Apply fixes based on the detection and correction model outputs
    # This step requires actual ADetailer implementation details for correcting eyes.

    return corrected_image  # Return the corrected image

@spaces.GPU  #[uncomment to use ZeroGPU]
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height,
        generator=generator
    ).images[0] 

    # Apply ADetailer to fix eyes after generating the image
    corrected_image = fix_eyes_with_adetailer(image)
    
    return corrected_image, seed

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""#col-container {margin: 0 auto; max-width: 640px;}"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        """)
        
        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.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=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():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024, #Replace with defaults that work for your model
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024, #Replace with defaults that work for your model
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0, #Replace with defaults that work for your model
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2, #Replace with defaults that work for your model
                )
        
        gr.Examples(
            examples=examples,
            inputs=[prompt]
        )
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

demo.queue().launch()