import torch
import gradio as gr
import spaces
import random
import numpy as np

from pipeline import ChatsSDXLPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import CLIPFeatureExtractor
from diffusers.utils import logging
from PIL import Image

logging.set_verbosity_error()

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MAX_SEED = np.iinfo(np.int32).max

feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        safety_checker=safety_checker,
        feature_extractor=feature_extractor,
        torch_dtype=torch.bfloat16
)
pipe.to(DEVICE)

@spaces.GPU(duration=75)
def generate(prompt, seed=0, randomize_seed=False, steps=50, guidance_scale=5.0):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    print('inference with prompt : {}, seed : {}, step : {}, cfg : {}'.format(prompt, seed, steps, guidance_scale))
    output = pipe(
        prompt=prompt,
        num_inference_steps=steps,
        guidance_scale=guidance_scale,
        seed=seed
    )
    return output['images'][0]

examples = [
    "Solar punk vehicle in a bustling city",
    "An anthropomorphic cat riding a Harley Davidson in Arizona with sunglasses and a leather jacket",
    "An elderly woman poses for a high fashion photoshoot in colorful, patterned clothes with a cyberpunk 2077 vibe",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# CHATS-SDXL
SDXL diffusion models finetuned using preference optimization framework CHATS. [[paper](https://arxiv.org/pdf/2502.12579)] [[code](https://github.com/AIDC-AI/CHATS)] [[model](https://huggingface.co/AIDC-AI/CHATS)]
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt here",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        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=False)
                        
            with gr.Row():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=14,
                    step=0.1,
                    value=5.0,
                )
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=50,
                )
        
        gr.Examples(
            examples = examples,
            fn = generate,
            inputs = [prompt],
            outputs = [result],
            cache_examples="lazy"
        )

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

if __name__ == '__main__':
    demo.launch()