import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline #, StableDiffusion3Pipeline
from huggingface_hub import hf_hub_download

device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.cuda.max_memory_allocated(device=device)
torch.cuda.empty_cache()

def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
       
    if Model == "PhotoReal":
        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1")
        pipe.enable_xformers_memory_efficient_attention()
        pipe = pipe.to(device)
        torch.cuda.empty_cache()
    
        image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
        torch.cuda.empty_cache()
        return image
    
    if Model == "Animagine XL 4":
        animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0")
        animagine.enable_xformers_memory_efficient_attention()
        animagine = animagine.to(device)
        torch.cuda.empty_cache()
        
        image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
        torch.cuda.empty_cache()
        return image

    
    return image
    
gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 4',], value='PhotoReal', label='Choose Model'),
                               gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                               gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
                               gr.Slider(512, 1024, 768, step=128, label='Height'),
                               gr.Slider(512, 1024, 768, step=128, label='Width'),
                               gr.Slider(3, maximum=12, value=5, step=.25, label='Guidance Scale', info="5-7 for PhotoReal and 7-10 for Animagine"), 
                               gr.Slider(25, maximum=50, value=25, step=25, label='Number of Iterations'), 
                               gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'), 
                               ],
             outputs=gr.Image(label='Generated Image'), 
             title="Manju Dream Booth V2.5 - GPU", 
             description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
             article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: DL5qRkGCzB2ENBKfEhHarvKm1qas3wyHx7<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)