import gradio as gr
#import torch
#from torch import autocast // only for GPU

from PIL import Image
import numpy as np
from io import BytesIO
import os
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')

#from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline

print("hello sylvain")

YOUR_TOKEN=MY_SECRET_TOKEN

device="cpu"

#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
#prompt_pipe.to(device)

img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
img_pipe.to(device)

source_img = gr.ImagePaint(type="filepath", elem_id="source_container", label="new gradio color sketch (beta)")

gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")

def resize(value,img):
  #baseheight = value
  img = Image.open(img)
  #hpercent = (baseheight/float(img.size[1]))
  #wsize = int((float(img.size[0])*float(hpercent)))
  #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
  img = img.resize((value,value), Image.Resampling.LANCZOS)
  return img


def infer(source_img, prompt): 
         
    source_image = resize(512, source_img)
    source_image.save('source.png')
    images_list = img_pipe([prompt] * 1, init_image=source_image, strength=0.75)
    images = []
    safe_image = Image.open(r"unsafe.png")
    for i, image in enumerate(images_list["sample"]):
        if(images_list["nsfw_content_detected"][i]):
            images.append(safe_image)
        else:
            images.append(image)    
    return images

print("Great sylvain ! Everything is working fine !")

title="Touch of Paint Stable Diffusion CPU"
description="Img-2-Img Stable Diffusion example using CPU and the beta color-sketch on uploaded gradio tool. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" 
custom_css = "style.css"

gr.Interface(fn=infer, inputs=[source_img, "text"], outputs=gallery,title=title,description=description,css=custom_css).queue(max_size=100).launch(enable_queue=True)