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.Image(source="upload", type="filepath", label="init_img")
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")

def resize(height,img):
  baseheight = height
  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)
  return img


def infer(prompt, source_img): 
    if(source_img != None):      
        init_image = resize(512,source_img)
        init_image.save('source.png')
        images_list = img_pipe([prompt] * 2, init_image=init_image, strength=0.75)
    else:       
        images_list = prompt_pipe([prompt] * 2)
    
    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="Stable Diffusion CPU"
description="Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" 

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