from diffusers import DiffusionPipeline
import gradio as gr
import numpy as np
import imageio
from PIL import Image
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained(/*udaleno*/, torch_dtype=torch.float32)
pipe.to(device)

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 predict(prompt, negative_prompt):
    image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=25, width=768, height=768).images[0]
    return image

title="i 768x768"
description="test"
gr.Interface(fn=predict, inputs=[
    #gr.Image(source="upload", type="numpy", tool="sketch", elem_id="source_container"),
    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')],
    outputs='image',
    title=title,
    description=description,
    article = " "
).launch(max_threads=True, debug=True)