import inspect
from typing import List, Optional, Union
import numpy as np
import torch
import PIL
import gradio as gr
from diffusers import StableDiffusionInpaintPipeline
from rembg import remove

def greet(name):
    img_url = "https://cdn.faire.com/fastly/893b071985d70819da5f0d485f1b1bb97ee4f16a6e14ef1bdd4a086b3588be58.png" # wino
    image = download_image(img_url).resize((512, 512))
    inverted_mask_image = remove(data = image, only_mask = True)
    mask_image = PIL.ImageOps.invert(inverted_mask_image)

    return "Hello " + name + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()

# def image_grid(imgs, rows, cols):
#     assert len(imgs) == rows*cols

#     w, h = imgs[0].size
#     grid = PIL.Image.new('RGB', size=(cols*w, rows*h))
#     grid_w, grid_h = grid.size
    
#     for i, img in enumerate(imgs):
#         grid.paste(img, box=(i%cols*w, i//cols*h))
#     return grid

def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")

# def predict(dict, prompt):
#   image =  dict['image'].convert("RGB").resize((512, 512))
#   mask_image = dict['mask'].convert("RGB").resize((512, 512))
#   images = pipe(prompt=prompt, image=image, mask_image=mask_image).images
#   return(images[0])