from PIL import Image
import requests
import os

from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
from diffusers import DiffusionPipeline
import torch
from torch import autocast

import gradio as gr

auth_token = os.environ.get("API_TOKEN") or True

url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)

processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

pipe = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    custom_pipeline="text_inpainting",
    segmentation_model=model,
    segmentation_processor=processor,
    use_auth_token=auth_token,
)

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)

def pad_image(image):
    w, h = image.size
    if w == h:
        return image
    elif w > h:
        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
        new_image.paste(image, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
        new_image.paste(image, ((h - w) // 2, 0))
        return new_image


def process_image(image, text, prompt):
  image = pad_image(image)
  image = image.resize((512, 512))
  with autocast("cuda"):
      inpainted_image = pipe(image=image, text=text, prompt=prompt).images[0]
  return inpainted_image
 

title = "Interactive demo: Text-based inpainting with CLIPSeg x Stable Diffusion"
description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. This model can be used to segment things in an image based on text. This way, one can use it to provide a binary mask for Stable Diffusion, which the latter needs to inpaint. To use it, simply upload an image and add a text to mask as well as a text which indicates what to replace, or use one of the examples below and click 'submit'. Results will show up in a few seconds."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>"

examples = [["example_image.png", "a glass", "a cup"]]
   
interface = gr.Interface(fn=process_image, 
                     inputs=[gr.Image(type="pil"), gr.Textbox(label="What's the thing you want to replace?"), gr.Textbox(label="What do you want as replacement?")], 
                     outputs=gr.Image(type="pil"),
                     title=title,
                     description=description,
                     article=article,
                     examples=examples)
                     
interface.launch(debug=True)