app.py

import gradio as gr
import io
from PIL import Image
import numpy as np

from config import WIDTH, HEIGHT
from models import make_image_controlnet, make_inpainting
from preprocessing import preprocess_seg_mask, get_image, get_mask

def image_to_byte_array(image: Image) -> bytes:
    # BytesIO is a fake file stored in memory
    imgByteArr = io.BytesIO()
    # image.save expects a file as a argument, passing a bytes io ins
    image.save(imgByteArr, format='png')  # image.format
    # Turn the BytesIO object back into a bytes object
    imgByteArr = imgByteArr.getvalue()
    return imgByteArr

def predict(input_img1,
            input_img2,
            positive_prompt,
            negative_prompt
            ):

    print("predict")
    input_img1 = Image.fromarray(input_img1)
    input_img2 = Image.fromarray(input_img2)

    input_img1 = input_img1.resize((WIDTH, HEIGHT))
    input_img2 = input_img2.resize((WIDTH, WIDTH))

    canvas_mask = np.array(input_img2)
    mask = get_mask(canvas_mask)

    print(input_img1, mask)

    result_image = make_inpainting(positive_prompt=positive_prompt,
                                   image=input_img1,
                                   mask_image=mask,
                                   negative_prompt=negative_prompt,
                                   )

    return result_image

def test1(param1):
    return  "here !"

gr.Interface(
    predict,
    inputs=[gr.Image(label="img", sources=['upload', 'webcam'], type="numpy"),
            gr.Image(label="mask", sources=['upload', 'webcam'], type="numpy"),
            gr.Textbox(label="positive_prompt"),
            gr.Textbox(label="negative_prompt")
            ],
    outputs= gr.Image(label="resp"),
    title="rem fur 1",
).launch(share=True)
#
#
# gr.Interface(
#     test1,
#     inputs=[gr.Textbox(label="param1")],
#     outputs= gr.Textbox(label="result"),
#     title="rem fur 1",
# ).launch(share=True)