app.py

import gradio as gr
import numpy as np
import cv2


def run(img: np.ndarray, thres: int) -> tuple[np.ndarray, int | str]:
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    if thres > 0:
        _, img = cv2.threshold(img, thres, 255, cv2.THRESH_BINARY)
        threshold = thres
    elif thres < 0:
        img = cv2.adaptiveThreshold(
            img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, -thres
        )
        threshold = "adaptive"
    else:
        threshold, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

    return img, threshold


app = gr.Interface(
    fn=run,
    inputs=[
        gr.Image(label="image"),
        gr.Slider(
            -30,
            255,
            -2,
            label="threshold",
            info="0 for Otsu's method, negative for adaptive thresholding",
        ),
    ],
    outputs=[
        gr.Image(label="processed"),
        gr.Label(label="threshold"),
    ],
    allow_flagging="never",
)

app.queue().launch()