import os

import cv2
import gradio as gr
import numpy as np
import onnxruntime as ort
from PIL import Image

_sess_options = ort.SessionOptions()
_sess_options.intra_op_num_threads = os.cpu_count()
MODEL_SESS = ort.InferenceSession(
    "cartoonizer.onnx", _sess_options, providers=["CPUExecutionProvider"]
)


def preprocess_image(image: Image) -> np.ndarray:
    image = np.array(image)
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    h, w, c = np.shape(image)
    if min(h, w) > 720:
        if h > w:
            h, w = int(720 * h / w), 720
        else:
            h, w = 720, int(720 * w / h)
    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
    h, w = (h // 8) * 8, (w // 8) * 8
    image = image[:h, :w, :]
    image = image.astype(np.float32) / 127.5 - 1
    return np.expand_dims(image, axis=0)


def inference(image: np.ndarray) -> Image:
    image = preprocess_image(image)
    results = MODEL_SESS.run(None, {"input_photo:0": image})
    output = (np.squeeze(results[0]) + 1.0) * 127.5
    output = np.clip(output, 0, 255).astype(np.uint8)
    output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
    return Image.fromarray(output)


title = "Generate cartoonized images"
article = "Demo of CartoonGAN model (https://systemerrorwang.github.io/White-box-Cartoonization/). \nDemo image is from https://unsplash.com/photos/f0SgAs27BYI."

iface = gr.Interface(
    inference,
    inputs=gr.inputs.Image(type="pil", label="Input Image"),
    outputs="image",
    title=title,
    article=article,
    allow_flagging="never",
    examples=[["mountain.jpeg"]],
)
iface.launch()