import gradio as gr
from monai.utils import first, set_determinism
from monai.transforms import(
    Compose,
    AddChanneld,
    LoadImaged,
    Resized,
    ToTensord,
    Spacingd,
    Orientationd,
    ScaleIntensityRanged,
    CropForegroundd,
    Activations,
)

from monai.networks.nets import UNet
from monai.networks.layers import Norm
from monai.data import CacheDataset, DataLoader, Dataset

import torch
import matplotlib.pyplot as plt

import os
from glob import glob
import numpy as np

from monai.inferers import sliding_window_inference

in_dir = 'Data_Train_Test'
model_dir = ''

train_loss = np.load(os.path.join(model_dir, 'loss_train.npy'))
train_metric = np.load(os.path.join(model_dir, 'metric_train.npy'))
test_loss = np.load(os.path.join(model_dir, 'loss_test.npy'))
test_metric = np.load(os.path.join(model_dir, 'metric_test.npy'))





def segment(image):
    return "liverseg.png"

gr.Interface(fn=segment, inputs="image", outputs="image", examples = ["liver.png"]).launch()