import torch
import torch.nn as nn
import numpy as np
from torchvision import models, transforms
import time
import os
import copy
import pickle
from PIL import Image
import datetime
import gdown
import urllib.request
import gradio as gr

url = 'https://drive.google.com/file/d/1GGnkicLQwgcgLq6sWClC_Igi8PWFQyV1'
#path_class_names = "./class_names_restnet_catsVSdogs.pkl"
gdown.download(url, path_class_names, quiet=False, use_cookies=False)

# Parameters
DATASET_PATH = '/content/APTOS2019'
PREP_PATH = DATASET_PATH + "/preprocessed/"
MODEL_PATH= '/content/APTOS2019/checkpoints/checkpoint_convnext.pth'

if not os.path.exists(PREP_PATH):
  os.mkdir(PREP_PATH)

if not os.path.exists("/checkpoints"):
  os.mkdir("/checkpoints")

def do_inference():
  return 0
      
title = "ConvNeXt for Diabetic Retinopathy Detection"
description = ""
#examples = [['./cat.jpg'],['./dog.jpg']]
#article="<p style='text-align: center'><a href='https://github.com/mawady/colab-recipes-cv' target='_blank'>Colab Recipes for Computer Vision - Dr. Mohamed Elawady</a></p>"
iface = gr.Interface(
    do_inference,  
    title=title,
    description=description,
)

iface.test_launch()

#iface.launch()