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| 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() |