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app.py

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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()