Update main.py

main.py

@@ -1,14 +1,14 @@
 import gradio as gr
-import torch
-import requests
-from torchvision import transforms
+# import torch
+# import requests
+# from torchvision import transforms
 import cv2
 from geti_sdk.deployment import Deployment
 from geti_sdk.utils import show_image_with_annotation_scene
 
-model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
+# model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
+# response = requests.get("https://git.io/JJkYN")
+# labels = response.text.split("\n")
 
 
 # Step 1: Load the deployment
@@ -37,12 +37,12 @@ def infer(image=None):
     return [output, prediction.overview]
 
 
-def predict(inp):
-    inp = transforms.ToTensor()(inp).unsqueeze(0)
-    with torch.no_grad():
-        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-        confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-    return confidences
+# def predict(inp):
+#     inp = transforms.ToTensor()(inp).unsqueeze(0)
+#     with torch.no_grad():
+#         prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+#         confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+#     return confidences
 
 
 def run():