Create codeforpredicting.py

codeforpredicting.py

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+import torch
+import torchvision
+from torchvision.transforms import transforms
+from PIL import Image
+
+model = torchvision.models.resnet50(pretrained=False)
+model.fc = torch.nn.Linear(in_features=2048, out_features=1)
+model.load_state_dict(torch.load('/content/zero_shot_classification_model.pth'))
+model.eval()
+
+test_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+test_image_path = '/content/test_img.png'  # Replace with your own test image path!
+test_image = Image.open(test_image_path).convert('RGB')
+test_image = test_transform(test_image)
+test_image = test_image.unsqueeze(0)
+
+with torch.no_grad():
+    prediction = model(test_image)
+
+probability = torch.sigmoid(prediction).item()
+
+print(f"The probability of the image being a Roblox character is: {probability*100}%")