Update app.py

app.py

@@ -4,10 +4,10 @@ import torchvision.transforms as transforms
 import torchvision.models as models
 from PIL import Image
 
-# Load the ResNet18 model with pre-trained weights
+# Load ResNet18 model
 model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
 model.fc = torch.nn.Linear(model.fc.in_features, 2)  # Adjust for two classes
-model.eval()  # Set to evaluation mode
+model.eval()
 
 # Define image transformation
 transform = transforms.Compose([
@@ -25,6 +25,6 @@ def classify_image(image):
     _, predicted = torch.max(output, 1)
     return "Good Posture" if predicted.item() == 0 else "Bad Posture"
 
-# Set up Gradio interface for webcam or image upload
-iface = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil", source="upload"), outputs="text")
+# Set up Gradio interface
+iface = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="text")
 iface.launch()