Update app.py

app.py

@@ -5,11 +5,11 @@ from torchvision.models import resnet18, ResNet18_Weights
 from torch import nn
 from PIL import Image # pip install pillow
 
-labels = ['Fractured','Non-fractured']
+labels = ['fractured','not fractured']
 
 # Same data transformation that was used for inputs (except data augmentation)
 data_transform = transforms.Compose([
-    transforms.Resize(size=(256, 256)),
+    transforms.Resize(size=(128, 128)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
@@ -30,14 +30,14 @@ def predict(img):
             confidences = {labels[i]: float(predictions[i]) for i in range(len(labels))}
     return confidences
 
-title = "Corn Leaf Diseases"
-description = "A corn leaf disease classifier trained on the Kaggle dataset using Resnet18"
+title = "Bone Fractures"
+description = "Bone fractures classifier trained on the Kaggle dataset using Resnet18"
 
 demo=gr.Interface(fn=predict,
              inputs=gr.Image(type="pil"),
              outputs=gr.Label(num_top_classes=len(labels)),
              title=title,
              description=description,     
-             examples=["2.jpg", "Corn_Common_Rust.jpg"])
+             examples=["2.jpg", "6.jpg"])
 
 demo.launch('share=True')