images

app.py

@@ -59,7 +59,6 @@ def predict(img):
         outputs = model(img)
         probabilities = F.softmax(outputs, dim=1)[0]
         
-    # Create dictionary with all classes and their probabilities
     predictions = {
         classes[i]: float(probabilities[i]) * 100  # Convert to percentage
         for i in range(len(classes))
@@ -70,15 +69,13 @@ def predict(img):
     
     return sorted_predictions
 
-# Create Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
     outputs=gr.Label(num_top_classes=10),  # Show all 10 classes
-    examples=[["example1.jpg"], ["example2.jpg"]],  # Optional: Add example images
+    examples=[["example1.jpeg"], ["example2.jpeg"]],  # Optional: Add example images
     title="CIFAR-10 Image Classifier",
     description="Upload an image to classify it into one of these categories: plane, car, bird, cat, deer, dog, frog, horse, ship, or truck. Results show prediction confidence for all classes as percentages."
 )
 
-# Launch the app
 iface.launch()