Update app.py

app.py

@@ -4,7 +4,9 @@ from transformers import pipeline
 # Function for image classification
 def classify(image, model_name):
     try:
+        # Load the pipeline with the given model name
         pipe = pipeline("image-classification", model=model_name)
+        # Perform image classification
         results = pipe(image)
         return {result["label"]: round(result["score"], 2) for result in results}
     except Exception as e:
@@ -21,6 +23,10 @@ demo = gr.Interface(
     outputs=gr.Label(num_top_classes=3, label="Top Predictions"),
     title="Custom timm Model Image Classifier",
     description="Enter a timm model name from Hugging Face, upload an image, and get predictions.",
+    examples=[
+        ["cat.png", "timm/mobilenetv3_small_100.lamb_in1k"],
+        ["cat.png", "timm/resnet50.a1_in1k"],
+    ],
 )
 
 demo.launch()