Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# load models
+vit_classifier = pipeline("image-classification", model="dewiri/vit-base-oxford-iiit-pets")
+clip_detector = pipeline("zero-shot-image-classification",model="openai/clip-vit-large-patch14"
+)
+
+# define labels from oxford pets dataset
+labels_oxford_pets = [
+    'Siamese', 'Birman', 'shiba inu', 'staffordshire bull terrier', 'basset hound', 'Bombay', 'japanese chin',
+    'chihuahua', 'german shorthaired', 'pomeranian', 'beagle', 'english cocker spaniel', 'american pit bull terrier',
+    'Ragdoll', 'Persian', 'Egyptian Mau', 'miniature pinscher', 'Sphynx', 'Maine Coon', 'keeshond', 'yorkshire terrier',
+    'havanese', 'leonberger', 'wheaten terrier', 'american bulldog', 'english setter', 'boxer', 'newfoundland', 'Bengal',
+    'samoyed', 'British Shorthair', 'great pyrenees', 'Abyssinian', 'pug', 'saint bernard', 'Russian Blue', 'scottish terrier'
+]
+
+def classify_pet(image):
+    vit_results = vit_classifier(image)
+    vit_output = {result['label']: result['score'] for result in vit_results}
+    
+    clip_results = clip_detector(image, candidate_labels=labels_oxford_pets)
+    clip_output = {result['label']: result['score'] for result in clip_results}
+    
+    return {"ViT Classification": vit_output, "CLIP Zero-Shot Classification": clip_output}
+
+# define example images for testing
+example_images = [
+    ["example_images/dog1.jpeg"],
+    ["example_images/dog2.jpeg"],
+    ["example_images/leonberger.jpg"],
+    ["example_images/snow_leopard.jpeg"],
+    ["example_images/cat.jpg"]
+]
+
+# create the gradio interface
+iface = gr.Interface(
+    fn=classify_pet,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.JSON(),
+    title="Pet Classification Comparison",
+    description="Upload an image of a pet, and compare results from a trained ViT model and a zero-shot CLIP model.",
+    examples=example_images
+)
+
+iface.launch()