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test

app2.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load models
+vit_classifier = pipeline("image-classification", model="LukeXOTWOD/vit-base-oxford-iiit-pets")
+clip_detector = pipeline(model="openai/clip-vit-large-patch14", task="zero-shot-image-classification")
+
+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 Transfer Learning Model": vit_output,
+        "CLIP Zero-Shot Model": clip_output
+    }
+
+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 predictions from a trained ViT model and a zero-shot CLIP model."
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch