Delete app2.py

app2.py

@@ -1,36 +0,0 @@
-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()