create demo

app.py

@@ -0,0 +1,20 @@
+from transformers import pipeline
+import gradio as gr
+from pathlib import Path
+
+examples = Path('./examples').glob('*')
+examples = list(map(str,examples))
+
+
+pipe = pipeline("image-classification", model="shreydan/vit-base-oxford-iiit-pets")
+
+def predict(inp_path):
+  confidences = pipe(inp_path)
+  confidences = {s['label']:s['score'] for s in confidences}
+  return confidences
+
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="filepath"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=examples).queue().launch()