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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() |