| 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() |