| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| from PIL import Image | |
| pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32") | |
| images="dog.jpg" | |
| def shot(image, labels_text): | |
| PIL_image = Image.fromarray(np.uint8(image)).convert('RGB') | |
| labels = labels_text.split(",") | |
| res = pipe(images=PIL_image, | |
| candidate_labels=labels, | |
| hypothesis_template= "This is a photo of a {}") | |
| return {dic["label"]: dic["score"] for dic in res} | |
| iface = gr.Interface(shot, ["image", "text"], "label", examples=[["dog.jpg", "dog,cat,bird"], | |
| ["germany.jpg", "germany,belgium,colombia"], | |
| ["colombia.jpg", "germany,belgium,colombia"]]) | |
| iface.launch() |