| 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") | |
| 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 {}") | |
| return {dic["label"]: dic["score"] for dic in res} | |
| iface = gr.Interface(shot, | |
| ["image", "text"], | |
| "label", | |
| examples=[ | |
| ["examples/1.jpg", "ralph lauren;;apparel store;;ralph lauren store;;shirts;;wardrobe;;white flower"], | |
| ["examples/2.JPG", "adidas;;apparel store;;adidas store;;shirts;;wardrobe;;women training;;shoes"], | |
| ["examples/3.jpg", "project x;;sweet monster;;bags store;;store;;shoes store;;glass windows;;hanging lights"], | |
| ["examples/4.JPG", "multi brand store;;multi brand shoe store;;shoe store;;mannequins;;adidas store;;reebok store;;puma store"], | |
| ["examples/5.png", "sophie;;scene"], | |
| ], | |
| description="Add a picture and a list of labels separated by ;;", | |
| title="Zero-shot Image Classification") | |
| iface.launch() | |