| 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() |
|
|
