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from turtle import title | |
import gradio as gr | |
from transformers import pipeline | |
import numpy as np | |
from PIL import Image | |
pipes = { | |
"ViT/B-16": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-base-patch16"), | |
"ViT/L-14": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-large-patch14"), | |
"ViT/L-14@336px": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-large-patch14-336px"), | |
"ViT/H-14": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-huge-patch14"), | |
} | |
inputs = [ | |
gr.inputs.Image(type='pil'), | |
"text", | |
gr.inputs.Radio(choices=[ | |
"ViT/B-16", | |
"ViT/L-14", | |
"ViT/L-14@336px", | |
"ViT/H-14", | |
], type="value", default="ViT/B-16", label="Model"), | |
] | |
images="festival.jpg" | |
def shot(image, labels_text, model_name): | |
labels = [label.strip(" ") for label in labels_text.strip(" ").split(",")] | |
res = pipes[model_name](images=image, | |
candidate_labels=labels, | |
hypothesis_template= "一张{}的图片。") | |
return {dic["label"]: dic["score"] for dic in res} | |
iface = gr.Interface(shot, | |
inputs, | |
"label", | |
examples=[["festival.jpg", "灯笼, 鞭炮, 对联", "ViT/B-16"], | |
["cat-dog-music.png", "音乐表演, 体育运动", "ViT/B-16"], | |
["football-match.jpg", "梅西, C罗, 马奎尔", "ViT/B-16"]], | |
description="Add a picture and a list of labels separated by commas", | |
title="Zero-shot Image Classification") | |
iface.launch() |