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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="""<p>Chinese CLIP is a contrastive-learning-based vision-language foundation model pretrained on large-scale Chinese data. For more information, please refer to the paper and official github. Also, Chinese CLIP has already been merged into Huggingface Transformers! <br><br> | |
| Paper: <a href='https://arxiv.org/abs/2211.01335'>https://arxiv.org/abs/2211.01335</a> <br> | |
| Github: <a href='https://github.com/OFA-Sys/Chinese-CLIP'>https://github.com/OFA-Sys/Chinese-CLIP</a> (Welcome to star! 🔥🔥) <br><br> | |
| To play with this demo, add a picture and a list of labels in Chinese separated by commas. 上传图片，并输入多个分类标签，用英文逗号分隔。<br> | |
| You can duplicate this space and run it privately: <a style='display:inline-block' href='https://huggingface.co/spaces/OFA-Sys/chinese-clip-zero-shot-image-classification?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>""", | |
| title="Zero-shot Image Classification (中文零样本图像分类)") | |
| iface.launch() |