import gradio as gr
from paddlenlp import Taskflow
from PIL import Image
vision_language=Taskflow("feature_extraction", model='PaddlePaddle/ernie_vil-2.0-base-zh')

def getImageTestFeture(content,flag):
    if flag==1:
        f_embeds = vision_language(Image.open(content))
    else:
        f_embeds = vision_language(content)
    text_features = f_embeds["features"][0]
    text_features=text_features.tolist()
    return text_features


def quickstart(name,fileinfo):
    pname=name
    flag=0
    if not fileinfo is None:
        pname=fileinfo.name
        flag=1
    xp=getImageTestFeture(pname,flag)
    return xp

    
demo = gr.Interface(fn=quickstart, inputs=["text","file"], outputs="text")

demo.launch()