import gradio as gr
from paddlenlp import Taskflow
import json
import numpy as np
import requests
import urllib
from io import BytesIO
from PIL import Image

vision_language=Taskflow("feature_extraction", model='PaddlePaddle/ernie_vil-2.0-base-zh')

def getImageTestFeture(content):
    if content.startswith("http"):
        response = requests.get(content)
        x=BytesIO(response.content)
        f_embeds = vision_language(Image.open(x))
    else:
        f_embeds = vision_language(content)
    text_features = f_embeds["features"][0]
    text_features=text_features.tolist()
    return text_features

def greet(name):
    x=getImageTestFeture(name)
    return x

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()