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from sentence_transformers import SentenceTransformer, util
from PIL import Image
from io import BytesIO
import gradio as gr
import requests
def get_image_embedding(image):
image_model = SentenceTransformer('clip-ViT-B-32')
# Load and preprocess the image
image = Image.open(BytesIO(image))
img_emb = image_model.encode(image)
return {"prediction": img_emb}
image_input = gr.inputs.Image()
label_output = gr.outputs.Label()
gr.Interface(fn=get_image_embedding, inputs=image_input, outputs=label_output).launch()
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