clip-embedding / app.py
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import gradio as gr
import numpy as np
from PIL import Image
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('clip-ViT-B-32')
def image_to_embedding(img: np.ndarray):
embedding = model.encode(sentences=[Image.fromarray(img)], batch_size=128)
return embedding
iface = gr.Interface(fn=image_to_embedding, inputs="image", outputs="textbox", cache_examples=True)
iface.launch()