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from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
import datasets
import gradio as gr
model = SentenceTransformer('clip-ViT-B-16')
dataset = datasets.load_dataset('brendenc/celeb-identities')
def predict(im1, im2):
embeddings = model.encode([im1, im2])
sim = cosine_similarity(embeddings)
sim = sim[0, 1]
if sim > 0.75:
return sim, "SAME PERSON, UNLOCK PHONE"
else:
return sim, "DIFFERENT PEOPLE, DON'T UNLOCK"
interface = gr.Interface(fn=predict,
inputs= [gr.Image(value = dataset['train']['image'][0], type="pil", source="webcam"),
gr.Image(value = dataset['train']['image'][1], type="pil", source="webcam")],
outputs= [gr.Number(label="Similarity"),
gr.Textbox(label="Message")],
title = 'Face ID',
description = 'This app uses emage embeddings and cosine similarity to function as a Face ID application. Cosine similarity is used, so it ranges from -1 to 1.'
)
interface.launch(debug=True)
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