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import datasets | |
from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
import gradio as gr | |
import torch | |
import transformers | |
from sklearn.metrics.pairwise import cosine_similarity | |
from sklearn.metrics import silhouette_score | |
from sentence_transformers import SentenceTransformer | |
model = SentenceTransformer("clip-ViT-L-14") | |
def predict(im1, im2): | |
embeddings = [model.encode(im1), model.encode(im2)] | |
sim = cosine_similarity(embeddings[0].reshape(1, -1), embeddings[1].reshape(1, -1)).squeeze() | |
if sim > 0.80: | |
return sim, "SAME PERSON, UNLOCK PHONE" | |
else: | |
return sim, "DIFFERENT PEOPLE, DON'T UNLOCK" | |
import gradio as gr | |
interface = gr.Interface(fn=predict, | |
inputs=[gr.Image(type="pil", source="webcam"), | |
gr.Image(type="pil", source="webcam")], | |
outputs=[gr.Number(label="Similarity"), | |
gr.Textbox(label="Message")], | |
title='Basic Face-Id', | |
description='A very simple face-id implementation using sentence-transformer embeddings.', | |
) | |
interface.launch() | |