app.py

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()