Updated from colab

app.py

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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)

requirements.txt

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+scikit-learn
+datasets
+sentence_transformers