Initial commit

app.py

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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()
+

requirements.txt

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+torch
+numpy
+torchvision
+torchaudio
+datasets
+sentence
+transformers
+sentence-transformers
+evaluate
+gradio