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| from sklearn.metrics.pairwise import cosine_similarity | |
| from sentence_transformers import SentenceTransformer | |
| import gradio as gr | |
| import numpy as np | |
| model = SentenceTransformer("clip-ViT-L-14") | |
| def predict(im1, im2): | |
| embeddings = model.encode([im1, im2]) | |
| embeddings_reshaped = [np.reshape(img, (1, -1)) for img in embeddings] | |
| sim = cosine_similarity(embeddings_reshaped[0], embeddings_reshaped[1]) | |
| if sim > 0.72: | |
| return sim, "SAME PERSON, UNLOCK PHONE" | |
| else: | |
| return sim, "DIFFERENT PEOPLE, DON'T UNLOCK" | |
| 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")] | |
| ) | |
| interface.launch(debug=True) | |