Updated from colab
Browse files- app.py +55 -0
- examples/img1.jpg +0 -0
- examples/img2.jpg +0 -0
- examples/img3.jpg +0 -0
- requirements.txt +3 -0
app.py
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import torch
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import gradio as gr
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import matplotlib.pyplot as plt
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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model = SentenceTransformer('clip-ViT-L-14')
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def predict(im1, im2):
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embeding = model.encode([im1, im2])
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sim = cosine_similarity(embeding)
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sim = sim[0][1]
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if sim > 0.75:
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return sim, "SAME PERSON, UNLOCK PHONE"
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else:
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return sim, "DIFFERENT PEOPLE, DON'T UNLOCK"
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title="Face-id Application Demo"
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description = "Upload similar/different images to compare Image similarity for face-id demo"
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article = """
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- Select an image from the examples provided as demo image
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- Click submit button to make Image classification
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- Click clear button to try new Image for classification
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"""
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img_upload = gr.Interface(
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fn=predict,
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inputs= [gr.Image(type="pil", source="upload"),
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gr.Image(type="pil", source="upload")],
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outputs= [gr.Number(label="Similarity"),
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gr.Textbox(label="Message")],
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title=title,
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description=description,
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article=article,
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examples=[['examples/img1.jpg', 'examples/img2.jpg'],
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['examples/img1.jpg', 'examples/img3.jpg']]
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)
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webcam_upload = gr.Interface(
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fn=predict,
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inputs= [gr.Image(type="pil", source="webcam"),
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gr.Image(type="pil", source="webcam")],
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outputs= [gr.Number(label="Similarity"),
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gr.Textbox(label="Message")],
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title=title,
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description=description,
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article=article,
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)
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face_id = gr.TabbedInterface(
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[img_upload, webcam_upload],
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["Upload-Image", "Use Webcam"])
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face_id.launch(debug=True)
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examples/img1.jpg
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examples/img2.jpg
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examples/img3.jpg
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requirements.txt
ADDED
@@ -0,0 +1,3 @@
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torch
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transformers
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sentence-transformers
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