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Parent(s):
d21fc46
Update app.py
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app.py
CHANGED
@@ -1,10 +1,18 @@
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import gradio as gr
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Find Your Twins
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Upload your face and find the most similar people from the X dataset. Powered by [Upstash Vector](https://upstash.com) 🚀
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"""
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)
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@@ -12,28 +20,39 @@ with gr.Blocks() as demo:
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with gr.Tab("Basic"):
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with gr.Row():
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with gr.Column(scale=1):
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input_image = gr.Image()
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with gr.Column(scale=3):
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output_image = gr.Gallery()
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@input_image.
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def find_similar_faces(image):
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with gr.Tab("Advanced"):
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with gr.Row():
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with gr.Column(scale=1):
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adv_input_image = gr.Image()
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adv_image_count = gr.Number(9, label="Image Count")
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with gr.Column(scale=3):
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adv_output_image = gr.Gallery(height=1000)
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@adv_input_image.
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def find_similar_faces(image, count):
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-
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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from torchvision.transforms import Resize
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import torch
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from upstash_vector import Index
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import os
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index = Index.from_env()
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resize_transform = Resize((250,250))
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Find Your Twins
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Upload your face and find the most similar people from the X dataset. Powered by [Upstash Vector](https://upstash.com) 🚀
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"""
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)
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with gr.Tab("Basic"):
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with gr.Row():
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with gr.Column(scale=1):
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input_image = gr.Image(type="pil")
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with gr.Column(scale=3):
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output_image = gr.Gallery()
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@input_image.upload(inputs=input_image, outputs=output_image)
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def find_similar_faces(image):
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resized_image = resize_transform(image)
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inputs = extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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embed = outputs.last_hidden_state[0][0]
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result = index.query(vector=embed.tolist(), top_k=3)
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return[dataset["train"][int(vector.id[3:])]["image"] for vector in result]
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with gr.Tab("Advanced"):
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with gr.Row():
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with gr.Column(scale=1):
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adv_input_image = gr.Image(type="pil")
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adv_image_count = gr.Number(9, label="Image Count")
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with gr.Column(scale=3):
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adv_output_image = gr.Gallery(height=1000)
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@adv_input_image.upload(inputs=[adv_input_image, adv_image_count], outputs=[adv_output_image])
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def find_similar_faces(image, count):
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resized_image = resize_transform(image)
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inputs = extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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embed = outputs.last_hidden_state[0][0]
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result = index.query(vector=embed.tolist(), top_k=min(count, 9))
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return[dataset["train"][int(vector.id[3:])]["image"] for vector in result]
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if __name__ == "__main__":
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demo.launch(debug=True)
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