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b8ab561
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1 Parent(s): 8e75bc6

Update app.py

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Files changed (1) hide show
  1. app.py +80 -0
app.py CHANGED
@@ -625,6 +625,47 @@ with demo:
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  # </p>
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  # """
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  # )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  gr.Markdown(
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  f"""
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  <p align="center">
@@ -634,6 +675,31 @@ with demo:
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  )
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  ########################## Key Gen Part ##########################
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  gr.Markdown(
@@ -910,6 +976,20 @@ demo.launch(share=False)
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  # # </p>
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  # # """
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  # # )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # with gr.Row():
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  # with gr.Column(elem_id="center_column"):
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  # gr.Image("Img/schema.png", width=200, show_label=False)
 
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  # </p>
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  # """
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  # )
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+
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+ with gr.Accordion("What is bank fraud detection?", open=False):
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+ gr.Markdown(
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+ """
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+ Bank fraud detection is the process of identifying fraudulent activities or transactions
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+ that may pose a risk to a bank or its customers. It is essential to detect fraudulent
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+ activities to prevent financial losses and protect the integrity of the banking system.
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+ """
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+ )
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+
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+ with gr.Accordion("Why is it important to protect this data?", open=False):
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+ gr.Markdown(
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+ """
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+ Banking and financial data often contain sensitive personal information, such as income,
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+ spending habits, and account numbers. Protecting this information ensures that customers'
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+ privacy is respected and safeguarded from unauthorized access.
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+ """
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+ )
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+
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+ with gr.Accordion("Why is Fully Homomorphic Encryption (FHE) a good solution?", open=False):
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+ gr.Markdown(
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+ """
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+ Fully Homomorphic Encryption (FHE) is a powerful technique for enhancing privacy and accuracy
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+ in the context of fraud detection, particularly when dealing with sensitive banking data. FHE
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+ allows for the encryption of data, which can then be processed and analyzed without ever needing
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+ to decrypt it.
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+ Each party involved in the detection process can collaborate without compromising user privacy,
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+ minimizing the risk of data leaks or breaches. The data remains confidential throughout the entire
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+ process, ensuring that the privacy of users is maintained.
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+ """
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+ )
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+
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+ gr.Markdown(
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+ """
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+ <p style="text-align: center;">
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+ Below, we will explain the flow in the image by simulating a purchase you've just made, and show you how our fraud detection model processes the transaction.
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+ </p>
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+ """
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+ )
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+
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+
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  gr.Markdown(
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  f"""
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  <p align="center">
 
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  )
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  ########################## Key Gen Part ##########################
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  gr.Markdown(
 
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  # # </p>
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  # # """
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  # # )
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  # with gr.Row():
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  # with gr.Column(elem_id="center_column"):
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  # gr.Image("Img/schema.png", width=200, show_label=False)