Update app.py

app.py

@@ -1,4 +1,4 @@
-"""A Gradio app for anonymizing text data using FHE."""
+"""A Gradio app for de-identifying text data using FHE."""
 
 import base64
 import os
@@ -13,7 +13,7 @@ import numpy
 import pandas as pd
 import requests
 from fhe_anonymizer import FHEAnonymizer
-from openai import OpenAI
+#from openai import OpenAI
 from utils_demo import *
 
 from concrete.ml.deployment import FHEModelClient
@@ -491,7 +491,7 @@ with demo:
         
     gr.Markdown(
         """
-        <h1 style="text-align: center;">Encrypted Anonymization Using Fully Homomorphic Encryption</h1>
+        <h1 style="text-align: center;">Encrypted de-identification Using Fully Homomorphic Encryption</h1>
         <p align="center">
             <a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/github.png">Concrete-ML</a>
             —
@@ -507,29 +507,16 @@ with demo:
     gr.Markdown(
     """
     <p align="center" style="font-size: 16px;">
-        Anonymization is the process of removing personally identifiable information (PII) data from 
-        a document in order to protect individual privacy.</p>
+        Anonymization makes it impossible to identify a person from a data set and thus allows their privacy to be respected (CNIL) </p> Concealing some of PII information is a helpful technique for de-identification. However, this can't be considered as anonymization. 
 
     <p align="center" style="font-size: 16px;">
-        Encrypted anonymization uses Fully Homomorphic Encryption (FHE) to anonymize personally 
+        De-identification uses Fully Homomorphic Encryption (FHE) to conceal personally 
         identifiable information (PII) within encrypted documents, enabling computations to be 
         performed on the encrypted data.</p>
-
-    <p align="center" style="font-size: 16px;">
-        In the example above, we're showing how encrypted anonymization can be leveraged to use LLM 
-        services such as ChatGPT in a privacy-preserving manner.</p>
+        
     """
     )    
     
-    gr.Markdown(
-        """
-        <p align="center">
-            <img width="75%" height="30%" src="https://raw.githubusercontent.com/kcelia/Img/main/fhe_anonymization_banner.png">
-        </p>
-        """
-    )
-
-
     ########################## Key Gen Part ##########################
 
     gr.Markdown(
@@ -613,34 +600,34 @@ with demo:
 
         with gr.Column(scale=5):
             output_encrypted_box = gr.Textbox(
-                label="Encrypted anonymized query that will be sent to the anonymization server:",
+                label="Encrypted de-identified query that will be sent to the de-identification server:",
                 lines=8,
             )
 
     ########################## FHE processing Part ##########################
 
     gr.Markdown("<hr />")
-    gr.Markdown("## Step 3: Anonymize the document and the prompt using FHE")
+    gr.Markdown("## Step 3: De-identify the document and the prompt using FHE")
     gr.Markdown(
         """Once the client encrypts the document and the prompt locally, it will be sent to a remote 
-        server to perform the anonymization on encrypted data. When the computation is done, the 
+        server to perform the de-identification on encrypted data. When the computation is done, the 
         server will return the result to the client for decryption.
         """
     )
 
-    run_fhe_btn = gr.Button("Anonymize using FHE")
+    run_fhe_btn = gr.Button("De-identify using FHE")
 
     with gr.Row():
         with gr.Column(scale=5):
 
             anonymized_doc_output = gr.Textbox(
-                label="Decrypted and anonymized document", lines=10, interactive=True
+                label="Decrypted and de-idenntified document", lines=10, interactive=True
             )
 
         with gr.Column(scale=5):
 
             anonymized_query_output = gr.Textbox(
-                label="Decrypted and anonymized prompt", lines=10, interactive=True
+                label="Decrypted and de-identified prompt", lines=10, interactive=True
             )