chore: update

app.py

@@ -470,26 +470,25 @@ with demo:
 
     with gr.Accordion("What is encrypted anonymization?", open=False):
         gr.Markdown(
-        """Anonymization is the process of removing personally identifiable information (PII) 
-        from data to protect individual privacy.
-    
-        To resolve trust issues when deploying anonymization as a cloud service, Fully Homomorphic 
-        Encryption (FHE) can be used to preserve the privacy of the original data using 
-        encryption.
-
-        The data remains encrypted throughout the anonymization process, eliminating the need for 
-        third-party access to the raw data. Once the data is anonymized, it can safely be sent 
-        to GenAI services such as ChatGPT.
+        """Anonymization is the process of removing personally identifiable information (PII) data 
+        from a document in order to protect individual privacy.
+        
+        Encrypted anonymization using Fully Homomorphic Encryption (FHE) solves issues when 
+        deploying such tool through an untrusted cloud service, as Fully Homomorphic Encryption 
+        (FHE) allows such services to anonymize personally identifiable information (PII) on an
+        encrypted document. Once the data is anonymized, it can safely be sent to LLM services such 
+        as ChatGPT.
         """
         )
 
     ########################## Key Gen Part ##########################
 
     gr.Markdown(
-        "## Step 1: Key generation\n\n"
-        """In FHE schemes, two sets of keys are generated. First, the secret keys which are used for 
-        encrypting and decrypting data owned by the client. Second, the evaluation keys that allow 
-        a server to blindly process the encrypted data.
+        "## Step 1: Generate the keys\n\n"
+        """In Fully Homomorphic Encryption (FHE) methods, two types of keys are created. The first 
+        type, called secret keys, are used to encrypt and decrypt the user's data. The second type, 
+        called evaluation keys, enable a server to work on the encrypted data without seeing the 
+        actual data.
         """
     )
 
@@ -504,28 +503,19 @@ with demo:
     ########################## Main document Part ##########################
 
     gr.Markdown("<hr />")
-    gr.Markdown("## Step 2: Private document")
+    gr.Markdown("## Step 2.1: Select the document you want to encrypt\n\n"
+        """To make it simple, we pre-compiled the following document, but you are free to choose 
+        on which part you want to run this example.
+        """
+    )
 
     with gr.Row():
         with gr.Column():
             gr.Markdown("**Original document:**")
-            gr.Markdown(
-                """This document was retrieved from the 
-                [Microsoft Presidio](https://huggingface.co/spaces/presidio/presidio_demo) demo.
 
-                You can select and deselect sentences to customize the document that will be used 
-                as the initial prompt for ChatGPT in step 5.
-                """
-            )
         with gr.Column():
-            gr.Markdown("**Anonymized document:**")
-            gr.Markdown(
-                """You can see below the anonymized text, replaced with hexademical strings, that 
-                will be sent to ChatGPT.
-                
-                ChatGPT will then be able to answer any queries about the document.
-                """
-            )
+            gr.Markdown("**Encrypted document:**")
+
 
     with gr.Row():
         with gr.Column():
@@ -549,12 +539,10 @@ with demo:
     ########################## User Query Part ##########################
 
     gr.Markdown("<hr />")
-    gr.Markdown("## Step 3: Private query")
-
-    gr.Markdown(
-        """Now, you can formulate a query. Please choose from the predefined options in 
-        <span style='color:grey'>“Queries examples”</span>" or craft a custom question in 
-        the <span style='color:grey'>“Customized query”</span>" text box.
+    gr.Markdown("## Step 2.2: Select the prompt you want to encrypt\n\n"
+        """Please choose from the predefined options in 
+        <span style='color:grey'>“Prompt examples”</span>" or craft a custom question in 
+        the <span style='color:grey'>“Customized prompt”</span>" text box.
 
         Remain concise and relevant to the context. Any off-topic query will not be processed.
         """
@@ -565,13 +553,13 @@ with demo:
 
             with gr.Column(scale=5):
                 default_query_box = gr.Dropdown(
-                    list(DEFAULT_QUERIES.values()), label="Queries examples:"
+                    list(DEFAULT_QUERIES.values()), label="PROMPT EXAMPLES:"
                 )
 
             gr.Markdown("Or")
 
             query_box = gr.Textbox(
-                value="Who lives in Maine?", label="Customized query:", interactive=True
+                value="What is Alice international bank account number?", label="CUSTOMIZED PROMPT:", interactive=True
             )
 
             default_query_box.change(
@@ -582,7 +570,7 @@ with demo:
 
         with gr.Column(scale=1, min_width=6):
             gr.HTML("<div style='height: 77px;'></div>")
-            encrypt_btn = gr.Button("Encrypt query")
+            encrypt_btn = gr.Button("Encrypt the prompt")
             # gr.HTML("<div style='height: 50px;'></div>")
 
         with gr.Column(scale=5):
@@ -594,15 +582,15 @@ with demo:
     ########################## FHE processing Part ##########################
 
     gr.Markdown("<hr />")
-    gr.Markdown("## Step 4: Secure anonymization with FHE")
+    gr.Markdown("## Step 3: Anonymize the document and the prompt using FHE")
     gr.Markdown(
-        """ Once the client encrypts the private query locally, it will be sent to a remote server 
-        to perform the anonymization on encrypted data. When the computation is done, the server 
-        will return the result to the client for decryption.
+        """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 will return the result to the client for decryption.
         """
     )
 
-    run_fhe_btn = gr.Button("Anonymize with FHE")
+    run_fhe_btn = gr.Button("Anonymize using FHE")
 
     anonymized_text_output = gr.Textbox(
         label="Decrypted anonymized query that will be sent to ChatGPT:", lines=1, interactive=True