Update app.py

app.py

@@ -564,18 +564,17 @@ with demo:
         </div>
         """
     )
-        
     gr.Markdown(
         """
-        <h1 style="text-align: center;">Encrypted Anonymization Using Fully Homomorphic Encryption</h1>
+        <h1 style="text-align: center;">Fraud Detection with FHE Model</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>
-            —
-            <a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/documentation.png">Documentation</a>
+            <a href="https://github.com/CirSandro/private-fhe-fraud-detection">
+                <span style="vertical-align: middle; display:inline-block; margin-right: 3px;">💳</span>private-fhe-fraud-detection
+            </a>
             —
-            <a href=" https://community.zama.ai/c/concrete-ml/8"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/community.png">Community</a>
-            —
-            <a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/x.png">@zama_fhe</a>
+            <a href="https://docs.zama.ai/concrete-ml">
+                <span style="vertical-align: middle; display:inline-block; margin-right: 3px;">🔒</span>Documentation Concrete-ML
+            </a>
         </p>
         """
     )
@@ -583,19 +582,41 @@ 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>
+        How to detect bank fraud without using your personal data ?</p>
+    """
+#     )
 
-    <p align="center" style="font-size: 16px;">
-        Encrypted anonymization uses Fully Homomorphic Encryption (FHE) to anonymize personally 
-        identifiable information (PII) within encrypted documents, enabling computations to be 
-        performed on the encrypted data.</p>
+    # gr.Markdown(
+    #     """
+    #     <h1 style="text-align: center;">Encrypted Anonymization 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>
+    #         —
+    #         <a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/documentation.png">Documentation</a>
+    #         —
+    #         <a href=" https://community.zama.ai/c/concrete-ml/8"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/community.png">Community</a>
+    #         —
+    #         <a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/x.png">@zama_fhe</a>
+    #     </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" 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>
+
+    # <p align="center" style="font-size: 16px;">
+    #     Encrypted anonymization uses Fully Homomorphic Encryption (FHE) to anonymize 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(
     #     """