Update app.py

app.py

@@ -487,39 +487,26 @@ with demo:
     gr.Markdown(
         """
         <p align="center">
-            <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
+            <img width=200 src="https://www.yamu.com/uploads/image/20220602/1d3eb99b96d3a84ef37eda59989e5e2f.png">
         </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>
+        <h1 style="text-align: center;">用全同态加密实现加密匿名化</h1>        
         """
     )
 
     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>
+        匿名化是为了保护个人隐私从文档中删除个人身份信息 (PII) 数据的过程。</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>
+        通常的匿名化会删除隐私数据或者用没有意义的字符代替，这就使得数据失去了价值。而加密匿名化使用完全同态加密 (FHE) 对文档中的个人身份信息 (PII) 进行加密实现匿名化，从而可以对加密后的数据执行其他计算。</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>
+        在本示例中，我们展示了如何利用加密匿名化以保护隐私的方式使用ChatGPT等LLM服务。</p>
     """
     )    
     
@@ -535,15 +522,12 @@ with demo:
     ########################## Key Gen Part ##########################
 
     gr.Markdown(
-        "## 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, enables a server to work on the encrypted data without seeing the 
-        actual data.
+        "## 第一步： 生成密钥\n\n"
+        """在全同态加密 (FHE) 方法中，会创建两种类型的密钥。第一种称为私钥，用于加密和解密用户的数据。第二种称为评估密钥，使服务器能够在不查看实际数据的情况下处理加密数据。        
         """
     )
 
-    gen_key_btn = gr.Button("Generate the secret and evaluation keys")
+    gen_key_btn = gr.Button("生成私钥和评估密钥")
 
     gen_key_btn.click(
         key_gen_fn,