Update tutorials for encryption model

tutorials/encrypt_model/README.md

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+English | [中文](README_CN.md)
+
+# FastDeploy generates an encrypted model
+
+This directory provides `encrypt.py` to quickly complete the encryption of the model and parameter files of ResNet50_vd
+
+## encryption
+```bash
+# Download deployment example code 
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/tutorials/encrypt_model
+
+# Download the ResNet50_vd model file
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+
+python encrypt.py --model ResNet50_vd_infer
+```
+>> **Note** After the encryption is completed, the ResNet50_vd_infer_encrypt folder will be generated, including `__model__.encrypted`, `__params__.encrypted`, `encryption_key.txt` three files, where `encryption_key.txt` contains the encrypted key. At the same time, you need to copy the `inference_cls.yaml` configuration file in the original folder to the ResNet50_vd_infer_encrypt folder for subsequent deployment
+
+### Python encryption interface
+
+Use the encrypted interface through the following interface settings
+```python
+import fastdeploy as fd
+import os
+# when key is not given, key will be automatically generated.
+# otherwise, the file will be encrypted by specific key
+encrypted_model, key = fd.encryption.encrypt(model_file.read())
+encrypted_params, key= fd.encryption.encrypt(params_file.read(), key)
+```
+
+### FastDeploy deployment encryption model (decryption)
+
+Through the setting of the following interface, FastDeploy can deploy the encryption model
+```python
+import fastdeploy as fd
+option = fd.RuntimeOption()
+option.set_encryption_key(key)
+```
+
+```C++
+fastdeploy::RuntimeOption option;
+option.SetEncryptionKey(key)
+```
+>> **Note** For more details about RuntimeOption, please refer to [RuntimeOption Python Documentation](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/runtime_option.html), [ RuntimeOption C++ Documentation](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/structfastdeploy_1_1RuntimeOption.html)

tutorials/encrypt_model/README_CN.md

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+[English](README.md) | 中文
+
+# 使用FastDeploy生成加密模型
+
+本目录下提供`encrypt.py`快速完成ResNet50_vd的模型和参数文件加密
+
+FastDeploy支持对称加密的方案，通过调用OpenSSL中的对称加密算法（AES）对模型进行加密并产生密钥
+
+## 加密
+```bash
+#下载加密示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/tutorials/encrypt_model
+
+# 下载ResNet50_vd模型文件
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+
+python encrypt.py --model ResNet50_vd_infer
+```
+>> **注意** 加密完成后会生成ResNet50_vd_infer_encrypt文件夹，包含`__model__.encrypted`,`__params__.encrypted`,`encryption_key.txt`三个文件，其中`encryption_key.txt`包含加密后的秘钥，同时需要将原文件夹中的、`inference_cls.yaml`配置文件 拷贝至ResNet50_vd_infer_encrypt文件夹，以便后续部署使用
+
+### Python加密接口
+
+通过如下接口的设定，使用加密接口（解密）
+```python
+import fastdeploy as fd
+import os
+# when key is not given, key will be automatically generated.
+# otherwise, the file will be encrypted by specific key
+encrypted_model, key = fd.encryption.encrypt(model_file.read())
+encrypted_params, key= fd.encryption.encrypt(params_file.read(), key)
+```
+
+### FastDeploy 部署加密模型
+
+通过如下接口的设定，完成加密模型的推理
+```python
+import fastdeploy as fd
+option = fd.RuntimeOption()
+option.set_encryption_key(key)
+```
+
+```C++
+fastdeploy::RuntimeOption option;
+option.SetEncryptionKey(key)
+```
+>> **注意** RuntimeOption的更多详细信息，请参考[RuntimeOption Python文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/runtime_option.html)，[RuntimeOption C++文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/structfastdeploy_1_1RuntimeOption.html)

tutorials/encrypt_model/encrypt.py

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+import fastdeploy as fd
+import os
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of model directory.")
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    model_file = os.path.join(args.model, "inference.pdmodel")
+    params_file = os.path.join(args.model, "inference.pdiparams")
+    config_file = os.path.join(args.model, "inference_cls.yaml")
+    model_buffer = open(model_file, 'rb')
+    params_buffer = open(params_file, 'rb')
+    encrypted_model, key = fd.encryption.encrypt(model_buffer.read())
+    encrypted_params, key= fd.encryption.encrypt(params_buffer.read(), key)
+    encrypted_model_dir = args.model + "_encrypt"
+    model_buffer.close()
+    params_buffer.close()
+    os.mkdir(encrypted_model_dir)
+    with open(os.path.join(encrypted_model_dir, "__model__.encrypted"), "w") as f:
+        f.write(encrypted_model)
+
+    with open(os.path.join(encrypted_model_dir, "__params__.encrypted"), "w") as f:
+        f.write(encrypted_params)
+
+    with open(os.path.join(encrypted_model_dir, "encryption_key.txt"), "w") as f:
+        f.write(key)
+    print("encryption success")