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# 将文件写入会话
<a name="code-interpreter-file-operations"></a>

您可以使用代码解释器在沙盒环境中读取和写入文件。这允许您上传数据文件，使用代码对其进行处理并检索结果。

 **安装依赖项** 

运行以下命令来安装所需的软件包：

```
pip install bedrock-agentcore
```

 **使用文件工具上传代码和数据 ** 

以下 Python 代码显示如何将文件上传到代码解释器会话并执行处理这些文件的代码。所需的文件是该软件包[中可用的文件](samples/read_write_files.zip)。`data.csv` `stats.py`

```
# file_mgmt_ci_agent.py

from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter
import json
from typing import Dict, Any, List

#Configure and Start the code interpreter session
code_client = CodeInterpreter('<Region>')
code_client.start()

#read the content of the sample data file
data_file = "data.csv"

try:
    with open(data_file, 'r', encoding='utf-8') as data_file_content:
        data_file_content = data_file_content.read()
    #print(data_file_content)
except FileNotFoundError:
    print(f"Error: The file '{data_file}' was not found.")
except Exception as e:
    print(f"An error occurred: {e}")

#read the content of the python script to analyze the sample file
code_file = "stats.py"
try:
    with open(code_file, 'r', encoding='utf-8') as code_file_content:
        code_file_content = code_file_content.read()
    #print(code_file_content)
except FileNotFoundError:
    print(f"Error: The file '{code_file}' was not found.")
except Exception as e:
    print(f"An error occurred: {e}")

files_to_create = [
                {
                    "path": "data.csv",
                    "text": data_file_content
                },
                {
                    "path": "stats.py",
                    "text": code_file_content
                }]

#define the method to call the invoke API
def call_tool(tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:

    response = code_client.invoke(tool_name, arguments)
    for event in response["stream"]:
        return json.dumps(event["result"], indent=2)

#write the sample data and analysis script into the code interpreter session
writing_files = call_tool("writeFiles", {"content": files_to_create})
print(f"writing files: {writing_files}")

#List and validate that the files were written successfully
listing_files = call_tool("listFiles", {"path": ""})
print(f"listing files: {listing_files}")

#Run the python script to analyze the sample data file
execute_code = call_tool("executeCode", {
                "code": files_to_create[1]['text'],
                "language": "python",
                "clearContext": True})
print(f"code execution result: {execute_code}")

#Clean up and stop the code interpreter session
code_client.stop()
```