chore: import upstream snapshot with attribution

app/tool/chart_visualization/python_execute.py

@@ -0,0 +1,36 @@
+from app.config import config
+from app.tool.python_execute import PythonExecute
+
+
+class NormalPythonExecute(PythonExecute):
+    """A tool for executing Python code with timeout and safety restrictions."""
+
+    name: str = "python_execute"
+    description: str = """Execute Python code for in-depth data analysis / data report(task conclusion) / other normal task without direct visualization."""
+    parameters: dict = {
+        "type": "object",
+        "properties": {
+            "code_type": {
+                "description": "code type, data process / data report / others",
+                "type": "string",
+                "default": "process",
+                "enum": ["process", "report", "others"],
+            },
+            "code": {
+                "type": "string",
+                "description": """Python code to execute.
+# Note
+1. The code should generate a comprehensive text-based report containing dataset overview, column details, basic statistics, derived metrics, timeseries comparisons, outliers, and key insights.
+2. Use print() for all outputs so the analysis (including sections like 'Dataset Overview' or 'Preprocessing Results') is clearly visible and save it also
+3. Save any report / processed files / each analysis result in worksapce directory: {directory}
+4. Data reports need to be content-rich, including your overall analysis process and corresponding data visualization.
+5. You can invode this tool step-by-step to do data analysis from summary to in-depth with data report saved also""".format(
+                    directory=config.workspace_root
+                ),
+            },
+        },
+        "required": ["code"],
+    }
+
+    async def execute(self, code: str, code_type: str | None = None, timeout=5):
+        return await super().execute(code, timeout)