/app/tool/chart_visualization/chart_prepare.py

from app.tool.chart_visualization.python_execute import NormalPythonExecute
class VisualizationPrepare(NormalPythonExecute):
    """A tool for Chart Generation Preparation"""

    name: str = "visualization_preparation"
    description: str = """
    You need some charts to replace initial report's placeholders. So you need to use this tool first to prepare metadata for data_visualization tool.
    Using Python code to generates metadata of data_visualization tool. Outputs: 1) JSON Information. 2) Cleaned CSV data files (Optional).
    """
    parameters: dict = {
        "type": "object",
        "properties": {
            "code_type": {
                "description": "code type, visualization: csv -> chart",
                "type": "string",
                "default": "visualization"
            },
            "code": {
                "type": "string",
                "description": """Python code for data_visualization prepare.

## Visualization Type (Initial Step)
1. Data loading logic
2. Csv Data and chart description generate
   2.1 Csv data (The data you want to visulazation, cleaning / transform from origin data, saved in .csv)
   2.2 Chart description of csv data (The chart title or description should be concise and clear. Examples: 'Product sales distribution', 'Monthly revenue trend'.)
3. Save information in json file.( format: {"csvFilePath": string, "chartTitle": string}[])


# Best Practices
1. Generate one or multiple csv data with different visualization needs based on the initial report
2. Make each chart data simple, clean and distinct
4. Json file saving in utf-8 with path print: print(json_path)
""",
            },
        },
        "required": ["code", "code_type"],
    }