from app.tool.chart_visualization.python_execute import NormalPythonExecute


class VisualizationPrepare(NormalPythonExecute):
    """A tool for Chart Generation Preparation"""

    name: str = "visualization_preparation"
    description: str = "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; insight: choose insight into chart",
                "type": "string",
                "default": "visualization",
                "enum": ["visualization", "insight"],
            },
            "code": {
                "type": "string",
                "description": """Python code for data_visualization prepare.
## Visualization Type
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}[])
## Insight Type
1. Select the insights from the data_visualization results that you want to add to the chart.
2. Save information in json file.( format: {"chartPath": string, "insights_id": number[]}[])
# Note
1. You can generate one or multiple csv data with different visualization needs.
2. Make each chart data esay, clean and different.
3. Json file saving in utf-8 with path print: print(json_path)
""",
            },
        },
        "required": ["code", "code_type"],
    }