Market & Demographic Analysis

@tool(name="demographic_projection_tool", description="Analyze demographic projections from CSV(country, metric, year range).", show_result=True)
def demographic_projection_tool(
    country: str = None,
    metric: str = "OldAgeDependencyRatio",
    start_year: int = 2020,
    end_year: int = 2050,
    csv_path: str = None,
) -> str:
    """
    Analyze demographic projections from a CSV dataset.

    Args:
        country(str, optional): Filter results for a specific country. Defaults to None (all countries).
        metric(str): The demographic metric to analyze.
                      Options: Population_65plus, LifeExpectancy, OldAgeDependencyRatio, PensionCoverageRate
        start_year(int): Starting year of analysis. Defaults to 2020.
        end_year(int): Ending year of analysis. Defaults to 2050.
        csv_path(str): Path to CSV file containing demographic data.

    Returns:
        str: JSON string with filtered and projected demographic data.
    """    
    csv_path = csv_path or _path("agent1.csv")
    try:
        df = pd.read_csv(csv_path)
        df = df[(df["Year"] >= start_year) & (df["Year"] <= end_year)]
        if country:
            df = df[df["Country"].str.lower() == country.lower()]
        if metric not in df.columns:
            return json.dumps({"error": f"Invalid metric '{metric}'"})
        result = df[["Country", "Year", metric]].to_dict(orient="records")
        return json.dumps(result, indent=2)
    except Exception as e:
        return json.dumps({"error": str(e)})