import typer

app = typer.Typer(name="ash", help="Ash Studio CLI - Manage data platform, AI models, analytics, and integrations.")

@app.command()
def insight_generator(
    data_file: str = typer.Option(..., help="Path to the data file."),
    output: str = typer.Option(..., help="Path for the output file.")
):
    """
    Discover patterns and predict future trends from your data.

    Example usage:
    ash-studio insight-generator --data-file=my_data.csv --output=trends.json
    """
    typer.echo(f"Analyzing data from {data_file} and saving results to {output}...")

@app.command()
def brain_builder(
    model_name: str = typer.Option(..., help="Name of the AI model."),
    deploy: bool = typer.Option(False, help="Deploy the model after building.")
):
    """
    Create, deploy, and optimize custom AI models that learn and adapt.

    Example usage:
    ash-studio brain-builder --model-name="CustomerChurnPredictor" --deploy
    """
    typer.echo(f"Building model '{model_name}'...")
    if deploy:
        typer.echo(f"Deploying model '{model_name}'...")

@app.command()
def data_shaper(
    workflow_name: str = typer.Option(..., help="Name of the data workflow."),
    real_time: bool = typer.Option(False, help="Enable real-time data adaptation.")
):
    """
    Transform and clean your data, creating workflows that adapt in real-time.

    Example usage:
    ash-studio data-shaper --workflow-name="SalesDataCleanup" --real-time
    """
    typer.echo(f"Creating data workflow '{workflow_name}' with real-time adaptation: {real_time}...")

@app.command()
def ml_orchestrator(
    pipeline_name: str = typer.Option(..., help="Name of the ML pipeline.")
):
    """
    Automate the entire machine learning pipeline from data prep to deployment.

    Example usage:
    ash-studio ml-orchestrator --pipeline-name="CustomerSegmentationPipeline"
    """
    typer.echo(f"Orchestrating ML pipeline '{pipeline_name}'...")

@app.command()
def speed_query(
    query: str = typer.Option(..., help="SQL query to execute."),
    dataset: str = typer.Option(..., help="Dataset to query.")
):
    """
    Quickly search and analyze large datasets with high-speed queries.

    Example usage:
    ash-studio speed-query --query="SELECT * FROM sales WHERE region='North'" --dataset="sales_data"
    """
    typer.echo(f"Executing query '{query}' on dataset '{dataset}'...")

@app.command()
def data_guardian(
    policy_file: str = typer.Option(..., help="Path to the policy configuration file.")
):
    """
    Manage data security, compliance, and governance seamlessly.

    Example usage:
    ash-studio data-guardian --policy-file=security_policy.yaml
    """
    typer.echo(f"Applying security policies from {policy_file}...")

@app.command()
def model_ops(
    model_name: str = typer.Option(..., help="Name of the model to manage."),
    action: str = typer.Option(..., help="Action to perform (e.g., monitor, scale).")
):
    """
    Oversee and manage the lifecycle of AI models, including monitoring and scaling.

    Example usage:
    ash-studio model-ops --model-name="ChurnModel" --action="monitor"
    """
    typer.echo(f"Performing '{action}' on model '{model_name}'...")

@app.command()
def data_explorer(
    dashboard_name: str = typer.Option(..., help="Name of the dashboard to generate."),
    filters: str = typer.Option(None, help="Filters to apply (optional).")
):
    """
    Generate insights and reports with self-service analytics tools.

    Example usage:
    ash-studio data-explorer --dashboard-name="RevenueAnalysis" --filters="region:North,year:2024"
    """
    typer.echo(f"Generating dashboard '{dashboard_name}' with filters '{filters}'...")

@app.command()
def live_insights(
    data_stream: str = typer.Option(..., help="Name of the data stream to analyze.")
):
    """
    Analyze data in real-time and make decisions instantly.

    Example usage:
    ash-studio live-insights --data-stream="CustomerFeedbackStream"
    """
    typer.echo(f"Analyzing real-time data from stream '{data_stream}'...")

@app.command()
def data_hub(
    source_name: str = typer.Option(..., help="Name of the data source to connect."),
    destination: str = typer.Option(..., help="Destination for the unified data.")
):
    """
    Connect and unify all your data sources in one place.

    Example usage:
    ash-studio data-hub --source-name="SalesDB" --destination="CentralDataLake"
    """
    typer.echo(f"Connecting data source '{source_name}' to destination '{destination}'...")

@app.command()
def dash_live(
    dashboard_name: str = typer.Option(..., help="Name of the interactive dashboard."),
    auto_refresh: bool = typer.Option(False, help="Enable auto-refresh for real-time updates.")
):
    """
    Create interactive dashboards that update in real-time.

    Example usage:
    ash-studio dash-live --dashboard-name="LiveSalesDashboard" --auto-refresh
    """
    typer.echo(f"Creating interactive dashboard '{dashboard_name}' with auto-refresh: {auto_refresh}...")

@app.command()
def tool_connector(
    tool_name: str = typer.Option(..., help="Name of the tool to integrate."),
    config_file: str = typer.Option(..., help="Configuration file for the integration.")
):
    """
    Seamlessly integrate Ash Studio with your existing tools and platforms.

    Example usage:
    ash-studio tool-connector --tool-name="Slack" --config-file=slack_integration.json
    """
    typer.echo(f"Integrating tool '{tool_name}' using configuration from '{config_file}'...")


def check_installation():
    """
    Check if Ash Studio is installed and install it if not present.
    """
    ...

def register_plugin(parent_app: typer.Typer):
    """
    Register the plugin with the main application.
    """
    try:
        check_installation()
        parent_app.add_typer(app)
    except Exception as e:
        print(f"Failed to register plugin '{app}': {str(e)}")


if __name__ == "__main__":
    app()
