feat: implement CLI structure with train and predict subcommands using Click

Author: artemisTurintech

cli.py

@@ -0,0 +1,88 @@
+import click
+from pathlib import Path
+
+
+@click.group()
+def cli():
+    """ML Training and Prediction CLI tool."""
+    pass
+
+
+@cli.command()
+@click.option(
+    '--input',
+    'input_path',
+    required=True,
+    type=click.Path(exists=True),
+    help='Path to input CSV file containing training data.'
+)
+@click.option(
+    '--target',
+    'target_column',
+    required=True,
+    type=str,
+    help='Name of the target column in the CSV file.'
+)
+@click.option(
+    '--output-model',
+    'output_model_path',
+    required=True,
+    type=click.Path(),
+    help='Path where the trained model will be saved.'
+)
+@click.option(
+    '--report',
+    'report_path',
+    required=True,
+    type=click.Path(),
+    help='Path where the training report will be saved.'
+)
+def train(input_path, target_column, output_model_path, report_path):
+    """Train a machine learning model on the provided dataset."""
+    # Placeholder function - print arguments for now
+    click.echo("=== Train Command ===")
+    click.echo(f"Input CSV: {input_path}")
+    click.echo(f"Target Column: {target_column}")
+    click.echo(f"Output Model Path: {output_model_path}")
+    click.echo(f"Report Path: {report_path}")
+    
+    # TODO: Add validation for output paths (check if writable)
+    # TODO: Implement actual training logic
+
+
+@cli.command()
+@click.option(
+    '--model',
+    'model_path',
+    required=True,
+    type=click.Path(exists=True),
+    help='Path to the trained model file.'
+)
+@click.option(
+    '--input',
+    'input_path',
+    required=True,
+    type=click.Path(exists=True),
+    help='Path to input CSV file containing data for prediction.'
+)
+@click.option(
+    '--output',
+    'output_path',
+    required=True,
+    type=click.Path(),
+    help='Path where predictions will be saved.'
+)
+def predict(model_path, input_path, output_path):
+    """Make predictions using a trained model."""
+    # Placeholder function - print arguments for now
+    click.echo("=== Predict Command ===")
+    click.echo(f"Model Path: {model_path}")
+    click.echo(f"Input CSV: {input_path}")
+    click.echo(f"Output Path: {output_path}")
+    
+    # TODO: Add validation for output path (check if writable)
+    # TODO: Implement actual prediction logic
+
+
+if __name__ == '__main__':
+    cli()

main.py

@@ -1,5 +1,9 @@
+from cli import cli
+
+
 def main():
-    print("Hello from template-python!")
+    """Entry point for the ML CLI application."""
+    cli()
 
 
 if __name__ == "__main__":