# /// script
# requires-python = ">=3.11"
# dependencies = [
{% for dep in deps %}#     "{{ dep }}",
{% endfor %}
# ]
# ///
"""
Model: {{ name }}

Generated by: seeknal draft model {{ name }} --python
"""

from seeknal.pipeline import transform


@transform(
    name="{{ name }}",
    description="{{ description or 'Machine learning model' }}",
    # inputs=["feature_group.user_features"],  # Input feature group
)
def {{ name }}(ctx):
    """
    Model function receives context with:
    - ctx.ref("kind.name") -> Get upstream DataFrame
    - ctx.duckdb -> DuckDB connection
    - ctx.config -> Profile configuration

    Return the model predictions.
    """
    from sklearn.linear_model import LogisticRegression

    df = ctx.ref("feature_group.user_features")

    # TODO: Implement model training/prediction
    # model = LogisticRegression()
    # X = df[feature_columns]
    # predictions = model.predict(X)

    raise NotImplementedError("TODO: Implement model logic")
