from content_agent.business_modules import learning_review
from content_agent.integrations.runtime_files import LocalRuntimeFileStore
from tests.test_p8_strategy_review import _write_minimal_runtime


class FeedbackRuntime(LocalRuntimeFileStore):
    def __init__(self, *args, feedback_rows=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.feedback_rows = feedback_rows or []

    def read_performance_feedback(self, run_id: str, policy_run_id: str):
        return [
            row
            for row in self.feedback_rows
            if row["run_id"] == run_id and row["policy_run_id"] == policy_run_id
        ]


def test_strategy_review_summarizes_fake_performance_feedback(tmp_path):
    run_id = "run_feedback"
    policy_run_id = "policy_feedback"
    runtime = FeedbackRuntime(
        tmp_path / "runtime",
        feedback_rows=[
            {
                "run_id": run_id,
                "policy_run_id": policy_run_id,
                "feedback_id": "feedback_001",
                "feedback_status": "available",
                "completion_rate": 0.72,
                "share_rate": 0.08,
                "average_watch_seconds": 18.5,
            }
        ],
    )
    runtime.prepare_run(run_id)
    _write_minimal_runtime(runtime, run_id, policy_run_id)

    review = learning_review.run(run_id, policy_run_id, runtime)

    assert review["performance_feedback"]["performance_feedback_status"] == "available"
    assert review["performance_feedback"]["feedback_count"] == 1
    assert review["performance_feedback"]["average_completion_rate"] == 0.72
    assert any(
        item["recommendation_type"] == "performance_feedback"
        and item["suggested_action"] == "review_feedback_before_strategy_change"
        for item in review["recommendations"]
    )
    assert review["rule_review"]["decision_distribution"]["ADD_TO_CONTENT_POOL"] == 1