src/core/intelligence/snapshot_builder.py

from core.intelligence.runner_intelligence_snapshot import RunnerIntelligenceSnapshot


def build_intelligence_snapshot(context) -> RunnerIntelligenceSnapshot:
    """
    Builds a RunnerIntelligenceSnapshot from a PipelineContext.
    
    This is an aggregation layer only. It uses safe accessors (`getattr`) 
    to extract already-computed values without introducing new business logic.
    """
    summary = context.summary
    
    # Helper to safely extract depending on whether summary is a dict, WeeklySnapshot, or WeeklySummary
    def _extract_summary_val(dict_key, attr_names, default, transform=None):
        if not summary:
            return default
        
        val = default
        if isinstance(summary, dict):
            val = summary.get(dict_key, default)
        else:
            for attr in attr_names:
                if hasattr(summary, attr):
                    val = getattr(summary, attr, default)
                    break
                    
        return transform(val) if transform and val != default else val

    return RunnerIntelligenceSnapshot(
        week_start=_extract_summary_val("week_start", ["week_start_date", "week_start"], None),

        training_state=getattr(context, "training_state", None),
        health_signal=getattr(context, "health_signal", None),

        positioning_status=getattr(context, "positioning_status", None),
        positioning_change=getattr(context, "positioning_change", None),

        goal_trajectory=getattr(context, "goal_trajectory", None),
        goal_progress_pct=getattr(context, "goal_progress_pct", None),

        next_run=getattr(context, "next_run", None),
        training_focus=getattr(context, "training_focus", None),

        key_insight=getattr(context, "key_insight", None),
        forward_focus=getattr(context, "forward_focus", None),

        weekly_distance_km=_extract_summary_val(
            "total_distance_m", 
            ["total_distance_km", "total_distance_m"], 
            0.0,
            transform=lambda x: x / 1000.0 if not hasattr(summary, "total_distance_km") else x
        ),
        run_count=_extract_summary_val("num_runs", ["run_count", "num_runs"], 0),
        consistency_score=_extract_summary_val("consistency_score", ["consistency_score"], 0),
    )