from rate_my_run.models import TrainingSummary

def format_pace(pace_min_per_km: float | None) -> str:
    """5.02 -> '5:01/km'. Returns 'n/a' for missing pace."""
    if pace_min_per_km is None:
        return "n/a"
    minutes = int(pace_min_per_km)
    seconds = round((pace_min_per_km - minutes) * 60)
    if seconds == 60:
        minutes, seconds = minutes + 1, 0
    return f"{minutes}:{seconds:02d}/km"

SIGNAL_DESCRIPTIONS = {
    "mileage_spike":          "Weekly mileage rose more than 10% versus last week.",
    "potential_fatigue":      "Mileage increased while pace slowed — a possible sign of fatigue or under-recovery.",
    "returning_after_break":  "The athlete is returning to running after a notable break.",
    "increasing_consistency": "The number of runs per week is increasing.",
    "acwr_danger_zone":       "Training load (ACWR) is in the danger zone — weekly mileage is spiking too fast versus the 4-week average, raising overuse-injury risk.",
    "acwr_undertraining":     "Training load (ACWR) is low — recent volume is below the 4-week average, so fitness may be stagnating.",
}


def acwr_band(acwr: float) -> str:
    """Plain-English band for an ACWR value (thresholds mirror analytics.ACWR_*)."""
    if acwr < 0.80:
        return "under-training (fitness may stagnate)"
    if acwr <= 1.30:
        return "the sweet spot (safe, progressive load)"
    if acwr <= 1.50:
        return "elevated (monitor recovery)"
    return "danger zone (mileage spiking too fast; high injury risk)"

def summary_to_text(summary: TrainingSummary, goal: str | None = None, name: str|None = None) -> str:
    """Render a TrainingSummary into neutral English for the coaching model."""
    lines: list[str] = []
    this_week = summary.weeks[-1]

    if goal:
        lines.append(f"Athlete's stated goal: {goal}")
        lines.append("")

    if name:
        lines.append(f"Athlete's name: {name}")
        lines.append("")

    # --- Current week ---
    lines.append("CURRENT WEEK")
    lines.append(f"- Runs: {this_week.num_runs}")
    lines.append(f"- Distance: {this_week.distance_km:.1f} km")
    lines.append(f"- Longest run: {this_week.longest_run_km:.1f} km")
    if this_week.avg_pace_min_per_km is not None:
        lines.append(f"- Average pace: {format_pace(this_week.avg_pace_min_per_km)}")
    else:
        lines.append("- Average pace: no runs this week")
    lines.append("")

    # --- Trends ---
    lines.append("TRENDS")
    if summary.mileage_change_pct is None:
        lines.append("- Weekly mileage: no comparison (no runs last week).")
    else:
        direction = "up" if summary.mileage_change_pct >= 0 else "down"
        lines.append(f"- Weekly mileage {direction} {abs(summary.mileage_change_pct):.0f}% vs last week.")


    if summary.pace_trend == 'insufficient_data':
        lines.append("- Weekly pace: insufficient data for pace trend analysis.")
    elif summary.pace_trend == 'improving':
        lines.append("- Weekly pace: weekly pace this week has improved compared to the recent baseline.")
    elif summary.pace_trend == 'plateauing':
        lines.append("- Weekly pace: weekly pace is plateauing as it has not changed significantly compared to the recent baseline.")
    elif summary.pace_trend == 'declining':
        lines.append("- Weekly pace: weekly pace this week has declined compared to the recent baseline.")

    if summary.acwr is not None:
        lines.append(f"- Training load (ACWR): {summary.acwr:.2f} — {acwr_band(summary.acwr)}.")

    lines.append(f"- Days since last run: {summary.days_since_last_run}")

    for i, week in enumerate(summary.weeks[-5:-1][::-1]):
        lines.append(f"- {i+1} Weeks ago -> {week.__str__()}")

    # --- Signals ---
    if summary.signals:
        lines.append("")
        lines.append("SIGNALS")
        for s in summary.signals:
            lines.append(f"- {SIGNAL_DESCRIPTIONS.get(s, s)}")

    return "\n".join(lines)