init

lib/ai/claude.ts

@@ -3,7 +3,7 @@ import { ObjectId } from "mongodb";
 import { formatDate, formatDateShort, formatDistance, formatDuration, formatPace } from "@/lib/format";
 import { fetchRecentWellness, type DayWellness } from "@/lib/garmin/wellness";
 import { getAuthorizedClient } from "@/lib/garmin/client";
-import { getRunningActivity, listRunningActivities, type RunningActivity } from "@/lib/models/running";
+import { getRunningActivity, listRunningActivities, type RunMetrics, type RunningActivity } from "@/lib/models/running";
 import { getStrengthWorkout, listStrengthWorkouts, type StrengthWorkout } from "@/lib/models/strength";
 import {
   getLatestAnalysisForTarget,
@@ -28,6 +28,50 @@ Jeśli podano dane z poprzednich treningów, odnieś się do progresu (np. zmian
 type PreviousRun = { run: RunningActivity; analysis: AiAnalysis | null };
 type PreviousWorkout = { workout: StrengthWorkout; analysis: AiAnalysis | null };
 
+function buildRunMetricsSummary(metrics: RunMetrics, totalDistanceM: number): string[] {
+  const { distanceKm, hrBpm, gcbLeftPct } = metrics;
+  if (!hrBpm && !gcbLeftPct) return [];
+  const n = distanceKm.length;
+  if (n < 8) return [];
+
+  const maxDist = Math.max(...distanceKm);
+  const totalKm = totalDistanceM / 1000;
+  const useIndex = maxDist === 0;
+
+  const position = (i: number) => (useIndex ? i / n : distanceKm[i] / maxDist);
+  const kmLabel = (i: number) =>
+    useIndex ? ((i / n) * totalKm).toFixed(1) : distanceKm[i].toFixed(1);
+  const avg = (vals: number[]) =>
+    vals.length ? Math.round(vals.reduce((s, v) => s + v, 0) / vals.length) : null;
+
+  const lines = [`Dane w trakcie biegu (4 kwartyle):`];
+  for (let q = 0; q < 4; q++) {
+    const from = q / 4;
+    const to = (q + 1) / 4;
+    const idx = Array.from({ length: n }, (_, i) => i).filter(
+      (i) => position(i) >= from && position(i) < to
+    );
+    if (idx.length === 0) continue;
+
+    const parts = [`${kmLabel(idx[0])}–${kmLabel(idx[idx.length - 1])} km`];
+
+    if (hrBpm) {
+      const vals = idx.map((i) => hrBpm[i]).filter((v) => v > 0);
+      const a = avg(vals);
+      if (a !== null) parts.push(`HR śr. ${a} bpm`);
+    }
+    if (gcbLeftPct) {
+      const vals = idx.map((i) => gcbLeftPct[i]).filter((v) => v > 0);
+      if (vals.length > 0) {
+        const mean = vals.reduce((s, v) => s + v, 0) / vals.length;
+        parts.push(`balans L/P ${mean.toFixed(1)}%/${(100 - mean).toFixed(1)}%`);
+      }
+    }
+    lines.push(`- ${parts.join(", ")}`);
+  }
+  return lines.length > 1 ? lines : [];
+}
+
 function buildRunningPrompt(activity: RunningActivity, previousRuns: PreviousRun[]): string {
   const lines = [
     `Przeanalizuj poniższy bieg i podaj krótkie podsumowanie oraz wskazówki potreningowe.`,
@@ -58,6 +102,13 @@ function buildRunningPrompt(activity: RunningActivity, previousRuns: PreviousRun
   if (activity.aerobicTrainingEffect) lines.push(`Efekt treningowy aerobowy: ${activity.aerobicTrainingEffect.toFixed(1)}`);
   if (activity.anaerobicTrainingEffect) lines.push(`Efekt treningowy anaerobowy: ${activity.anaerobicTrainingEffect.toFixed(1)}`);
 
+  if (activity.runMetrics) {
+    const metricLines = buildRunMetricsSummary(activity.runMetrics, activity.distanceM);
+    if (metricLines.length > 0) {
+      lines.push(``, ...metricLines);
+    }
+  }
+
   if (previousRuns.length > 0) {
     lines.push(``, `Poprzednie biegi (od najnowszego):`);
     for (const { run, analysis } of previousRuns) {