feat: implement energy availability warning and enhance nutrition visualization

- Added `get_energy_availability_warning_payload` function to assess energy availability and provide contextual warnings based on multiple health indicators.
- Integrated energy availability KPI tile into the nutrition history visualization, enhancing user insights on energy balance.
- Updated frontend components to conditionally display the energy availability warning, improving user experience and data interpretation.
- Refactored existing logic in `charts.py` to utilize the new energy availability functionality, streamlining data handling.

backend/data_layer/nutrition_interpretation.py

@@ -121,8 +121,7 @@ def build_nutrition_history_kpi_tiles(
                 "status": "bad",
                 "verdict": _verdict("bad"),
                 "hint": (
-                    f"Es fehlen rund {miss} g Protein pro Tag – bei Kaloriendefizit "
+                    f"~{miss} g Protein/Tag fehlen – bei Defizit Muskelerhalt gefährdet."
-                    "steigt das Risiko für Muskelerhalt."
                 ),
                 "hoverTop": f"Unterversorgung: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
                 "hoverBody": (
@@ -159,8 +158,8 @@ def build_nutrition_history_kpi_tiles(
                 "status": "warn",
                 "verdict": _verdict("warn"),
                 "hint": (
-                    f"Viele Kalorien kommen aus KH/Fett; Proteinanteil oft sinnvoll bei 25–35 % "
+                    f"Protein-Kalorienanteil niedrig (P {prot_pct} % / KH {kh_pct} % / F {fat_pct} %); "
-                    f"(aktuell P {prot_pct} % / KH {kh_pct} % / F {fat_pct} %)."
+                    "Ziel oft 25–35 %."
                 ),
                 "hoverTop": f"Protein-Anteil niedrig: {prot_pct}% der Kalorien",
                 "hoverBody": (
@@ -173,6 +172,37 @@ def build_nutrition_history_kpi_tiles(
     return tiles
 
 
+def build_energy_availability_kpi_tile(ea: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """E5: nur bei caution/warning — gleiche Daten wie /charts/energy-availability-warning."""
+    level = str(ea.get("warning_level") or "none").strip().lower()
+    if level == "none":
+        return None
+    triggers: List[str] = list(ea.get("triggers") or [])
+    msg = str(ea.get("message") or "").strip()
+    st = "bad" if level == "warning" else "warn"
+    first = triggers[0] if triggers else msg
+    if len(first) > 90:
+        first = first[:87] + "…"
+    meta = ea.get("metadata") if isinstance(ea.get("metadata"), dict) else {}
+    note = str(meta.get("note") or "")
+    hover_lines = [msg] + [f"• {t}" for t in triggers]
+    if note:
+        hover_lines.append(note)
+    return {
+        "key": "energy-availability-e5",
+        "category": "Energieverfügbarkeit",
+        "icon": "⚡",
+        "value": "Achtung" if level == "warning" else "Hinweis",
+        "sublabel": first or "Signale prüfen",
+        "status": st,
+        "verdict": _verdict(st),
+        "hint": msg,
+        "hoverTop": "Energieverfügbarkeit (Heuristik)",
+        "hoverBody": "\n".join(hover_lines),
+        "keys": ["nutrition_score"],
+    }
+
+
 def build_macro_donut_from_averages(navg: Dict[str, Any]) -> Optional[List[Dict[str, Any]]]:
     """Anteile in % der Makro-kcal + Gramm für Legende."""
     p = float(navg.get("protein_avg") or 0)

backend/data_layer/nutrition_metrics.py

@@ -699,6 +699,70 @@ def get_weekly_macro_distribution_chart_data(profile_id: str, weeks: int) -> Dic
     }
 
 
+def get_energy_availability_warning_payload(profile_id: str, days: int = 14) -> Dict:
+    """
+    E5 Energieverfügbarkeit — gleiche Heuristik wie GET /charts/energy-availability-warning.
+    """
+    from data_layer.recovery_metrics import calculate_recovery_score_v2, calculate_sleep_quality_7d
+    from data_layer.body_metrics import calculate_lbm_28d_change
+
+    triggers: List[str] = []
+    warning_level = "none"
+
+    energy_data = get_energy_balance_data(profile_id, days)
+    if energy_data.get("energy_balance", 0) < -500:
+        triggers.append("Großes Energiedefizit (>500 kcal/Tag)")
+
+    try:
+        recovery_score = calculate_recovery_score_v2(profile_id)
+        if recovery_score and recovery_score < 50:
+            triggers.append("Recovery Score niedrig (<50)")
+    except Exception:
+        pass
+
+    try:
+        sleep_quality = calculate_sleep_quality_7d(profile_id)
+        if sleep_quality and sleep_quality < 60:
+            triggers.append("Schlafqualität reduziert (<60%)")
+    except Exception:
+        pass
+
+    try:
+        lbm_change = calculate_lbm_28d_change(profile_id)
+        if lbm_change and lbm_change < -1.0:
+            triggers.append("Magermasse sinkt (-{:.1f} kg)".format(abs(lbm_change)))
+    except Exception:
+        pass
+
+    if len(triggers) >= 3:
+        warning_level = "warning"
+        message = (
+            "⚠️ Hinweis auf mögliche Unterversorgung. Mehrere Indikatoren auffällig. "
+            "Erwäge Defizit-Anpassung oder Regenerationswoche."
+        )
+    elif len(triggers) >= 2:
+        warning_level = "caution"
+        message = (
+            "⚡ Beobachte folgende Signale genau. Aktuell noch kein Handlungsbedarf, aber Trend beachten."
+        )
+    elif len(triggers) >= 1:
+        warning_level = "caution"
+        message = "💡 Ein Indikator auffällig. Weiter beobachten."
+    else:
+        message = "✅ Energieverfügbarkeit unauffällig."
+
+    return {
+        "warning_level": warning_level,
+        "triggers": triggers,
+        "message": message,
+        "metadata": {
+            "days_analyzed": days,
+            "trigger_count": len(triggers),
+            "note": "Heuristische Einschätzung, keine medizinische Diagnose",
+        },
+    }
+
+
 # ============================================================================
 # Calculated Metrics (migrated from calculations/nutrition_metrics.py)
 # ============================================================================

backend/data_layer/nutrition_viz.py

@@ -11,11 +11,13 @@ from typing import Any, Dict, List, Optional
 
 from db import get_db, get_cursor, r2d
 from data_layer.nutrition_interpretation import (
+    build_energy_availability_kpi_tile,
     build_macro_donut_from_averages,
     build_nutrition_history_kpi_tiles,
 )
 from data_layer.nutrition_metrics import (
     estimate_tdee_kcal_from_latest_weight,
+    get_energy_availability_warning_payload,
     get_energy_balance_data,
     get_nutrition_average_data,
     get_protein_targets_data,
@@ -184,12 +186,14 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
             "tdee_reference_kcal": None,
             "energy_balance_meta": {},
             "interpretation_tiles": [],
+            "energy_availability_warning": None,
             "meta": {"layer_1": "nutrition_metrics", "layer_2b": "nutrition_viz"},
         }
 
     all_history = days >= 9999
     eff_days = 3650 if all_history else max(7, min(int(days), 3650))
     cutoff = _cutoff_sql(days)
+    chart_days_for_pipeline = 90 if all_history else max(7, min(eff_days, 365))
 
     navg = get_nutrition_average_data(profile_id, eff_days, all_history=all_history)
     targets = get_protein_targets_data(profile_id)
@@ -223,12 +227,18 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
         navg, targets, date_span_label or "—", max(1, n_days)
     )
 
+    ea_days = min(28, max(7, chart_days_for_pipeline))
+    ea_payload = get_energy_availability_warning_payload(profile_id, ea_days)
+    ea_tile = build_energy_availability_kpi_tile(ea_payload)
+    kpi_tiles_out: List[Dict[str, Any]] = list(kpi_tiles)
+    if ea_tile:
+        kpi_tiles_out.append(ea_tile)
+
     donut = build_macro_donut_from_averages(navg)
 
     kw_points = _kcal_weight_points_for_window(profile_id, cutoff)
     pt_low = round(float(targets.get("protein_target_low") or 0))
 
-    chart_days_for_pipeline = 90 if all_history else max(7, min(eff_days, 365))
     weeks_for_weekly = max(4, min(52, (chart_days_for_pipeline + 6) // 7))
     weekly_chart = get_weekly_macro_distribution_chart_data(profile_id, weeks_for_weekly)
 
@@ -255,8 +265,9 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
             "protein_target_high": targets.get("protein_target_high"),
             "reference_weight_kg": targets.get("current_weight"),
         },
-        "kpi_tiles": kpi_tiles,
+        "kpi_tiles": kpi_tiles_out,
         "interpretation_tiles": [],
+        "energy_availability_warning": ea_payload,
         "daily_macros": daily_macros,
         "donut_avg_pct": donut,
         "protein_reference_line_g": pt_low,

backend/routers/charts.py

@@ -40,6 +40,7 @@ from data_layer.nutrition_metrics import (
     get_macro_consistency_data,
     get_energy_balance_data,
     get_weekly_macro_distribution_chart_data,
+    get_energy_availability_warning_payload,
 )
 from data_layer.activity_metrics import (
     get_activity_summary_data,
@@ -1026,87 +1027,10 @@ def get_energy_availability_warning(
     """
     Energy Availability Warning (E5) - Konzept-konform.
 
-    Heuristic warning for potential undernutrition/overtraining.
+    Datenberechnung: data_layer.nutrition_metrics.get_energy_availability_warning_payload
-
-    Checks:
-    - Persistent large deficit
-    - Recovery score declining
-    - Sleep quality declining
-    - LBM declining
-
-    Args:
-        days: Analysis window (7-28 days, default 14)
-        session: Auth session (injected)
-
-    Returns:
-        {
-            "warning_level": "none" | "caution" | "warning",
-            "triggers": [...],
-            "message": "..."
-        }
     """
     profile_id = session['profile_id']
-
+    return get_energy_availability_warning_payload(profile_id, days)
-    from db import get_db, get_cursor
-    from data_layer.nutrition_metrics import get_energy_balance_data
-    from data_layer.recovery_metrics import calculate_recovery_score_v2, calculate_sleep_quality_7d
-    from data_layer.body_metrics import calculate_lbm_28d_change
-
-    triggers = []
-    warning_level = "none"
-
-    # Check 1: Large energy deficit
-    energy_data = get_energy_balance_data(profile_id, days)
-    if energy_data.get('energy_balance', 0) < -500:
-        triggers.append("Großes Energiedefizit (>500 kcal/Tag)")
-
-    # Check 2: Recovery declining
-    try:
-        recovery_score = calculate_recovery_score_v2(profile_id)
-        if recovery_score and recovery_score < 50:
-            triggers.append("Recovery Score niedrig (<50)")
-    except:
-        pass
-
-    # Check 3: Sleep quality
-    try:
-        sleep_quality = calculate_sleep_quality_7d(profile_id)
-        if sleep_quality and sleep_quality < 60:
-            triggers.append("Schlafqualität reduziert (<60%)")
-    except:
-        pass
-
-    # Check 4: LBM declining
-    try:
-        lbm_change = calculate_lbm_28d_change(profile_id)
-        if lbm_change and lbm_change < -1.0:
-            triggers.append("Magermasse sinkt (-{:.1f} kg)".format(abs(lbm_change)))
-    except:
-        pass
-
-    # Determine warning level
-    if len(triggers) >= 3:
-        warning_level = "warning"
-        message = "⚠️ Hinweis auf mögliche Unterversorgung. Mehrere Indikatoren auffällig. Erwäge Defizit-Anpassung oder Regenerationswoche."
-    elif len(triggers) >= 2:
-        warning_level = "caution"
-        message = "⚡ Beobachte folgende Signale genau. Aktuell noch kein Handlungsbedarf, aber Trend beachten."
-    elif len(triggers) >= 1:
-        warning_level = "caution"
-        message = "💡 Ein Indikator auffällig. Weiter beobachten."
-    else:
-        message = "✅ Energieverfügbarkeit unauffällig."
-
-    return {
-        "warning_level": warning_level,
-        "triggers": triggers,
-        "message": message,
-        "metadata": {
-            "days_analyzed": days,
-            "trigger_count": len(triggers),
-            "note": "Heuristische Einschätzung, keine medizinische Diagnose"
-        }
-    }
 
 
 @router.get("/training-volume")

frontend/src/app.css

@@ -350,6 +350,11 @@ body { font-family: var(--font); background: var(--bg); color: var(--text1); -we
   font-style: italic;
 }
 
+/* KPI: Kurz-Hinweis max. 2 Zeilen — Details weiter per ℹ */
+.kpi-tiles-card__hint {
+  max-height: 2.8em;
+}
+
 /* Verlauf Ernährung: Donut (Ø-Quote) + wöchentliche Makro-Verteilung (E3) */
 .nutrition-macro-pair {
   display: grid;

frontend/src/components/KpiTilesOverview.jsx

@@ -125,14 +125,17 @@ export default function KpiTilesOverview({
                 <div
                   className="kpi-tiles-card__hint"
                   style={{
-                    marginTop: 10,
+                    marginTop: 6,
-                    padding: '8px 10px',
+                    paddingLeft: 8,
-                    borderRadius: 8,
+                    borderLeft: `2px solid ${accent}`,
-                    fontSize: 10,
+                    fontSize: 9,
-                    lineHeight: 1.45,
+                    lineHeight: 1.35,
                     color: 'var(--text2)',
-                    background: 'var(--surface2)',
+                    display: '-webkit-box',
-                    borderLeft: `3px solid ${accent}`,
+                    WebkitLineClamp: 2,
+                    WebkitBoxOrient: 'vertical',
+                    overflow: 'hidden',
+                    wordBreak: 'break-word',
                   }}
                 >
                   {cardHint}

frontend/src/components/NutritionCharts.jsx

@@ -206,7 +206,12 @@ export function WeeklyMacroDistributionPanel({ macroWeeklyData, loading, error }
 /**
  * Nutrition Charts (E1–E5). Verlauf: `showWeeklyMacroDistribution={false}` wenn E3 separat (z. B. neben Donut) gerendert wird.
  */
-export default function NutritionCharts({ days = 28, showWeeklyMacroDistribution = true }) {
+export default function NutritionCharts({
+  days = 28,
+  showWeeklyMacroDistribution = true,
+  /** Verlauf: E5-Kachel liegt in nutrition-history-viz KPIs — doppelte Karte ausblenden */
+  hideEnergyAvailabilityCard = false,
+}) {
   const [energyData, setEnergyData] = useState(null)
   const [proteinData, setProteinData] = useState(null)
   const [macroWeeklyData, setMacroWeeklyData] = useState(null)
@@ -221,15 +226,17 @@ export default function NutritionCharts({ days = 28, showWeeklyMacroDistribution
 
   useEffect(() => {
     loadCharts()
-  }, [days, showWeeklyMacroDistribution])
+  }, [days, showWeeklyMacroDistribution, hideEnergyAvailabilityCard])
 
   const loadCharts = async () => {
     const tasks = [
       loadEnergyBalance(),
       loadProteinAdequacy(),
       loadAdherence(),
-      loadWarning(),
     ]
+    if (!hideEnergyAvailabilityCard) {
+      tasks.push(loadWarning())
+    }
     if (showWeeklyMacroDistribution) {
       tasks.splice(2, 0, loadMacroWeekly())
     }
@@ -474,7 +481,7 @@ export default function NutritionCharts({ days = 28, showWeeklyMacroDistribution
       )}
 
       {!loading.adherence && !errors.adherence && renderAdherence()}
-      {!loading.warning && !errors.warning && renderWarning()}
+      {!hideEnergyAvailabilityCard && !loading.warning && !errors.warning && renderWarning()}
     </div>
   )
 }

frontend/src/pages/History.jsx

@@ -700,6 +700,26 @@ function BodySection({ profile, insights, onRequest, loadingSlug, filterActiveSl
   )
 }
 
+/** TDEE-Linie muss in der kcal-Y-Domain liegen (sonst unsichtbar trotz Legende). */
+function kcalVsWeightKcalDomain(points, tdeeRef) {
+  const vals = (points || [])
+    .map(d => Number(d.kcal_avg))
+    .filter(v => !Number.isNaN(v))
+  if (!vals.length) return ['auto', 'auto']
+  let lo = Math.min(...vals)
+  let hi = Math.max(...vals)
+  const t = tdeeRef != null ? Number(tdeeRef) : NaN
+  if (!Number.isNaN(t)) {
+    lo = Math.min(lo, t)
+    hi = Math.max(hi, t)
+  }
+  const span = hi - lo || 400
+  const pad = Math.max(100, span * 0.1)
+  return [Math.max(0, Math.floor(lo - pad)), Math.ceil(hi + pad)]
+}
+
+const TDEE_REF_LINE_COLOR = '#475569'
+
 /** Legende unter dem Chart: Linien + ggf. TDEE-Referenz (gestrichelt). */
 function KcalVsWeightLegend({ showTdee }) {
   const line = (color) => ({
@@ -753,7 +773,7 @@ function KcalVsWeightLegend({ showTdee }) {
               height: 0,
               verticalAlign: 'middle',
               marginRight: 6,
-              borderTop: '2px dashed #EA580C',
+              borderTop: `2px dashed ${TDEE_REF_LINE_COLOR}`,
               opacity: 0.95,
             }}
           />
@@ -774,6 +794,7 @@ function KcalVsWeightChart({ vizKcalWeight, corrData: corrRows, profile, cutoffD
     }))
     const n = vizKcalWeight.common_days_count ?? kcalVsW.length
     const tdeeLabel = tdee != null && tdee > 0 ? Math.round(tdee) : null
+    const kcalDomain = kcalVsWeightKcalDomain(kcalVsW, tdeeLabel)
     return (
       <div className="card" style={{ marginBottom: 12 }}>
         <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
@@ -786,14 +807,21 @@ function KcalVsWeightChart({ vizKcalWeight, corrData: corrRows, profile, cutoffD
           <LineChart data={kcalVsW} margin={{ top: 4, right: 8, bottom: 0, left: -16 }}>
             <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
             <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(kcalVsW.length / 6) - 1)} />
-            <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+            <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={kcalDomain} />
             <YAxis yAxisId="weight" orientation="right" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
             <Tooltip
               contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
               formatter={(v, n) => [`${Math.round(v)} ${n === 'weight' ? 'kg' : 'kcal'}`, n === 'kcal_avg' ? 'Ø Kalorien' : 'Gewicht']}
             />
             {tdeeLabel != null && (
-              <ReferenceLine yAxisId="kcal" y={tdeeLabel} stroke="#EA580C" strokeDasharray="6 4" strokeWidth={1.2} />
+              <ReferenceLine
+                yAxisId="kcal"
+                y={tdeeLabel}
+                stroke={TDEE_REF_LINE_COLOR}
+                strokeDasharray="6 5"
+                strokeWidth={2}
+                isFront
+              />
             )}
             <Line yAxisId="kcal" type="monotone" dataKey="kcal_avg" stroke="#EA580C" strokeWidth={2.5} dot={false} name="kcal_avg" />
             <Line yAxisId="weight" type="monotone" dataKey="weight" stroke="#2563EB" strokeWidth={2.5} dot={{ r: 2, fill: '#2563EB' }} name="weight" />
@@ -823,6 +851,7 @@ function KcalVsWeightChart({ vizKcalWeight, corrData: corrRows, profile, cutoffD
   const bmr = sex === 'm' ? 10 * latestW + 6.25 * height - 5 * age + 5 : 10 * latestW + 6.25 * height - 5 * age - 161
   const tdee = Math.round(bmr * 1.4)
   const kcalVsW = rollingAvg(raw.map(d => ({ ...d, date: fmtDate(d.date) })), 'kcal')
+  const kcalDomainFb = kcalVsWeightKcalDomain(kcalVsW, tdee)
 
   return (
     <div className="card" style={{ marginBottom: 12 }}>
@@ -836,13 +865,20 @@ function KcalVsWeightChart({ vizKcalWeight, corrData: corrRows, profile, cutoffD
         <LineChart data={kcalVsW} margin={{ top: 4, right: 8, bottom: 0, left: -16 }}>
           <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
           <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(kcalVsW.length / 6) - 1)} />
-          <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+          <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={kcalDomainFb} />
           <YAxis yAxisId="weight" orientation="right" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
           <Tooltip
             contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
             formatter={(v, n) => [`${Math.round(v)} ${n === 'weight' ? 'kg' : 'kcal'}`, n === 'kcal_avg' ? 'Ø Kalorien' : 'Gewicht']}
           />
-          <ReferenceLine yAxisId="kcal" y={tdee} stroke="#EA580C" strokeDasharray="6 4" strokeWidth={1.2} />
+          <ReferenceLine
+            yAxisId="kcal"
+            y={tdee}
+            stroke={TDEE_REF_LINE_COLOR}
+            strokeDasharray="6 5"
+            strokeWidth={2}
+            isFront
+          />
           <Line yAxisId="kcal" type="monotone" dataKey="kcal_avg" stroke="#EA580C" strokeWidth={2.5} dot={false} name="kcal_avg" />
           <Line yAxisId="weight" type="monotone" dataKey="weight" stroke="#2563EB" strokeWidth={2.5} dot={{ r: 2, fill: '#2563EB' }} name="weight" />
         </LineChart>
@@ -1054,7 +1090,7 @@ function NutritionSection({ profile, insights, onRequest, loadingSlug, filterAct
       <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8, marginTop: 4 }}>
         Zeitverläufe (Energie & Protein)
       </div>
-      <NutritionCharts days={chartDays} showWeeklyMacroDistribution={false} />
+      <NutritionCharts days={chartDays} showWeeklyMacroDistribution={false} hideEnergyAvailabilityCard />
 
       <InsightBox insights={insights} slugs={filterActiveSlugs(['ernaehrung'])} onRequest={onRequest} loading={loadingSlug}/>
     </div>