docs: goal system priority analysis - hybrid approach
Key Decision: Minimal Goal System BEFORE Placeholders Critical Finding: - Same data = different interpretation per goal - Example: -5kg FM, -2kg LBM - weight_loss: 78/100 (good!) - strength: 32/100 (LBM loss critical!) - Without goal: 50/100 (generic, wrong for both) Recommended Approach (Hybrid): 1. Phase 0a (2-3h): Minimal Goal System - DB: goal_mode field - API: Get/Set Goal - UI: Goal Selector - Default: health 2. Phase 0b (16-20h): Goal-Aware Placeholders - 84 placeholders with goal-dependent calculations - Scores use goal_mode from day 1 - No rework needed later 3. Phase 2+ (6-8h): Full Goal System - Goal recognition from patterns - Secondary goals - Goal progression tracking Why Hybrid Works: ✅ Charts show correct interpretations immediately ✅ No rework of 84 placeholders later ✅ Goal recognition can come later (needs placeholders anyway) ✅ System is "smart coach" from day 1 File: docs/GOAL_SYSTEM_PRIORITY_ANALYSIS.md (650 lines)
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# Zielesystem: Prioritäts-Analyse
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**Datum:** 26. März 2026
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**Frage:** Zielesystem vor oder nach Platzhaltern/Charts?
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**Antwort:** **Minimales Zielesystem VOR Platzhaltern, volles System parallel**
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---
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## 1. Kritische Erkenntnis aus Fachkonzept
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### Zitat Fachkonzept (Zeile 20-28):
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> **Wichtig ist, dass das System zielabhängig interpretiert:**
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> - Gewichtsreduktion
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> - Muskel-/Kraftaufbau
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> - Konditions-/Ausdaueraufbau
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> - Körperrekomposition
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> - allgemeine Gesundheit
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>
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> **Dasselbe Rohsignal kann je nach Ziel anders bewertet werden.**
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> Ein Kaloriendefizit ist z. B. bei Gewichtsreduktion oft positiv,
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> bei Kraftaufbau aber potenziell hinderlich.
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### Konsequenz
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❌ **Charts OHNE Zielesystem = falsche Interpretationen**
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✅ **Charts MIT Zielesystem = korrekte, zielspezifische Aussagen**
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---
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## 2. Abhängigkeits-Matrix
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### Was hängt vom Zielesystem ab?
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| Komponente | Zielabhängig? | Beispiel |
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|------------|---------------|----------|
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| **Rohdaten-Charts** | ❌ Nein | Gewichtsverlauf, Umfänge-Trend |
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| **Score-Gewichtung** | ✅ JA | Body Progress Score: 30% bei weight_loss, 20% bei strength |
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| **Interpretationen** | ✅ JA | Kaloriendefizit: "gut" bei weight_loss, "kritisch" bei strength |
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| **Hinweise** | ✅ JA | "Gewicht stagniert" → bei weight_loss: Warnung, bei strength: egal |
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| **Platzhalter (Berechnungen)** | ⚠️ TEILWEISE | Trends: Nein, Scores: JA |
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| **KI-Prompts** | ✅ JA | Analyse-Kontext ändert sich komplett |
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### Fachkonzept: Score-Gewichtung (Zeile 185-216)
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```yaml
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score_weights:
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weight_loss:
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body_progress: 0.30 # Körper wichtig
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nutrition: 0.25
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activity: 0.20
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recovery: 0.15
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health_risk: 0.10
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strength:
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body_progress: 0.20
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nutrition: 0.25
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activity: 0.30 # Training wichtiger
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recovery: 0.20
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health_risk: 0.05 # Weniger kritisch
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endurance:
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body_progress: 0.10 # Körper unwichtiger
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activity: 0.35 # Training am wichtigsten
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recovery: 0.25 # Recovery sehr wichtig
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```
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### Beispiel: Body Progress Score
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**OHNE Zielesystem:**
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```python
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def calculate_body_progress_score():
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# Generisch, für niemanden wirklich passend
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fm_delta_score = calculate_fm_change() # -5kg
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lbm_delta_score = calculate_lbm_change() # -2kg
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return (fm_delta_score + lbm_delta_score) / 2
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# Score: 50/100 (FM gut runter, aber LBM auch runter)
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```
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**MIT Zielesystem:**
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```python
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def calculate_body_progress_score(goal_mode):
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fm_delta_score = calculate_fm_change() # -5kg
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lbm_delta_score = calculate_lbm_change() # -2kg
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if goal_mode == "weight_loss":
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# FM runter: sehr gut, LBM runter: tolerierbar wenn nicht zu viel
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return 0.70 * fm_delta_score + 0.30 * lbm_delta_score
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# Score: 78/100 (FM wichtiger, LBM-Verlust weniger kritisch)
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elif goal_mode == "strength":
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# FM runter: ok, LBM runter: SEHR SCHLECHT
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return 0.30 * fm_delta_score + 0.70 * lbm_delta_score
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# Score: 32/100 (LBM-Verlust ist Hauptproblem!)
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elif goal_mode == "recomposition":
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# FM runter: gut, LBM runter: schlecht
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return 0.50 * fm_delta_score + 0.50 * lbm_delta_score
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# Score: 50/100 (ausgewogen bewertet)
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```
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**Ergebnis:**
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- Gleiche Daten (-5kg FM, -2kg LBM)
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- ABER: 78/100 bei weight_loss, 32/100 bei strength
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- **Ohne Ziel: völlig falsche Bewertung!**
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---
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## 3. Ziel-Erkennung aus Daten
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### Fachkonzept erwähnt dies NICHT explizit, aber logisch ableitbar:
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**Pattern-Erkennung:**
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```python
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def suggest_goal_from_data(profile_id):
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"""Schlägt Ziel basierend auf Daten-Mustern vor."""
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# Analyse der letzten 28 Tage
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training_types = get_training_distribution_28d(profile_id)
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nutrition = get_nutrition_pattern_28d(profile_id)
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body_changes = get_body_changes_28d(profile_id)
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# Pattern 1: Viel Kraft + viel Protein + LBM steigt
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if (training_types['strength'] > 60% and
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nutrition['protein_g_per_kg'] > 1.8 and
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body_changes['lbm_trend'] > 0):
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return {
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'suggested_goal': 'strength',
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'confidence': 'high',
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'reasoning': 'Krafttraining dominant + hohe Proteinzufuhr + Muskelaufbau erkennbar'
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}
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# Pattern 2: Viel Cardio + Kaloriendefizit + Gewicht sinkt
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if (training_types['endurance'] > 50% and
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nutrition['kcal_balance_avg'] < -300 and
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body_changes['weight_trend'] < 0):
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return {
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'suggested_goal': 'weight_loss',
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'confidence': 'high',
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'reasoning': 'Ausdauertraining + Kaloriendefizit + Gewichtsverlust'
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}
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# Pattern 3: Mixed Training + Protein hoch + Gewicht stabil + Rekomposition
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if (training_types['mixed'] == True and
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nutrition['protein_g_per_kg'] > 1.6 and
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abs(body_changes['weight_trend']) < 0.05 and
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body_changes['fm_trend'] < 0 and
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body_changes['lbm_trend'] > 0):
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return {
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'suggested_goal': 'recomposition',
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'confidence': 'medium',
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'reasoning': 'Gemischtes Training + Rekomposition sichtbar (FM↓, LBM↑)'
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}
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# Default: Nicht genug Muster erkennbar
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return {
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'suggested_goal': 'health',
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'confidence': 'low',
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'reasoning': 'Keine klaren Muster erkennbar, gesundheitsorientiertes Training angenommen'
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}
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```
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### Voraussetzungen für Ziel-Erkennung:
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1. ✅ Mindestens 21-28 Tage Daten
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2. ✅ Training-Type Distribution
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3. ✅ Ernährungs-Pattern
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4. ✅ Körper-Trends (FM, LBM, Gewicht)
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5. ✅ Berechnet → **braucht Platzhalter!**
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**ABER:** Ziel-Erkennung ist **nachgelagert**, nicht Voraussetzung.
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---
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## 4. Empfohlene Implementierungs-Strategie
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### Hybrid-Ansatz: Minimal-Ziele SOFORT, Voll-System parallel
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## Phase 0a: Minimal-Zielesystem (2-3h) ⭐ **START HIER**
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### Ziel
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User kann manuell Ziel setzen, System nutzt es für Berechnungen.
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### Implementierung
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**1. DB-Schema erweitern:**
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```sql
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-- Migration 023
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ALTER TABLE profiles ADD COLUMN goal_mode VARCHAR(50) DEFAULT 'health';
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ALTER TABLE profiles ADD COLUMN goal_weight DECIMAL(5,2);
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ALTER TABLE profiles ADD COLUMN goal_bf_pct DECIMAL(4,1);
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ALTER TABLE profiles ADD COLUMN goal_set_date DATE;
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ALTER TABLE profiles ADD COLUMN goal_target_date DATE;
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COMMENT ON COLUMN profiles.goal_mode IS
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'Primary goal: weight_loss, strength, endurance, recomposition, health';
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```
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**2. Goal-Mode Konstanten:**
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```python
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# backend/goals.py (NEU)
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GOAL_MODES = {
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'weight_loss': {
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'label': 'Gewichtsreduktion',
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'description': 'Fettabbau bei Erhalt der Magermasse',
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'score_weights': {
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'body_progress': 0.30,
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'nutrition': 0.25,
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'activity': 0.20,
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'recovery': 0.15,
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'health_risk': 0.10
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},
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'focus_areas': ['fettmasse', 'gewichtstrend', 'kalorienbilanz', 'protein_sicherung']
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},
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'strength': {
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'label': 'Kraftaufbau',
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'description': 'Muskelaufbau und Kraftsteigerung',
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'score_weights': {
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'body_progress': 0.20,
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'nutrition': 0.25,
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'activity': 0.30,
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'recovery': 0.20,
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'health_risk': 0.05
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},
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'focus_areas': ['trainingsqualitaet', 'protein', 'lbm', 'recovery']
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},
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'endurance': {
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'label': 'Ausdaueraufbau',
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'description': 'Kondition und VO2max verbessern',
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'score_weights': {
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'body_progress': 0.10,
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'nutrition': 0.20,
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'activity': 0.35,
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'recovery': 0.25,
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'health_risk': 0.10
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},
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'focus_areas': ['trainingsvolumen', 'intensitaetsverteilung', 'vo2max', 'recovery']
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},
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'recomposition': {
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'label': 'Körperrekomposition',
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'description': 'Fettabbau bei gleichzeitigem Muskelaufbau',
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'score_weights': {
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'body_progress': 0.30,
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'nutrition': 0.25,
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'activity': 0.25,
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'recovery': 0.15,
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'health_risk': 0.05
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},
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'focus_areas': ['lbm', 'fettmasse', 'protein', 'trainingsqualitaet']
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},
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'health': {
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'label': 'Allgemeine Gesundheit',
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'description': 'Ausgeglichenes Gesundheits- und Fitnesstraining',
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'score_weights': {
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'body_progress': 0.20,
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'nutrition': 0.20,
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'activity': 0.20,
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'recovery': 0.20,
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'health_risk': 0.20
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},
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'focus_areas': ['bewegung', 'blutdruck', 'schlaf', 'gewicht', 'regelmaessigkeit']
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}
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}
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```
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**3. API-Endpoint:**
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```python
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# routers/goals.py (NEU)
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from fastapi import APIRouter, Depends
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from auth import require_auth
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from goals import GOAL_MODES
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router = APIRouter(prefix="/api/goals", tags=["goals"])
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@router.get("/modes")
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def get_goal_modes():
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"""Return all available goal modes with descriptions."""
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return GOAL_MODES
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@router.get("/current")
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def get_current_goal(session: dict = Depends(require_auth)):
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"""Get user's current goal settings."""
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profile_id = session['profile_id']
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT goal_mode, goal_weight, goal_bf_pct,
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goal_set_date, goal_target_date
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FROM profiles WHERE id=%s""",
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(profile_id,)
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)
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row = r2d(cur.fetchone())
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return {
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**row,
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'mode_config': GOAL_MODES.get(row['goal_mode'], GOAL_MODES['health'])
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}
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@router.post("/set")
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def set_goal(
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goal_mode: str,
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goal_weight: Optional[float] = None,
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goal_bf_pct: Optional[float] = None,
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target_date: Optional[str] = None,
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session: dict = Depends(require_auth)
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):
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"""Set user's goal."""
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if goal_mode not in GOAL_MODES:
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raise HTTPException(400, f"Invalid goal_mode. Must be one of: {list(GOAL_MODES.keys())}")
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profile_id = session['profile_id']
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""UPDATE profiles
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SET goal_mode=%s, goal_weight=%s, goal_bf_pct=%s,
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goal_set_date=CURRENT_DATE, goal_target_date=%s
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WHERE id=%s""",
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(goal_mode, goal_weight, goal_bf_pct, target_date, profile_id)
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)
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conn.commit()
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return {"success": True, "goal_mode": goal_mode}
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```
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**4. Frontend UI (Settings.jsx):**
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```jsx
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// Minimal Goal Selector
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function GoalSettings() {
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const [goalModes, setGoalModes] = useState({})
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const [currentGoal, setCurrentGoal] = useState(null)
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const [selectedMode, setSelectedMode] = useState('health')
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useEffect(() => {
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loadGoalModes()
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loadCurrentGoal()
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}, [])
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const loadGoalModes = async () => {
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const modes = await api.getGoalModes()
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setGoalModes(modes)
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}
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const loadCurrentGoal = async () => {
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const goal = await api.getCurrentGoal()
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setCurrentGoal(goal)
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setSelectedMode(goal.goal_mode || 'health')
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}
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const saveGoal = async () => {
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await api.setGoal({
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goal_mode: selectedMode,
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goal_weight: goalWeight,
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goal_bf_pct: goalBfPct,
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target_date: targetDate
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})
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loadCurrentGoal()
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}
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return (
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<div className="card">
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<h2>🎯 Trainingsziel</h2>
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<div className="form-row">
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<label>Hauptziel</label>
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<select value={selectedMode} onChange={e => setSelectedMode(e.target.value)}>
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{Object.entries(goalModes).map(([key, config]) => (
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<option key={key} value={key}>
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{config.label}
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</option>
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))}
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</select>
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<p style={{fontSize: 12, color: 'var(--text3)'}}>
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{goalModes[selectedMode]?.description}
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</p>
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</div>
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{(selectedMode === 'weight_loss' || selectedMode === 'recomposition') && (
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<div className="form-row">
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<label>Zielgewicht (optional)</label>
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<input type="number" step="0.1" value={goalWeight} onChange={...} />
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</div>
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)}
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<button onClick={saveGoal}>Ziel speichern</button>
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</div>
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)
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}
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```
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### Aufwand: 2-3h
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- 1h: DB + Backend
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- 1h: Frontend UI
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- 0.5h: Testing
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---
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## Phase 0b: Goal-Aware Platzhalter (16-20h)
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**Alle 84 Platzhalter implementieren, ABER:**
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- Score-Berechnungen nutzen `goal_mode` von Anfang an
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- Beispiel:
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```python
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def get_body_progress_score(profile_id: str) -> str:
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"""Body Progress Score (0-100, goal-dependent)."""
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profile = get_profile_data(profile_id)
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goal_mode = profile.get('goal_mode', 'health')
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# Hole Gewichte aus goals.GOAL_MODES
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weights = GOAL_MODES[goal_mode]['score_weights']
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# Berechne Sub-Scores
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fm_score = calculate_fm_progress(profile_id)
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lbm_score = calculate_lbm_progress(profile_id)
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weight_score = calculate_weight_progress(profile_id, goal_mode)
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# Gewichte nach Ziel
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if goal_mode == 'weight_loss':
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total = (0.50 * fm_score + 0.30 * weight_score + 0.20 * lbm_score)
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elif goal_mode == 'strength':
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total = (0.60 * lbm_score + 0.30 * fm_score + 0.10 * weight_score)
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elif goal_mode == 'recomposition':
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total = (0.45 * fm_score + 0.45 * lbm_score + 0.10 * weight_score)
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else: # health, endurance
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total = (0.40 * weight_score + 0.30 * fm_score + 0.30 * lbm_score)
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return f"{int(total)}/100"
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```
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**Resultat:**
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- Charts bekommen von Anfang an **korrekte** Scores
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- Keine Umarbeitung nötig später
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- System ist "smart" ab Tag 1
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---
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## Phase 2+: Vollständiges Zielesystem (6-8h)
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**Features:**
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1. **Ziel-Erkennung aus Daten**
|
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- Pattern-Analyse (wie oben)
|
||||
- Vorschlag mit Confidence
|
||||
- "Passt dein Ziel noch?" Check
|
||||
|
||||
2. **Sekundäre Ziele**
|
||||
- `goal_mode` = primary
|
||||
- `secondary_goals[]` = weitere Schwerpunkte
|
||||
- Gewichtung: 70% primary, 30% secondary
|
||||
|
||||
3. **Ziel-Progression Tracking**
|
||||
- Fortschritt zum Ziel (%)
|
||||
- Geschätzte Erreichung (Datum)
|
||||
- Anpassungs-Vorschläge
|
||||
|
||||
4. **Goal-Aware Charts**
|
||||
- Priorisierung nach goal_relevance
|
||||
- Dashboard zeigt ziel-spezifische Charts zuerst
|
||||
|
||||
5. **Goal-Aware KI**
|
||||
- Prompt-Kontext enthält goal_mode
|
||||
- KI interpretiert zielspezifisch
|
||||
|
||||
---
|
||||
|
||||
## 5. Entscheidungs-Matrix
|
||||
|
||||
### Option A: Zielesystem komplett ZUERST
|
||||
**Aufwand:** 10-12h
|
||||
**Pro:**
|
||||
- Alles konsistent von Anfang an
|
||||
- Keine Umarbeitung
|
||||
**Contra:**
|
||||
- Verzögert Platzhalter-Start
|
||||
- Ziel-Erkennung braucht Platzhalter (Henne-Ei)
|
||||
|
||||
### Option B: Platzhalter ZUERST, dann Ziele
|
||||
**Aufwand:** 16-20h + später Rework
|
||||
**Pro:**
|
||||
- Schneller Start
|
||||
**Contra:**
|
||||
- ALLE Scores falsch gewichtet
|
||||
- Komplette Umarbeitung nötig
|
||||
- User sehen falsche Werte
|
||||
|
||||
### Option C: HYBRID ⭐ **EMPFOHLEN**
|
||||
**Aufwand:** 2-3h (Minimal-Ziele) + 16-20h (Goal-Aware Platzhalter) + später 6-8h (Voll-System)
|
||||
**Pro:**
|
||||
- ✅ Beste aus beiden Welten
|
||||
- ✅ Korrekte Scores von Anfang an
|
||||
- ✅ Keine Umarbeitung
|
||||
- ✅ Ziel-Erkennung später als Enhancement
|
||||
**Contra:**
|
||||
- Keinen signifikanten Nachteil
|
||||
|
||||
---
|
||||
|
||||
## 6. Empfehlung
|
||||
|
||||
### JA, Zielesystem VOR Platzhaltern – aber minimal!
|
||||
|
||||
**Reihenfolge:**
|
||||
|
||||
1. **Phase 0a (2-3h):** Minimal-Zielesystem
|
||||
- DB: goal_mode field
|
||||
- API: Get/Set Goal
|
||||
- UI: Goal Selector (Settings)
|
||||
- Default: "health"
|
||||
|
||||
2. **Phase 0b (16-20h):** Goal-Aware Platzhalter
|
||||
- 84 Platzhalter implementieren
|
||||
- Scores nutzen goal_mode
|
||||
- Berechnungen goal-abhängig
|
||||
|
||||
3. **Phase 1 (12-16h):** Charts
|
||||
- Nutzen goal-aware Platzhalter
|
||||
- Zeigen korrekte Interpretationen
|
||||
|
||||
4. **Phase 2+ (6-8h):** Vollständiges Zielesystem
|
||||
- Ziel-Erkennung
|
||||
- Sekundäre Ziele
|
||||
- Goal Progression Tracking
|
||||
|
||||
---
|
||||
|
||||
## 7. Fazit
|
||||
|
||||
**Deine Intuition war 100% richtig!**
|
||||
|
||||
✅ **Ohne Zielesystem:**
|
||||
- Charts zeigen falsche Interpretationen
|
||||
- Scores sind generisch und für niemanden passend
|
||||
- System bleibt "dummer Datensammler"
|
||||
|
||||
✅ **Mit Zielesystem:**
|
||||
- Charts interpretieren zielspezifisch
|
||||
- Scores sind individuell gewichtet
|
||||
- System wird "intelligenter Coach"
|
||||
|
||||
**Nächster Schritt:** Phase 0a implementieren (2-3h), dann Phase 0b mit goal-aware Platzhaltern.
|
||||
|
||||
**Soll ich mit Phase 0a (Minimal-Zielesystem) starten?**
|
||||
Loading…
Reference in New Issue
Block a user