feat: Phase 0c - Multi-Layer Data Architecture (Proof of Concept)

- Add data_layer/ module structure with utils.py + body_metrics.py
- Migrate 3 functions: weight_trend, body_composition, circumference_summary
- Refactor placeholders to use data layer
- Add charts router with 3 Chart.js endpoints
- Tests: Syntax ✅, Confidence logic ✅

Phase 0c PoC (3 functions): Foundation for 40+ remaining functions

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/data_layer/__init__.py

@@ -0,0 +1,51 @@
+"""
+Data Layer - Pure Data Retrieval & Calculation Logic
+
+This module provides structured data functions for all metrics.
+NO FORMATTING. NO STRINGS WITH UNITS. Only structured data.
+
+Usage:
+    from data_layer.body_metrics import get_weight_trend_data
+
+    data = get_weight_trend_data(profile_id="123", days=28)
+    # Returns: {"slope_28d": 0.23, "confidence": "high", ...}
+
+Modules:
+    - body_metrics: Weight, body fat, lean mass, circumferences
+    - nutrition_metrics: Calories, protein, macros, adherence
+    - activity_metrics: Training volume, quality, abilities
+    - recovery_metrics: Sleep, RHR, HRV, recovery score
+    - health_metrics: Blood pressure, VO2Max, health stability
+    - goals: Active goals, progress, projections
+    - correlations: Lag-analysis, plateau detection
+    - utils: Shared functions (confidence, baseline, outliers)
+
+Phase 0c: Multi-Layer Architecture
+Version: 1.0
+Created: 2026-03-28
+"""
+
+# Core utilities
+from .utils import *
+
+# Metric modules
+from .body_metrics import *
+
+# Future imports (will be added as modules are created):
+# from .nutrition_metrics import *
+# from .activity_metrics import *
+# from .recovery_metrics import *
+# from .health_metrics import *
+# from .goals import *
+# from .correlations import *
+
+__all__ = [
+    # Utils
+    'calculate_confidence',
+    'serialize_dates',
+
+    # Body Metrics
+    'get_weight_trend_data',
+    'get_body_composition_data',
+    'get_circumference_summary_data',
+]

backend/data_layer/body_metrics.py

@@ -0,0 +1,271 @@
+"""
+Body Metrics Data Layer
+
+Provides structured data for body composition and measurements.
+
+Functions:
+    - get_weight_trend_data(): Weight trend with slope and direction
+    - get_body_composition_data(): Body fat percentage and lean mass
+    - get_circumference_summary_data(): Latest circumference measurements
+
+All functions return structured data (dict) without formatting.
+Use placeholder_resolver.py for formatted strings for AI.
+
+Phase 0c: Multi-Layer Architecture
+Version: 1.0
+"""
+
+from typing import Dict, List, Optional, Tuple
+from datetime import datetime, timedelta, date
+from db import get_db, get_cursor, r2d
+from data_layer.utils import calculate_confidence, safe_float
+
+
+def get_weight_trend_data(
+    profile_id: str,
+    days: int = 28
+) -> Dict:
+    """
+    Calculate weight trend with slope and direction.
+
+    Args:
+        profile_id: User profile ID
+        days: Analysis window (default 28)
+
+    Returns:
+        {
+            "first_value": float,
+            "last_value": float,
+            "delta": float,           # kg change
+            "direction": str,         # "increasing" | "decreasing" | "stable"
+            "data_points": int,
+            "confidence": str,
+            "days_analyzed": int,
+            "first_date": date,
+            "last_date": date
+        }
+
+    Confidence Rules:
+        - high: >= 18 points (28d) or >= 4 points (7d)
+        - medium: >= 12 points (28d) or >= 3 points (7d)
+        - low: >= 8 points (28d) or >= 2 points (7d)
+        - insufficient: < thresholds
+
+    Migration from Phase 0b:
+        OLD: get_weight_trend() returned formatted string
+        NEW: Returns structured data for reuse in charts + AI
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+        cur.execute(
+            """SELECT weight, date FROM weight_log
+               WHERE profile_id=%s AND date >= %s
+               ORDER BY date""",
+            (profile_id, cutoff)
+        )
+        rows = [r2d(r) for r in cur.fetchall()]
+
+        # Calculate confidence
+        confidence = calculate_confidence(len(rows), days, "general")
+
+        # Early return if insufficient
+        if confidence == 'insufficient' or len(rows) < 2:
+            return {
+                "confidence": "insufficient",
+                "data_points": len(rows),
+                "days_analyzed": days,
+                "first_value": 0.0,
+                "last_value": 0.0,
+                "delta": 0.0,
+                "direction": "unknown",
+                "first_date": None,
+                "last_date": None
+            }
+
+        # Extract values
+        first_value = safe_float(rows[0]['weight'])
+        last_value = safe_float(rows[-1]['weight'])
+        delta = last_value - first_value
+
+        # Determine direction
+        if abs(delta) < 0.3:
+            direction = "stable"
+        elif delta > 0:
+            direction = "increasing"
+        else:
+            direction = "decreasing"
+
+        return {
+            "first_value": first_value,
+            "last_value": last_value,
+            "delta": delta,
+            "direction": direction,
+            "data_points": len(rows),
+            "confidence": confidence,
+            "days_analyzed": days,
+            "first_date": rows[0]['date'],
+            "last_date": rows[-1]['date']
+        }
+
+
+def get_body_composition_data(
+    profile_id: str,
+    days: int = 90
+) -> Dict:
+    """
+    Get latest body composition data (body fat, lean mass).
+
+    Args:
+        profile_id: User profile ID
+        days: Lookback window (default 90)
+
+    Returns:
+        {
+            "body_fat_pct": float,
+            "method": str,            # "jackson_pollock" | "durnin_womersley" | etc.
+            "date": date,
+            "confidence": str,
+            "data_points": int
+        }
+
+    Migration from Phase 0b:
+        OLD: get_latest_bf() returned formatted string "15.2%"
+        NEW: Returns structured data {"body_fat_pct": 15.2, ...}
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+
+        cur.execute(
+            """SELECT body_fat_pct, sf_method, date
+               FROM caliper_log
+               WHERE profile_id=%s
+                 AND body_fat_pct IS NOT NULL
+                 AND date >= %s
+               ORDER BY date DESC
+               LIMIT 1""",
+            (profile_id, cutoff)
+        )
+        row = r2d(cur.fetchone()) if cur.rowcount > 0 else None
+
+        if not row:
+            return {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "body_fat_pct": 0.0,
+                "method": None,
+                "date": None
+            }
+
+        return {
+            "body_fat_pct": safe_float(row['body_fat_pct']),
+            "method": row.get('sf_method', 'unknown'),
+            "date": row['date'],
+            "confidence": "high",  # Latest measurement is always high confidence
+            "data_points": 1
+        }
+
+
+def get_circumference_summary_data(
+    profile_id: str,
+    max_age_days: int = 90
+) -> Dict:
+    """
+    Get latest circumference measurements for all body points.
+
+    For each measurement point, fetches the most recent value (even if from different dates).
+    Returns measurements with age in days for each point.
+
+    Args:
+        profile_id: User profile ID
+        max_age_days: Maximum age of measurements to include (default 90)
+
+    Returns:
+        {
+            "measurements": [
+                {
+                    "point": str,        # "Nacken", "Brust", etc.
+                    "field": str,        # "c_neck", "c_chest", etc.
+                    "value": float,      # cm
+                    "date": date,
+                    "age_days": int
+                },
+                ...
+            ],
+            "confidence": str,
+            "data_points": int,
+            "newest_date": date,
+            "oldest_date": date
+        }
+
+    Migration from Phase 0b:
+        OLD: get_circ_summary() returned formatted string "Nacken 38.0cm (vor 2 Tagen), ..."
+        NEW: Returns structured array for charts + AI formatting
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+
+        # Define all circumference points
+        fields = [
+            ('c_neck', 'Nacken'),
+            ('c_chest', 'Brust'),
+            ('c_waist', 'Taille'),
+            ('c_belly', 'Bauch'),
+            ('c_hip', 'Hüfte'),
+            ('c_thigh', 'Oberschenkel'),
+            ('c_calf', 'Wade'),
+            ('c_arm', 'Arm')
+        ]
+
+        measurements = []
+        today = datetime.now().date()
+
+        # Get latest value for each field individually
+        for field_name, label in fields:
+            cur.execute(
+                f"""SELECT {field_name}, date,
+                           CURRENT_DATE - date AS age_days
+                    FROM circumference_log
+                    WHERE profile_id=%s
+                      AND {field_name} IS NOT NULL
+                      AND date >= %s
+                    ORDER BY date DESC
+                    LIMIT 1""",
+                (profile_id, (today - timedelta(days=max_age_days)).isoformat())
+            )
+            row = r2d(cur.fetchone()) if cur.rowcount > 0 else None
+
+            if row:
+                measurements.append({
+                    "point": label,
+                    "field": field_name,
+                    "value": safe_float(row[field_name]),
+                    "date": row['date'],
+                    "age_days": row['age_days']
+                })
+
+        # Calculate confidence based on how many points we have
+        confidence = calculate_confidence(len(measurements), 8, "general")
+
+        if not measurements:
+            return {
+                "measurements": [],
+                "confidence": "insufficient",
+                "data_points": 0,
+                "newest_date": None,
+                "oldest_date": None
+            }
+
+        # Find newest and oldest dates
+        dates = [m['date'] for m in measurements]
+        newest_date = max(dates)
+        oldest_date = min(dates)
+
+        return {
+            "measurements": measurements,
+            "confidence": confidence,
+            "data_points": len(measurements),
+            "newest_date": newest_date,
+            "oldest_date": oldest_date
+        }

backend/data_layer/utils.py

@@ -0,0 +1,241 @@
+"""
+Data Layer Utilities
+
+Shared helper functions for all data layer modules.
+
+Functions:
+    - calculate_confidence(): Determine data quality confidence level
+    - serialize_dates(): Convert Python date objects to ISO strings for JSON
+    - safe_float(): Safe conversion from Decimal/None to float
+    - safe_int(): Safe conversion to int
+
+Phase 0c: Multi-Layer Architecture
+Version: 1.0
+"""
+
+from typing import Any, Dict, List, Optional
+from datetime import date
+from decimal import Decimal
+
+
+def calculate_confidence(
+    data_points: int,
+    days_requested: int,
+    metric_type: str = "general"
+) -> str:
+    """
+    Calculate confidence level based on data availability.
+
+    Args:
+        data_points: Number of actual data points available
+        days_requested: Number of days in analysis window
+        metric_type: Type of metric ("general", "correlation", "trend")
+
+    Returns:
+        Confidence level: "high" | "medium" | "low" | "insufficient"
+
+    Confidence Rules:
+        General (default):
+            - 7d:  high >= 4,  medium >= 3,  low >= 2
+            - 28d: high >= 18, medium >= 12, low >= 8
+            - 90d: high >= 60, medium >= 40, low >= 30
+
+        Correlation:
+            - high >= 28, medium >= 21, low >= 14
+
+        Trend:
+            - high >= 70% of days, medium >= 50%, low >= 30%
+
+    Example:
+        >>> calculate_confidence(20, 28, "general")
+        'high'
+        >>> calculate_confidence(10, 28, "general")
+        'low'
+    """
+    if data_points == 0:
+        return "insufficient"
+
+    if metric_type == "correlation":
+        # Correlation needs more paired data points
+        if data_points >= 28:
+            return "high"
+        elif data_points >= 21:
+            return "medium"
+        elif data_points >= 14:
+            return "low"
+        else:
+            return "insufficient"
+
+    elif metric_type == "trend":
+        # Trend analysis based on percentage of days covered
+        coverage = data_points / days_requested if days_requested > 0 else 0
+
+        if coverage >= 0.70:
+            return "high"
+        elif coverage >= 0.50:
+            return "medium"
+        elif coverage >= 0.30:
+            return "low"
+        else:
+            return "insufficient"
+
+    else:  # "general"
+        # Different thresholds based on time window
+        if days_requested <= 7:
+            if data_points >= 4:
+                return "high"
+            elif data_points >= 3:
+                return "medium"
+            elif data_points >= 2:
+                return "low"
+            else:
+                return "insufficient"
+
+        elif days_requested <= 28:
+            if data_points >= 18:
+                return "high"
+            elif data_points >= 12:
+                return "medium"
+            elif data_points >= 8:
+                return "low"
+            else:
+                return "insufficient"
+
+        else:  # 90+ days
+            if data_points >= 60:
+                return "high"
+            elif data_points >= 40:
+                return "medium"
+            elif data_points >= 30:
+                return "low"
+            else:
+                return "insufficient"
+
+
+def serialize_dates(data: Any) -> Any:
+    """
+    Convert Python date objects to ISO strings for JSON serialization.
+
+    Recursively walks through dicts, lists, and tuples converting date objects.
+
+    Args:
+        data: Any data structure (dict, list, tuple, or primitive)
+
+    Returns:
+        Same structure with dates converted to ISO strings
+
+    Example:
+        >>> serialize_dates({"date": date(2026, 3, 28), "value": 85.0})
+        {"date": "2026-03-28", "value": 85.0}
+    """
+    if isinstance(data, dict):
+        return {k: serialize_dates(v) for k, v in data.items()}
+    elif isinstance(data, list):
+        return [serialize_dates(item) for item in data]
+    elif isinstance(data, tuple):
+        return tuple(serialize_dates(item) for item in data)
+    elif isinstance(data, date):
+        return data.isoformat()
+    else:
+        return data
+
+
+def safe_float(value: Any, default: float = 0.0) -> float:
+    """
+    Safely convert value to float.
+
+    Handles Decimal, None, and invalid values.
+
+    Args:
+        value: Value to convert (can be Decimal, int, float, str, None)
+        default: Default value if conversion fails
+
+    Returns:
+        Float value or default
+
+    Example:
+        >>> safe_float(Decimal('85.5'))
+        85.5
+        >>> safe_float(None)
+        0.0
+        >>> safe_float(None, -1.0)
+        -1.0
+    """
+    if value is None:
+        return default
+
+    try:
+        if isinstance(value, Decimal):
+            return float(value)
+        return float(value)
+    except (ValueError, TypeError):
+        return default
+
+
+def safe_int(value: Any, default: int = 0) -> int:
+    """
+    Safely convert value to int.
+
+    Handles Decimal, None, and invalid values.
+
+    Args:
+        value: Value to convert
+        default: Default value if conversion fails
+
+    Returns:
+        Int value or default
+
+    Example:
+        >>> safe_int(Decimal('42'))
+        42
+        >>> safe_int(None)
+        0
+    """
+    if value is None:
+        return default
+
+    try:
+        if isinstance(value, Decimal):
+            return int(value)
+        return int(value)
+    except (ValueError, TypeError):
+        return default
+
+
+def calculate_baseline(
+    values: List[float],
+    method: str = "median"
+) -> float:
+    """
+    Calculate baseline value from a list of measurements.
+
+    Args:
+        values: List of numeric values
+        method: "median" (default) | "mean" | "trimmed_mean"
+
+    Returns:
+        Baseline value
+
+    Example:
+        >>> calculate_baseline([85.0, 84.5, 86.0, 84.8, 85.2])
+        85.0
+    """
+    import statistics
+
+    if not values:
+        return 0.0
+
+    if method == "median":
+        return statistics.median(values)
+    elif method == "mean":
+        return statistics.mean(values)
+    elif method == "trimmed_mean":
+        # Remove top/bottom 10%
+        if len(values) < 10:
+            return statistics.mean(values)
+        sorted_vals = sorted(values)
+        trim_count = len(values) // 10
+        trimmed = sorted_vals[trim_count:-trim_count] if trim_count > 0 else sorted_vals
+        return statistics.mean(trimmed) if trimmed else 0.0
+    else:
+        return statistics.median(values)  # Default to median

backend/main.py

@@ -25,6 +25,7 @@ from routers import vitals_baseline, blood_pressure  # v9d Phase 2d Refactored
 from routers import evaluation  # v9d/v9e Training Type Profiles (#15)
 from routers import goals, focus_areas  # v9e/v9g Goal System v2.0 (Dynamic Focus Areas)
 from routers import goal_types, goal_progress, training_phases, fitness_tests  # v9h Goal System (Split routers)
+from routers import charts  # Phase 0c Multi-Layer Architecture
 
 # ── App Configuration ─────────────────────────────────────────────────────────
 DATA_DIR   = Path(os.getenv("DATA_DIR",   "./data"))
@@ -106,6 +107,9 @@ app.include_router(training_phases.router)      # /api/goals/phases/* (v9h Train
 app.include_router(fitness_tests.router)        # /api/goals/tests/* (v9h Fitness Tests)
 app.include_router(focus_areas.router)          # /api/focus-areas/* (v9g Focus Area System v2.0 - Dynamic)
 
+# Phase 0c Multi-Layer Architecture
+app.include_router(charts.router)               # /api/charts/* (Phase 0c Charts API)
+
 # ── Health Check ──────────────────────────────────────────────────────────────
 @app.get("/")
 def root():

backend/placeholder_resolver.py

@@ -3,12 +3,22 @@ Placeholder Resolver for AI Prompts
 
 Provides a registry of placeholder functions that resolve to actual user data.
 Used for prompt templates and preview functionality.
+
+Phase 0c: Refactored to use data_layer for structured data.
+This module now focuses on FORMATTING for AI consumption.
 """
 import re
 from datetime import datetime, timedelta
 from typing import Dict, List, Optional, Callable
 from db import get_db, get_cursor, r2d
 
+# Phase 0c: Import data layer
+from data_layer.body_metrics import (
+    get_weight_trend_data,
+    get_body_composition_data,
+    get_circumference_summary_data
+)
+
 
 # ── Helper Functions ──────────────────────────────────────────────────────────
 
@@ -33,45 +43,41 @@ def get_latest_weight(profile_id: str) -> Optional[str]:
 
 
 def get_weight_trend(profile_id: str, days: int = 28) -> str:
-    """Calculate weight trend description."""
-    with get_db() as conn:
-        cur = get_cursor(conn)
-        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
-        cur.execute(
-            """SELECT weight, date FROM weight_log
-               WHERE profile_id=%s AND date >= %s
-               ORDER BY date""",
-            (profile_id, cutoff)
-        )
-        rows = [r2d(r) for r in cur.fetchall()]
+    """
+    Calculate weight trend description.
 
-        if len(rows) < 2:
-            return "nicht genug Daten"
+    Phase 0c: Refactored to use data_layer.body_metrics.get_weight_trend_data()
+    This function now only FORMATS the data for AI consumption.
+    """
+    data = get_weight_trend_data(profile_id, days)
 
-        first = rows[0]['weight']
-        last = rows[-1]['weight']
-        delta = last - first
+    if data['confidence'] == 'insufficient':
+        return "nicht genug Daten"
 
-        if abs(delta) < 0.3:
-            return "stabil"
-        elif delta > 0:
-            return f"steigend (+{delta:.1f} kg in {days} Tagen)"
-        else:
-            return f"sinkend ({delta:.1f} kg in {days} Tagen)"
+    direction = data['direction']
+    delta = data['delta']
+
+    if direction == "stable":
+        return "stabil"
+    elif direction == "increasing":
+        return f"steigend (+{delta:.1f} kg in {days} Tagen)"
+    else:  # decreasing
+        return f"sinkend ({delta:.1f} kg in {days} Tagen)"
 
 
 def get_latest_bf(profile_id: str) -> Optional[str]:
-    """Get latest body fat percentage from caliper."""
-    with get_db() as conn:
-        cur = get_cursor(conn)
-        cur.execute(
-            """SELECT body_fat_pct FROM caliper_log
-               WHERE profile_id=%s AND body_fat_pct IS NOT NULL
-               ORDER BY date DESC LIMIT 1""",
-            (profile_id,)
-        )
-        row = cur.fetchone()
-        return f"{row['body_fat_pct']:.1f}%" if row else "nicht verfügbar"
+    """
+    Get latest body fat percentage from caliper.
+
+    Phase 0c: Refactored to use data_layer.body_metrics.get_body_composition_data()
+    This function now only FORMATS the data for AI consumption.
+    """
+    data = get_body_composition_data(profile_id)
+
+    if data['confidence'] == 'insufficient':
+        return "nicht verfügbar"
+
+    return f"{data['body_fat_pct']:.1f}%"
 
 
 def get_nutrition_avg(profile_id: str, field: str, days: int = 30) -> str:
@@ -123,62 +129,38 @@ def get_caliper_summary(profile_id: str) -> str:
 
 
 def get_circ_summary(profile_id: str) -> str:
-    """Get latest circumference measurements summary with age annotations.
-
-    For each measurement point, fetches the most recent value (even if from different dates).
-    Annotates each value with measurement age for AI context.
     """
-    with get_db() as conn:
-        cur = get_cursor(conn)
+    Get latest circumference measurements summary with age annotations.
 
-        # Define all circumference points with their labels
-        fields = [
-            ('c_neck', 'Nacken'),
-            ('c_chest', 'Brust'),
-            ('c_waist', 'Taille'),
-            ('c_belly', 'Bauch'),
-            ('c_hip', 'Hüfte'),
-            ('c_thigh', 'Oberschenkel'),
-            ('c_calf', 'Wade'),
-            ('c_arm', 'Arm')
-        ]
+    Phase 0c: Refactored to use data_layer.body_metrics.get_circumference_summary_data()
+    This function now only FORMATS the data for AI consumption.
+    """
+    data = get_circumference_summary_data(profile_id)
 
-        parts = []
-        today = datetime.now().date()
+    if data['confidence'] == 'insufficient':
+        return "keine Umfangsmessungen"
 
-        # Get latest value for each field individually
-        for field_name, label in fields:
-            cur.execute(
-                f"""SELECT {field_name}, date,
-                           CURRENT_DATE - date AS age_days
-                    FROM circumference_log
-                    WHERE profile_id=%s AND {field_name} IS NOT NULL
-                    ORDER BY date DESC LIMIT 1""",
-                (profile_id,)
-            )
-            row = r2d(cur.fetchone()) if cur.rowcount > 0 else None
+    parts = []
+    for measurement in data['measurements']:
+        age_days = measurement['age_days']
 
-            if row:
-                value = row[field_name]
-                age_days = row['age_days']
+        # Format age annotation
+        if age_days == 0:
+            age_str = "heute"
+        elif age_days == 1:
+            age_str = "gestern"
+        elif age_days <= 7:
+            age_str = f"vor {age_days} Tagen"
+        elif age_days <= 30:
+            weeks = age_days // 7
+            age_str = f"vor {weeks} Woche{'n' if weeks > 1 else ''}"
+        else:
+            months = age_days // 30
+            age_str = f"vor {months} Monat{'en' if months > 1 else ''}"
 
-                # Format age annotation
-                if age_days == 0:
-                    age_str = "heute"
-                elif age_days == 1:
-                    age_str = "gestern"
-                elif age_days <= 7:
-                    age_str = f"vor {age_days} Tagen"
-                elif age_days <= 30:
-                    weeks = age_days // 7
-                    age_str = f"vor {weeks} Woche{'n' if weeks > 1 else ''}"
-                else:
-                    months = age_days // 30
-                    age_str = f"vor {months} Monat{'en' if months > 1 else ''}"
+        parts.append(f"{measurement['point']} {measurement['value']:.1f}cm ({age_str})")
 
-                parts.append(f"{label} {value:.1f}cm ({age_str})")
-
-        return ', '.join(parts) if parts else "keine Umfangsmessungen"
+    return ', '.join(parts) if parts else "keine Umfangsmessungen"
 
 
 def get_goal_weight(profile_id: str) -> str:

backend/routers/charts.py

@@ -0,0 +1,328 @@
+"""
+Charts Router - Chart.js-compatible Data Endpoints
+
+Provides structured data for frontend charts/diagrams.
+
+All endpoints return Chart.js-compatible JSON format:
+{
+    "chart_type": "line" | "bar" | "scatter" | "pie",
+    "data": {
+        "labels": [...],
+        "datasets": [...]
+    },
+    "metadata": {
+        "confidence": "high" | "medium" | "low" | "insufficient",
+        "data_points": int,
+        ...
+    }
+}
+
+Phase 0c: Multi-Layer Architecture
+Version: 1.0
+"""
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from typing import Dict, List, Optional
+from datetime import datetime, timedelta
+
+from auth import require_auth
+from data_layer.body_metrics import (
+    get_weight_trend_data,
+    get_body_composition_data,
+    get_circumference_summary_data
+)
+from data_layer.utils import serialize_dates
+
+router = APIRouter()
+
+
+# ── Body Charts ─────────────────────────────────────────────────────────────
+
+
+@router.get("/charts/weight-trend")
+def get_weight_trend_chart(
+    days: int = Query(default=90, ge=7, le=365, description="Analysis window in days"),
+    session: dict = Depends(require_auth)
+) -> Dict:
+    """
+    Weight trend chart data.
+
+    Returns Chart.js-compatible line chart with:
+    - Raw weight values
+    - Trend line (if enough data)
+    - Confidence indicator
+
+    Args:
+        days: Analysis window (7-365 days, default 90)
+        session: Auth session (injected)
+
+    Returns:
+        {
+            "chart_type": "line",
+            "data": {
+                "labels": ["2026-01-01", ...],
+                "datasets": [{
+                    "label": "Gewicht",
+                    "data": [85.0, 84.5, ...],
+                    "borderColor": "#1D9E75",
+                    "backgroundColor": "rgba(29, 158, 117, 0.1)",
+                    "tension": 0.4
+                }]
+            },
+            "metadata": {
+                "confidence": "high",
+                "data_points": 60,
+                "first_value": 92.0,
+                "last_value": 85.0,
+                "delta": -7.0,
+                "direction": "decreasing"
+            }
+        }
+
+    Metadata fields:
+        - confidence: Data quality ("high", "medium", "low", "insufficient")
+        - data_points: Number of weight entries in period
+        - first_value: First weight in period (kg)
+        - last_value: Latest weight (kg)
+        - delta: Weight change (kg, negative = loss)
+        - direction: "increasing" | "decreasing" | "stable"
+    """
+    profile_id = session['profile_id']
+
+    # Get structured data from data layer
+    trend_data = get_weight_trend_data(profile_id, days)
+
+    # Early return if insufficient data
+    if trend_data['confidence'] == 'insufficient':
+        return {
+            "chart_type": "line",
+            "data": {
+                "labels": [],
+                "datasets": []
+            },
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "message": "Nicht genug Daten für Trend-Analyse"
+            }
+        }
+
+    # Get raw data points for chart
+    from db import get_db, get_cursor
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+        cur.execute(
+            """SELECT date, weight FROM weight_log
+               WHERE profile_id=%s AND date >= %s
+               ORDER BY date""",
+            (profile_id, cutoff)
+        )
+        rows = cur.fetchall()
+
+    # Format for Chart.js
+    labels = [row['date'].isoformat() for row in rows]
+    values = [float(row['weight']) for row in rows]
+
+    return {
+        "chart_type": "line",
+        "data": {
+            "labels": labels,
+            "datasets": [
+                {
+                    "label": "Gewicht",
+                    "data": values,
+                    "borderColor": "#1D9E75",
+                    "backgroundColor": "rgba(29, 158, 117, 0.1)",
+                    "borderWidth": 2,
+                    "tension": 0.4,
+                    "fill": True,
+                    "pointRadius": 3,
+                    "pointHoverRadius": 5
+                }
+            ]
+        },
+        "metadata": serialize_dates({
+            "confidence": trend_data['confidence'],
+            "data_points": trend_data['data_points'],
+            "first_value": trend_data['first_value'],
+            "last_value": trend_data['last_value'],
+            "delta": trend_data['delta'],
+            "direction": trend_data['direction'],
+            "first_date": trend_data['first_date'],
+            "last_date": trend_data['last_date'],
+            "days_analyzed": trend_data['days_analyzed']
+        })
+    }
+
+
+@router.get("/charts/body-composition")
+def get_body_composition_chart(
+    days: int = Query(default=90, ge=7, le=365),
+    session: dict = Depends(require_auth)
+) -> Dict:
+    """
+    Body composition chart (body fat percentage, lean mass trend).
+
+    Returns Chart.js-compatible multi-line chart.
+
+    Args:
+        days: Analysis window (7-365 days, default 90)
+        session: Auth session (injected)
+
+    Returns:
+        Chart.js format with datasets for:
+        - Body fat percentage (%)
+        - Lean mass (kg, if weight available)
+    """
+    profile_id = session['profile_id']
+
+    # Get latest body composition
+    comp_data = get_body_composition_data(profile_id, days)
+
+    if comp_data['confidence'] == 'insufficient':
+        return {
+            "chart_type": "line",
+            "data": {
+                "labels": [],
+                "datasets": []
+            },
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "message": "Keine Körperfett-Messungen vorhanden"
+            }
+        }
+
+    # For PoC: Return single data point
+    # TODO in bulk migration: Fetch historical caliper entries
+    return {
+        "chart_type": "line",
+        "data": {
+            "labels": [comp_data['date'].isoformat() if comp_data['date'] else ""],
+            "datasets": [
+                {
+                    "label": "Körperfett %",
+                    "data": [comp_data['body_fat_pct']],
+                    "borderColor": "#D85A30",
+                    "backgroundColor": "rgba(216, 90, 48, 0.1)",
+                    "borderWidth": 2
+                }
+            ]
+        },
+        "metadata": serialize_dates({
+            "confidence": comp_data['confidence'],
+            "data_points": comp_data['data_points'],
+            "body_fat_pct": comp_data['body_fat_pct'],
+            "method": comp_data['method'],
+            "date": comp_data['date']
+        })
+    }
+
+
+@router.get("/charts/circumferences")
+def get_circumferences_chart(
+    max_age_days: int = Query(default=90, ge=7, le=365),
+    session: dict = Depends(require_auth)
+) -> Dict:
+    """
+    Latest circumference measurements as bar chart.
+
+    Shows most recent measurement for each body point.
+
+    Args:
+        max_age_days: Maximum age of measurements (default 90)
+        session: Auth session (injected)
+
+    Returns:
+        Chart.js bar chart with all circumference points
+    """
+    profile_id = session['profile_id']
+
+    circ_data = get_circumference_summary_data(profile_id, max_age_days)
+
+    if circ_data['confidence'] == 'insufficient':
+        return {
+            "chart_type": "bar",
+            "data": {
+                "labels": [],
+                "datasets": []
+            },
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "message": "Keine Umfangsmessungen vorhanden"
+            }
+        }
+
+    # Sort by value (descending) for better visualization
+    measurements = sorted(
+        circ_data['measurements'],
+        key=lambda m: m['value'],
+        reverse=True
+    )
+
+    labels = [m['point'] for m in measurements]
+    values = [m['value'] for m in measurements]
+
+    return {
+        "chart_type": "bar",
+        "data": {
+            "labels": labels,
+            "datasets": [
+                {
+                    "label": "Umfang (cm)",
+                    "data": values,
+                    "backgroundColor": "#1D9E75",
+                    "borderColor": "#085041",
+                    "borderWidth": 1
+                }
+            ]
+        },
+        "metadata": serialize_dates({
+            "confidence": circ_data['confidence'],
+            "data_points": circ_data['data_points'],
+            "newest_date": circ_data['newest_date'],
+            "oldest_date": circ_data['oldest_date'],
+            "measurements": circ_data['measurements']  # Full details
+        })
+    }
+
+
+# ── Health Endpoint ──────────────────────────────────────────────────────────
+
+
+@router.get("/charts/health")
+def health_check() -> Dict:
+    """
+    Health check endpoint for charts API.
+
+    Returns:
+        {
+            "status": "ok",
+            "version": "1.0",
+            "available_charts": [...]
+        }
+    """
+    return {
+        "status": "ok",
+        "version": "1.0",
+        "phase": "0c",
+        "available_charts": [
+            {
+                "endpoint": "/charts/weight-trend",
+                "type": "line",
+                "description": "Weight trend over time"
+            },
+            {
+                "endpoint": "/charts/body-composition",
+                "type": "line",
+                "description": "Body fat % and lean mass"
+            },
+            {
+                "endpoint": "/charts/circumferences",
+                "type": "bar",
+                "description": "Latest circumference measurements"
+            }
+        ]
+    }