fix: comprehensive PostgreSQL Decimal handling across all endpoints

Fixed all remaining Decimal → float conversion issues found by audit.

**Fixed Endpoints:**
1. Weight Stats: min/max/avg calculations
2. Activity Stats: kcal/duration accumulation
3. Nutrition Weekly: average calculations
4. Template Variables: all f-string Decimal formatting
5. CSV Export: all numeric value formatting
6. JSON Export: added Decimal handler
7. ZIP Export: added Decimal handler
8. Correlations: weight, nutrition, caliper values

**Changes:**
- Added `from decimal import Decimal` import
- Weight stats: convert to float for min/max/avg
- Activity: float() in sum() and accumulation
- Nutrition: float() in averages
- Template vars: float() for weight_aktuell, kf_aktuell, goals
- CSV: float() in all f-strings (weight, circ, caliper, nutrition, activity)
- JSON/ZIP: custom decimal_handler for json.dumps()
- Correlations: float() for all numeric DB values

Prevents:
- TypeError in math operations
- "Decimal('X')" strings in exports
- JSON serialization failures

All numeric values from PostgreSQL now properly converted to float.

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

backend/main.py

@@ -2,6 +2,7 @@ import os, csv, io, uuid, json, zipfile
 from pathlib import Path
 from typing import Optional
 from datetime import datetime
+from decimal import Decimal
 
 from fastapi import FastAPI, HTTPException, UploadFile, File, Header, Query, Depends
 from fastapi.middleware.cors import CORSMiddleware
@@ -268,9 +269,9 @@ def weight_stats(x_profile_id: Optional[str]=Header(default=None), session: dict
         cur.execute("SELECT date,weight FROM weight_log WHERE profile_id=%s ORDER BY date DESC LIMIT 90", (pid,))
         rows = cur.fetchall()
     if not rows: return {"count":0,"latest":None,"prev":None,"min":None,"max":None,"avg_7d":None}
-    w=[r['weight'] for r in rows]
+    w=[float(r['weight']) for r in rows]
-    return {"count":len(rows),"latest":{"date":rows[0]['date'],"weight":rows[0]['weight']},
+    return {"count":len(rows),"latest":{"date":rows[0]['date'],"weight":float(rows[0]['weight'])},
-            "prev":{"date":rows[1]['date'],"weight":rows[1]['weight']} if len(rows)>1 else None,
+            "prev":{"date":rows[1]['date'],"weight":float(rows[1]['weight'])} if len(rows)>1 else None,
             "min":min(w),"max":max(w),"avg_7d":round(sum(w[:7])/min(7,len(w)),2)}
 
 # ── Circumferences ────────────────────────────────────────────────────────────
@@ -428,13 +429,14 @@ def activity_stats(x_profile_id: Optional[str]=Header(default=None), session: di
             "SELECT * FROM activity_log WHERE profile_id=%s ORDER BY date DESC LIMIT 30", (pid,))
         rows = [r2d(r) for r in cur.fetchall()]
     if not rows: return {"count":0,"total_kcal":0,"total_min":0,"by_type":{}}
-    total_kcal=sum(r.get('kcal_active') or 0 for r in rows)
+    total_kcal=sum(float(r.get('kcal_active') or 0) for r in rows)
-    total_min=sum(r.get('duration_min') or 0 for r in rows)
+    total_min=sum(float(r.get('duration_min') or 0) for r in rows)
     by_type={}
     for r in rows:
         t=r['activity_type']; by_type.setdefault(t,{'count':0,'kcal':0,'min':0})
-        by_type[t]['count']+=1; by_type[t]['kcal']+=r.get('kcal_active') or 0
+        by_type[t]['count']+=1
-        by_type[t]['min']+=r.get('duration_min') or 0
+        by_type[t]['kcal']+=float(r.get('kcal_active') or 0)
+        by_type[t]['min']+=float(r.get('duration_min') or 0)
     return {"count":len(rows),"total_kcal":round(total_kcal),"total_min":round(total_min),"by_type":by_type}
 
 @app.post("/api/activity/import-csv")
@@ -590,11 +592,13 @@ def nutrition_correlations(x_profile_id: Optional[str]=Header(default=None), ses
     for d in all_dates:
         if d not in nutr and d not in wlog: continue
         row={'date':d}
-        if d in nutr: row.update({k:nutr[d][k] for k in ['kcal','protein_g','fat_g','carbs_g']})
+        if d in nutr: row.update({k:float(nutr[d][k]) if nutr[d][k] is not None else None for k in ['kcal','protein_g','fat_g','carbs_g']})
-        if d in wlog: row['weight']=wlog[d]
+        if d in wlog: row['weight']=float(wlog[d])
         if d in cal_by_date:
-            row['lean_mass']=cal_by_date[d].get('lean_mass')
+            lm = cal_by_date[d].get('lean_mass')
-            row['body_fat_pct']=cal_by_date[d].get('body_fat_pct')
+            bf = cal_by_date[d].get('body_fat_pct')
+            row['lean_mass']=float(lm) if lm is not None else None
+            row['body_fat_pct']=float(bf) if bf is not None else None
         result.append(row)
     return result
 
@@ -613,7 +617,7 @@ def nutrition_weekly(weeks: int=16, x_profile_id: Optional[str]=Header(default=N
     result=[]
     for wk in sorted(wm):
         en=wm[wk]; n=len(en)
-        def avg(k): return round(sum(e.get(k) or 0 for e in en)/n,1)
+        def avg(k): return round(sum(float(e.get(k) or 0) for e in en)/n,1)
         result.append({'week':wk,'days':n,'kcal':avg('kcal'),'protein_g':avg('protein_g'),'fat_g':avg('fat_g'),'carbs_g':avg('carbs_g')})
     return result
 
@@ -768,10 +772,10 @@ def _prepare_template_vars(data: dict) -> dict:
         "name": prof.get('name', 'Nutzer'),
         "geschlecht": "männlich" if prof.get('sex') == 'm' else "weiblich",
         "height": prof.get('height', 178),
-        "goal_weight": prof.get('goal_weight') or "nicht gesetzt",
+        "goal_weight": float(prof.get('goal_weight')) if prof.get('goal_weight') else "nicht gesetzt",
-        "goal_bf_pct": prof.get('goal_bf_pct') or "nicht gesetzt",
+        "goal_bf_pct": float(prof.get('goal_bf_pct')) if prof.get('goal_bf_pct') else "nicht gesetzt",
-        "weight_aktuell": weight[0]['weight'] if weight else "keine Daten",
+        "weight_aktuell": float(weight[0]['weight']) if weight else "keine Daten",
-        "kf_aktuell": caliper[0]['body_fat_pct'] if caliper and caliper[0].get('body_fat_pct') else "unbekannt",
+        "kf_aktuell": float(caliper[0]['body_fat_pct']) if caliper and caliper[0].get('body_fat_pct') else "unbekannt",
     }
 
     # Calculate age from dob
@@ -798,7 +802,8 @@ def _prepare_template_vars(data: dict) -> dict:
     # Caliper summary
     if caliper:
         c = caliper[0]
-        vars['caliper_summary'] = f"KF: {c.get('body_fat_pct','?')}%, Methode: {c.get('sf_method','?')}"
+        bf = float(c.get('body_fat_pct')) if c.get('body_fat_pct') else '?'
+        vars['caliper_summary'] = f"KF: {bf}%, Methode: {c.get('sf_method','?')}"
     else:
         vars['caliper_summary'] = "keine Daten"
 
@@ -807,7 +812,7 @@ def _prepare_template_vars(data: dict) -> dict:
         c = circ[0]
         parts = []
         for k in ['c_waist', 'c_belly', 'c_hip']:
-            if c.get(k): parts.append(f"{k.split('_')[1]}: {c[k]}cm")
+            if c.get(k): parts.append(f"{k.split('_')[1]}: {float(c[k])}cm")
         vars['circ_summary'] = ", ".join(parts) if parts else "keine Daten"
     else:
         vars['circ_summary'] = "keine Daten"
@@ -1431,28 +1436,28 @@ def export_csv(x_profile_id: Optional[str]=Header(default=None), session: dict=D
         cur = get_cursor(conn)
         cur.execute("SELECT date, weight, note FROM weight_log WHERE profile_id=%s ORDER BY date", (pid,))
         for r in cur.fetchall():
-            writer.writerow(["Gewicht", r['date'], f"{r['weight']}kg", r['note'] or ""])
+            writer.writerow(["Gewicht", r['date'], f"{float(r['weight'])}kg", r['note'] or ""])
 
         # Circumferences
         cur.execute("SELECT date, c_waist, c_belly, c_hip FROM circumference_log WHERE profile_id=%s ORDER BY date", (pid,))
         for r in cur.fetchall():
-            details = f"Taille:{r['c_waist']}cm Bauch:{r['c_belly']}cm Hüfte:{r['c_hip']}cm"
+            details = f"Taille:{float(r['c_waist'])}cm Bauch:{float(r['c_belly'])}cm Hüfte:{float(r['c_hip'])}cm"
             writer.writerow(["Umfänge", r['date'], "", details])
 
         # Caliper
         cur.execute("SELECT date, body_fat_pct, lean_mass FROM caliper_log WHERE profile_id=%s ORDER BY date", (pid,))
         for r in cur.fetchall():
-            writer.writerow(["Caliper", r['date'], f"{r['body_fat_pct']}%", f"Magermasse:{r['lean_mass']}kg"])
+            writer.writerow(["Caliper", r['date'], f"{float(r['body_fat_pct'])}%", f"Magermasse:{float(r['lean_mass'])}kg"])
 
         # Nutrition
         cur.execute("SELECT date, kcal, protein_g FROM nutrition_log WHERE profile_id=%s ORDER BY date", (pid,))
         for r in cur.fetchall():
-            writer.writerow(["Ernährung", r['date'], f"{r['kcal']}kcal", f"Protein:{r['protein_g']}g"])
+            writer.writerow(["Ernährung", r['date'], f"{float(r['kcal'])}kcal", f"Protein:{float(r['protein_g'])}g"])
 
         # Activity
         cur.execute("SELECT date, activity_type, duration_min, kcal_active FROM activity_log WHERE profile_id=%s ORDER BY date", (pid,))
         for r in cur.fetchall():
-            writer.writerow(["Training", r['date'], r['activity_type'], f"{r['duration_min']}min {r['kcal_active']}kcal"])
+            writer.writerow(["Training", r['date'], r['activity_type'], f"{float(r['duration_min'])}min {float(r['kcal_active'])}kcal"])
 
     output.seek(0)
     return StreamingResponse(
@@ -1500,7 +1505,12 @@ def export_json(x_profile_id: Optional[str]=Header(default=None), session: dict=
         cur.execute("SELECT * FROM ai_insights WHERE profile_id=%s ORDER BY created DESC", (pid,))
         data['insights'] = [r2d(r) for r in cur.fetchall()]
 
-    json_str = json.dumps(data, indent=2, default=str)
+    def decimal_handler(obj):
+        if isinstance(obj, Decimal):
+            return float(obj)
+        return str(obj)
+
+    json_str = json.dumps(data, indent=2, default=decimal_handler)
     return Response(
         content=json_str,
         media_type="application/json",
@@ -1549,7 +1559,12 @@ def export_zip(x_profile_id: Optional[str]=Header(default=None), session: dict=D
             cur.execute("SELECT * FROM ai_insights WHERE profile_id=%s ORDER BY created DESC", (pid,))
             data['insights'] = [r2d(r) for r in cur.fetchall()]
 
-        zf.writestr("data.json", json.dumps(data, indent=2, default=str))
+        def decimal_handler(obj):
+            if isinstance(obj, Decimal):
+                return float(obj)
+            return str(obj)
+
+        zf.writestr("data.json", json.dumps(data, indent=2, default=decimal_handler))
 
         # Add photos if they exist
         with get_db() as conn: