fitness-nutrition

Research

Search exercises by muscle/equipment, lookup food nutrition data. Uses free wger and USDA APIs.

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Fitness & Nutrition

Exercise database and food nutrition lookup via free public APIs.

Exercise Search (wger API — free, no key)

# Search exercises
curl -s "https://wger.de/api/v2/exercise/search/?term=squat&language=english&format=json"

# List muscle groups
curl -s "https://wger.de/api/v2/muscle/?format=json" | python3 -c "
import sys,json; [print(f\"{m['id']}: {m['name_en']}\") for m in json.load(sys.stdin)['results']]"

# Exercises for specific muscle (e.g. chest=4)
curl -s "https://wger.de/api/v2/exercise/?muscles=4&language=2&format=json"

Muscle Groups

ID	English	中文
1	Biceps	肱二头肌
2	Shoulders	肩部
4	Chest	胸部
9	Legs	腿部
10	Abs/Core	核心/腹肌
12	Back	背部
5	Triceps	肱三头肌

Nutrition Lookup (USDA FoodData Central)

# Search food (free, DEMO_KEY works)
curl -s "https://api.nal.usda.gov/fdc/v1/foods/search?query=chicken+breast&pageSize=3&api_key=DEMO_KEY" | \
  python3 -c "import sys,json; [print(f\"{f['description']}: {[n for n in f.get('foodNutrients',[]) if 'Energy' in n.get('nutrientName','')]}\") for f in json.load(sys.stdin)['foods']]"

Calculators

# BMI
bmi = weight_kg / (height_m ** 2)
# Categories: <18.5 underweight, 18.5-24.9 normal, 25-29.9 overweight, 30+ obese

# TDEE (Harris-Benedict)
bmr_male = 88.362 + (13.397 * weight_kg) + (4.799 * height_cm) - (5.677 * age)
bmr_female = 447.593 + (9.247 * weight_kg) + (3.098 * height_cm) - (4.330 * age)
# Activity multiplier: sedentary 1.2, light 1.375, moderate 1.55, active 1.725

Script

python3 scripts/health_query.py exercise "push up"
python3 scripts/health_query.py exercise-detail 123
python3 scripts/health_query.py food "鸡胸肉"
python3 scripts/health_query.py bmi 75 1.78