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calorie_tracker_app/utils.py
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258
calorie_tracker_app/utils.py
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from datetime import date, timedelta
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from models import db, Meal, WaterLog, WeightLog, DailySummary, UserGoal
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def calculate_bmr(weight_kg, height_cm, age, gender):
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"""
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Calculate Basal Metabolic Rate using Mifflin-St Jeor Equation
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"""
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if gender.lower() == 'male':
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bmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) + 5
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else: # female
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bmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) - 161
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return round(bmr)
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def calculate_tdee(bmr, activity_level):
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"""
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Calculate Total Daily Energy Expenditure
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"""
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multipliers = {
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'sedentary': 1.2,
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'light': 1.375,
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'moderate': 1.55,
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'active': 1.725,
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'very_active': 1.9
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}
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multiplier = multipliers.get(activity_level, 1.55)
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return round(bmr * multiplier)
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def calculate_macro_targets(weight_kg, goal_type='recomp'):
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"""
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Calculate macro targets based on body weight and goal
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"""
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if goal_type == 'muscle_gain':
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protein = weight_kg * 2.4 # High protein for muscle building
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carbs = weight_kg * 3.5 # Higher carbs for energy
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fat = weight_kg * 1.0 # Moderate fat
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elif goal_type == 'weight_loss':
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protein = weight_kg * 2.2 # High protein to preserve muscle
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carbs = weight_kg * 2.0 # Lower carbs for deficit
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fat = weight_kg * 0.8 # Lower fat
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else: # recomp (body recomposition)
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protein = weight_kg * 2.2 # High protein
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carbs = weight_kg * 2.5 # Moderate carbs
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fat = weight_kg * 0.9 # Moderate fat
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return {
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'protein_g': round(protein),
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'carbs_g': round(carbs),
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'fat_g': round(fat)
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}
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def calculate_daily_totals(user_id, target_date=None):
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"""
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Calculate total nutrition consumed for a given date
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"""
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if target_date is None:
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target_date = date.today()
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# Get all meals for the date
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meals = Meal.query.filter_by(user_id=user_id, date=target_date).all()
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totals = {
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'calories': 0,
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'protein': 0,
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'carbs': 0,
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'fat': 0,
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'meals': []
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}
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for meal in meals:
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meal_totals = meal.calculate_totals()
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totals['calories'] += meal_totals['calories']
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totals['protein'] += meal_totals['protein']
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totals['carbs'] += meal_totals['carbs']
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totals['fat'] += meal_totals['fat']
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totals['meals'].append({
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'id': meal.id,
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'type': meal.meal_type,
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'time': meal.time.strftime('%H:%M') if meal.time else None,
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'totals': meal_totals,
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'foods': [
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{
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'name': mf.food.name,
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'quantity': mf.quantity,
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'calories': mf.calories_consumed
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}
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for mf in meal.foods
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]
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})
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return totals
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def calculate_water_total(user_id, target_date=None):
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"""
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Calculate total water intake for a given date
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"""
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if target_date is None:
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target_date = date.today()
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water_logs = WaterLog.query.filter_by(user_id=user_id, date=target_date).all()
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total = sum(log.amount_ml for log in water_logs)
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return {
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'total_ml': total,
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'logs': [
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{
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'id': log.id,
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'amount_ml': log.amount_ml,
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'time': log.time.strftime('%H:%M') if log.time else None
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}
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for log in water_logs
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]
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}
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def get_weight_trend(user_id, days=7):
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"""
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Get weight trend for the past N days
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"""
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end_date = date.today()
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start_date = end_date - timedelta(days=days-1)
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weight_logs = WeightLog.query.filter(
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WeightLog.user_id == user_id,
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WeightLog.date >= start_date,
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WeightLog.date <= end_date
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).order_by(WeightLog.date).all()
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return [
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{
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'date': log.date.strftime('%Y-%m-%d'),
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'weight_kg': log.weight_kg
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}
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for log in weight_logs
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]
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def get_calorie_trend(user_id, days=7):
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"""
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Get calorie intake trend for the past N days
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"""
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end_date = date.today()
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start_date = end_date - timedelta(days=days-1)
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trend = []
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current_date = start_date
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while current_date <= end_date:
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totals = calculate_daily_totals(user_id, current_date)
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trend.append({
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'date': current_date.strftime('%Y-%m-%d'),
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'calories': round(totals['calories']),
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'protein': round(totals['protein']),
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'carbs': round(totals['carbs']),
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'fat': round(totals['fat'])
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})
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current_date += timedelta(days=1)
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return trend
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def update_daily_summary(user_id, target_date=None):
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"""
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Update or create daily summary for a user
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"""
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if target_date is None:
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target_date = date.today()
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# Calculate totals
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nutrition = calculate_daily_totals(user_id, target_date)
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water = calculate_water_total(user_id, target_date)
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# Get weight for the day
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weight_log = WeightLog.query.filter_by(user_id=user_id, date=target_date).first()
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weight = weight_log.weight_kg if weight_log else None
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# Get user's calorie target
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from models import User
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user = User.query.get(user_id)
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target_calories = user.target_daily_calories if user else 2000
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# Find or create summary
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summary = DailySummary.query.filter_by(user_id=user_id, date=target_date).first()
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if not summary:
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summary = DailySummary(user_id=user_id, date=target_date)
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db.session.add(summary)
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# Update values
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summary.total_calories = nutrition['calories']
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summary.total_protein_g = nutrition['protein']
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summary.total_carbs_g = nutrition['carbs']
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summary.total_fat_g = nutrition['fat']
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summary.total_water_ml = water['total_ml']
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summary.calories_remaining = target_calories - nutrition['calories']
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summary.weight_kg = weight
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try:
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db.session.commit()
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return summary
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except Exception as e:
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db.session.rollback()
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print(f"Error updating daily summary: {e}")
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return None
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def get_macro_percentages(protein_g, carbs_g, fat_g):
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"""
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Calculate macro distribution as percentages
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"""
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protein_cal = protein_g * 4
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carbs_cal = carbs_g * 4
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fat_cal = fat_g * 9
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total_cal = protein_cal + carbs_cal + fat_cal
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if total_cal == 0:
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return {'protein': 0, 'carbs': 0, 'fat': 0}
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return {
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'protein': round((protein_cal / total_cal) * 100),
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'carbs': round((carbs_cal / total_cal) * 100),
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'fat': round((fat_cal / total_cal) * 100)
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}
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def suggest_foods_for_macros(remaining_protein, remaining_carbs, remaining_fat):
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"""
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Suggest Filipino foods based on remaining macros
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Returns category suggestions
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"""
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suggestions = []
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# High protein needed
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if remaining_protein > 30:
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suggestions.append({
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'category': 'High Protein Ulam',
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'examples': ['Grilled Tilapia', 'Chicken Tinola', 'Grilled Chicken']
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})
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# High carbs needed
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if remaining_carbs > 40:
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suggestions.append({
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'category': 'Carbs',
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'examples': ['White Rice', 'Pandesal', 'Sweet Potato']
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})
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# High fat needed
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if remaining_fat > 20:
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suggestions.append({
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'category': 'Healthy Fats',
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'examples': ['Sisig', 'Lechon Kawali', 'Bicol Express']
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})
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# Balanced meal needed
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if remaining_protein > 20 and remaining_carbs > 30:
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suggestions.append({
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'category': 'Balanced Meals',
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'examples': ['Tapsilog', 'Chicken Adobo with Rice', 'Sinigang']
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})
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return suggestions
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