id      name               energy   sugar   Food_Groups
1       4-Grain Flakes      140     58.8    Breakfast
2       Beef Mince, Fried   1443    8.0     Meat
3       Pork                1000    3.0     Meat
4       cake                1200    150     Sweet
5       cheese              1100    140     Sweet
6       Juice               700     85      Drink
7       cabbage             60      13      vegetarian
8       cucumber            10      10      vegetarian
9       eggs                45      30      Breakfast

# Create the 'prob' variable to contain the problem data
prob = LpProblem("Simple Diet Problem",LpMinimize)
#create data variables and dictionary
food_items = list(df['name'])
calories = dict(zip(food_items,df['energy']))
sugars = dict(zip(food_items,df['sugar']))

food_vars =LpVariable.dicts("Food",food_items,lowBound=0,cat='Integer')

#Building the LP problem by adding the main objective function.
prob += lpSum([sugars[i]*food_vars[i] for i in food_items])

#adding calorie constraint
prob += lpSum([calories[f] * food_vars[f] for f in food_items]) >=
1800.0, "CalorieMinimum"
prob += lpSum([calories[f] * food_vars[f] for f in food_items]) <=
2200.0, "CalorieMaximum"

prob.writeLP("SimpleDietProblem.lp")

prob.solve()

print("Status:", LpStatus[prob.status])

# Creates a list of the Food_groups
food_choices = list(df['Food_Groups'])

food_chosen = LpVariable.dicts("Chosen",food_choices,0,1,cat='Integer')


# Creates a list of the Days
Days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

days_menu = LpVariable.dicts("days_menu",Days,0,1,cat='Integer')

#updating my objective function
prob += lpSum([sugars[i]*food_vars[i]*days_menu[i]*food_chosen[i] for i in
food_items])