import pulp
#initialise the model
whiskas_model = pulp.LpProblem('The Whiskas Problem', pulp.LpMaximize)
# make a list of ingredients
ingredients = ['chicken', 'beef', 'mutton', 'rice', 'wheat', 'gel']
# create a dictionary of pulp variables with keys from ingredients
# the default lower bound is -inf
x = pulp.LpVariable.dict('x_%s', ingredients, lowBound = 0)
# cost data
cost = dict(zip(ingredients, [0.013, 0.008, 0.010, 0.002, 0.005, 0.001]))
# create the objective
whiskas_model += sum( [cost[i] * x[i] for i in ingredients])
# ingredient parameters
protein = dict(zip(ingredients, [0.100, 0.200, 0.150, 0.000, 0.040, 0.000]))
fat = dict(zip(ingredients, [0.080, 0.100, 0.110, 0.010, 0.010, 0.000]))
fibre = dict(zip(ingredients, [0.001, 0.005, 0.003, 0.100, 0.150, 0.000]))
salt = dict(zip(ingredients, [0.002, 0.005, 0.007, 0.008, 0.000, 0.002]))
#note these are constraints and not an objective as there is a equality/inequality
whiskas_model += sum([x[i] for i in ingredients]) == 100.0
whiskas_model += sum([protein[i]*x[i] for i in ingredients]) >= 8.0
whiskas_model += sum([fat[i]*x[i] for i in ingredients]) >= 6.0
whiskas_model += sum([fibre[i]*x[i] for i in ingredients]) <= 2.0
whiskas_model += sum([salt[i]*x[i] for i in ingredients]) <= 0.4
#problem is then solved with the default solver
whiskas_model.solve()
#print the result
for ingredient in ingredients:
    print('The mass of %s is %s grams per can'%(ingredient, x[ingredient].value()))
