## Step 1: Define the symbolic representation of the problem

Let's denote the number of servings of supplement A as $x_1$ and the number of servings of supplement B as $x_2$. The objective is to minimize the total cost, which is $2x_1 + 4x_2$. The constraints are:

- Keratin requirement: $x_1 + 4x_2 \geq 12$
- Calcium requirement: $5x_1 + x_2 \geq 20$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'servings of supplement A'), ('x2', 'servings of supplement B')],
    'objective_function': '2*x1 + 4*x2',
    'constraints': [
        'x1 + 4*x2 >= 12',
        '5*x1 + x2 >= 20',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code to solve the problem

```python
import gurobi

def solve_supplement_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, vtype=gurobi.GRB.CONTINUOUS)  # servings of supplement A
    x2 = model.addVar(name="x2", lb=0, vtype=gurobi.GRB.CONTINUOUS)  # servings of supplement B

    # Define the objective function
    model.setObjective(2*x1 + 4*x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(x1 + 4*x2 >= 12, name="keratin_requirement")
    model.addConstr(5*x1 + x2 >= 20, name="calcium_requirement")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Servings of supplement A: {x1.varValue}")
        print(f"Servings of supplement B: {x2.varValue}")
        print(f"Total cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_supplement_problem()
```