To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's define:
- $x_1$ as the number of chocolate toppings,
- $x_2$ as the number of strawberry toppings.

The objective is to minimize the cost of making these toppings, given that it costs $2 to make one chocolate topping and $3 to make one strawberry topping. Thus, the objective function can be represented as:
\[ \text{Minimize:} \quad 2x_1 + 3x_2 \]

The constraints are:
1. The cake will have at most 5 chocolate toppings: $x_1 \leq 5$
2. At least 10 grams of sugar must be used: $1x_1 + 0.5x_2 \geq 10$
3. At least 15 grams of butter must be used: $2x_1 + 0.7x_2 \geq 15$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

Therefore, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of chocolate toppings'), ('x2', 'number of strawberry toppings')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': ['x1 <= 5', 'x1 + 0.5*x2 >= 10', '2*x1 + 0.7*x2 >= 15', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Northwest_Golden_Bakery")

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="chocolate_toppings")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="strawberry_toppings")

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1 <= 5, "max_chocolate")
m.addConstr(x1 + 0.5*x2 >= 10, "min_sugar")
m.addConstr(2*x1 + 0.7*x2 >= 15, "min_butter")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Chocolate toppings: {x1.x}")
    print(f"Strawberry toppings: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```